26265933
2015
08
12
2015
08
13
2015
08
14
1748-7188
10
2015
Algorithms for molecular biology : AMB
Algorithms Mol Biol
Erratum to: Inferring interaction type in gene regulatory networks using co-expression data.
25
10.1186/s13015-015-0055-3
[This corrects the article DOI: 10.1186/s13015-015-0054-4.].
Khosravi
Pegah
P
School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; The Donnelly Centre, University of Toronto, Toronto, Canada.
Gazestani
Vahid H
VH
Institute of Parasitology, McGill University, Montreal, QC Canada.
Pirhaji
Leila
L
Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
Law
Brian
B
The Donnelly Centre, University of Toronto, Toronto, Canada ; Department of Computer Science, University of Toronto, Toronto, Canada ; Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
Sadeghi
Mehdi
M
School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, Canada ; Department of Computer Science, University of Toronto, Toronto, Canada.
Goliaei
Bahram
B
Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
eng
Published Erratum
2015
08
11
England
Algorithms Mol Biol
101265088
1748-7188
Algorithms Mol Biol. 2015;10:23
26157474
PMC4532253
2015
2015
7
20
2015
7
20
2015
8
11
2015
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2015
8
13
6
0
2015
8
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epublish
10.1186/s13015-015-0055-3
55
26265933
PMC4532253
26258683
2015
08
28
1546-1726
18
9
2015
Sep
Nature neuroscience
Nat. Neurosci.
EAG2 potassium channel with evolutionarily conserved function as a brain tumor target.
1236-46
10.1038/nn.4088
Over 20% of the drugs for treating human diseases target ion channels, but no cancer drug approved by the US Food and Drug Administration (FDA) is intended to target an ion channel. We found that the EAG2 (Ether-a-go-go 2) potassium channel has an evolutionarily conserved function for promoting brain tumor growth and metastasis, delineate downstream pathways, and uncover a mechanism for different potassium channels to functionally cooperate and regulate mitotic cell volume and tumor progression. EAG2 potassium channel was enriched at the trailing edge of migrating medulloblastoma (MB) cells to regulate local cell volume dynamics, thereby facilitating cell motility. We identified the FDA-approved antipsychotic drug thioridazine as an EAG2 channel blocker that reduces xenografted MB growth and metastasis, and present a case report of repurposing thioridazine for treating a human patient. Our findings illustrate the potential of targeting ion channels in cancer treatment.
Huang
Xi
X
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
He
Ye
Y
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
Dubuc
Adrian M
AM
Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada.
Hashizume
Rintaro
R
Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Zhang
Wei
W
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
Reimand
Jüri
J
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Yang
Huanghe
H
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
Wang
Tongfei A
TA
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
Stehbens
Samantha J
SJ
Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, California, USA.
Younger
Susan
S
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
Barshow
Suzanne
S
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
Zhu
Sijun
S
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
Cooper
Michael K
MK
Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Peacock
John
J
Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada.
Ramaswamy
Vijay
V
http://orcid.org/0000-0002-6557-895X
Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada.
Garzia
Livia
L
Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada.
Wu
Xiaochong
X
Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada.
Remke
Marc
M
http://orcid.org/0000-0002-9404-9993
Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada.
Forester
Craig M
CM
Department of Pediatrics, University of California, San Francisco, San Francisco, California, USA.
Kim
Charles C
CC
Division of Experimental Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California, USA.
Weiss
William A
WA
Department of Pediatrics, University of California, San Francisco, San Francisco, California, USA.
Department of Neurological Surgery and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California USA.
Department of Neurology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA.
James
C David
CD
Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
Shuman
Marc A
MA
Department of Medicine and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA.
Bader
Gary D
GD
http://orcid.org/0000-0003-0185-8861
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Mueller
Sabine
S
http://orcid.org/0000-0002-8216-9357
Department of Neurology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA.
Taylor
Michael D
MD
Developmental and Stem Cell Program, Arthur and Sonia Labatt Brain Tumor Research Centre, Hospital for Sick Children, Toronto, Ontario, Canada.
Jan
Yuh Nung
YN
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
Jan
Lily Yeh
LY
Howard Hughes Medical Institute, Department of Physiology, University of California, San Francisco, San Francisco, California, USA.
Howard Hughes Medical Institute, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, California, USA.
eng
Journal Article
2015
08
10
United States
Nat Neurosci
9809671
1097-6256
IM
2015
6
25
2015
7
15
2015
8
10
2015
8
11
6
0
2015
8
11
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2015
8
11
6
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ppublish
nn.4088
10.1038/nn.4088
26258683
26165520
2015
07
13
2015
07
19
2045-2322
5
2015
Scientific reports
Sci Rep
GreedyPlus: An Algorithm for the Alignment of Interface Interaction Networks.
12074
10.1038/srep12074
The increasing ease and accuracy of protein-protein interaction detection has resulted in the ability to map the interactomes of multiple species. We now have an opportunity to compare species to better understand how interactomes evolve. As DNA and protein sequence alignment algorithms were required for comparative genomics, network alignment algorithms are required for comparative interactomics. A number of network alignment methods have been developed for protein-protein interaction networks, where proteins are represented as vertices linked by edges if they interact. Recently, protein interactions have been mapped at the level of amino acid positions, which can be represented as an interface-interaction network (IIN), where vertices represent binding sites, such as protein domains and short sequence motifs. However, current algorithms are not designed to align these networks and generally fail to do so in practice. We present a greedy algorithm, GreedyPlus, for IIN alignment, combining data from diverse sources, including network, protein and binding site properties, to identify putative orthologous relationships between interfaces in available worm and yeast data. GreedyPlus is fast and simple, allowing for easy customization of behaviour, yet still capable of generating biologically meaningful network alignments.
Law
Brian
B
1] Department of Computer Science, University of Toronto, Toronto, ON, Canada [2] The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
Bader
Gary D
GD
1] Department of Computer Science, University of Toronto, Toronto, ON, Canada [2] The Donnelly Centre, University of Toronto, Toronto, ON, Canada [3] Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
eng
Journal Article
2015
07
13
England
Sci Rep
101563288
2045-2322
IM
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PMC4499810
2015
3
12
2015
6
15
2015
7
14
6
0
2015
7
15
6
0
2015
7
15
6
0
epublish
srep12074
10.1038/srep12074
26165520
PMC4499810
26157474
2015
07
09
2015
07
09
2015
08
12
1748-7188
10
2015
Algorithms for molecular biology : AMB
Algorithms Mol Biol
Inferring interaction type in gene regulatory networks using co-expression data.
23
10.1186/s13015-015-0054-4
Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction.
This paper describes a novel algorithm, "Signing of Regulatory Networks" (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN.
SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type.
Khosravi
Pegah
P
School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; The Donnelly Centre, University of Toronto, Toronto, Canada.
Gazestani
Vahid H
VH
Institute of Parasitology, McGill University, Montreal, QC Canada.
Pirhaji
Leila
L
Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
Law
Brian
B
The Donnelly Centre, University of Toronto, Toronto, Canada ; Department of Computer Science, University of Toronto, Toronto, Canada.
Sadeghi
Mehdi
M
National Institute of Genetic Engineering and Biotechnology, Tehran, Iran ; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Goliaei
Bahram
B
Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, Canada.
eng
Journal Article
2015
07
08
England
Algorithms Mol Biol
101265088
1748-7188
Algorithms Mol Biol. 2015;10:25
26265933
PMC4495944
Gene expression data
Information-based approach
Interaction type
Regulatory interaction
2015
2014
4
11
2015
6
16
2015
7
8
2015
7
10
6
0
2015
7
15
6
0
2015
7
15
6
1
epublish
10.1186/s13015-015-0054-4
54
26157474
PMC4495944
26125594
2015
07
01
2015
07
18
1548-7105
12
7
2015
Jun
30
Nature methods
Nat. Methods
Pathway and network analysis of cancer genomes.
615-21
10.1038/nmeth.3440
Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.
Mutation Consequences and Pathway Analysis working group of the International Cancer Genome Consortium
eng
R01 HG007069
HG
NHGRI NIH HHS
United States
Journal Article
United States
Nat Methods
101215604
1548-7091
IM
Creixell
Pau
P
Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark.
Reimand
Jüri
J
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Haider
Syed
S
Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
Wu
Guanming
G
1] Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. [2] Department of Medical Informatics and Clinical Epidemiology, Oregon Health &Science University, Portland, Oregon, USA.
Shibata
Tatsuhiro
T
Division of Cancer Genomics, National Cancer Center, Tokyo, Japan.
Vazquez
Miguel
M
Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain.
Mustonen
Ville
V
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
Gonzalez-Perez
Abel
A
Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain.
Pearson
John
J
Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St. Lucia, Brisbane, Queensland, Australia.
Sander
Chris
C
Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Raphael
Benjamin J
BJ
Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA.
Marks
Debora S
DS
Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA.
Ouellette
B F Francis
BF
1] Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. [2] Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada.
Valencia
Alfonso
A
Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Boutros
Paul C
PC
1] Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. [2] Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. [3] Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.
Stuart
Joshua M
JM
1] Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, California, USA. [2] Center for Biomolecular Science and Engineering, University of California, Santa Cruz, Santa Cruz, California, USA.
Linding
Rune
R
1] Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark. [2] Biotech Research &Innovation Centre (BRIC), University of Copenhagen (UCPH), Copenhagen, Denmark.
Lopez-Bigas
Nuria
N
1] Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain. [2] Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.
Stein
Lincoln D
LD
1] Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada. [2] Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
2015
1
20
2015
4
27
2015
7
1
6
0
2015
7
1
6
0
2015
7
1
6
0
ppublish
nmeth.3440
10.1038/nmeth.3440
26125594
26098549
2015
06
23
2015
06
27
1932-6203
10
6
2015
PloS one
PLoS ONE
Cardioprotective Signature of Short-Term Caloric Restriction.
e0130658
10.1371/journal.pone.0130658
To understand the molecular pathways underlying the cardiac preconditioning effect of short-term caloric restriction (CR).
Lifelong CR has been suggested to reduce the incidence of cardiovascular disease through a variety of mechanisms. However, prolonged adherence to a CR life-style is difficult. Here we reveal the pathways that are modulated by short-term CR, which are associated with protection of the mouse heart from ischemia.
Male 10-12 wk old C57bl/6 mice were randomly assigned to an ad libitum (AL) diet with free access to regular chow, or CR, receiving 30% less food for 7 days (d), prior to myocardial infarction (MI) via permanent coronary ligation. At d8, the left ventricles (LV) of AL and CR mice were collected for Western blot, mRNA and microRNA (miR) analyses to identify cardioprotective gene expression signatures. In separate groups, infarct size, cardiac hemodynamics and protein abundance of caspase 3 was measured at d2 post-MI.
This short-term model of CR was associated with cardio-protection, as evidenced by decreased infarct size (18.5±2.4% vs. 26.6±1.7%, N=10/group; P=0.01). mRNA and miR profiles pre-MI (N=5/group) identified genes modulated by short-term CR to be associated with circadian clock, oxidative stress, immune function, apoptosis, metabolism, angiogenesis, cytoskeleton and extracellular matrix (ECM). Western blots pre-MI revealed CR-associated increases in phosphorylated Akt and GSK3ß, reduced levels of phosphorylated AMPK and mitochondrial related proteins PGC-1α, cytochrome C and cyclooxygenase (COX) IV, with no differences in the levels of phosphorylated eNOS or MAPK (ERK1/2; p38). CR regimen was also associated with reduced protein abundance of cleaved caspase 3 in the infarcted heart and improved cardiac function.
Noyan
Hossein
H
Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada.
El-Mounayri
Omar
O
Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada.
Isserlin
Ruth
R
Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
Arab
Sara
S
Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada.
Momen
Abdul
A
Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada.
Cheng
Henry S
HS
Division of Advanced Diagnostics, Toronto General Research Institute, Toronto, Ontario Canada.
Wu
Jun
J
Division of Advanced Diagnostics, Toronto General Research Institute, Toronto, Ontario Canada.
Afroze
Talat
T
Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada.
Li
Ren-Ke
RK
Division of Advanced Diagnostics, Toronto General Research Institute, Toronto, Ontario Canada; Heart and Stroke Richard Lewar Centre of Excellence in Cardiovascular Research, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
Fish
Jason E
JE
Division of Advanced Diagnostics, Toronto General Research Institute, Toronto, Ontario Canada; Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada; Heart and Stroke Richard Lewar Centre of Excellence in Cardiovascular Research, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
Bader
Gary D
GD
Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada; The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Husain
Mansoor
M
Division of Experimental Therapeutics, Toronto General Research Institute, Toronto, Ontario, Canada; Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada; Heart and Stroke Richard Lewar Centre of Excellence in Cardiovascular Research, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada; Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Ted Rogers Centre for Heart Research, University Health Network, Toronto, Ontario, Canada.
eng
GEO
GSE68646
Journal Article
Research Support, Non-U.S. Gov't
2015
06
22
United States
PLoS One
101285081
1932-6203
IM
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PMC4476723
2015
2014
6
25
2015
5
25
2015
6
22
2015
6
23
6
0
2015
6
23
6
0
2015
6
23
6
0
epublish
10.1371/journal.pone.0130658
PONE-D-14-23448
26098549
PMC4476723
26058080
2015
06
10
2015
06
14
1878-3686
27
6
2015
Jun
8
Cancer cell
Cancer Cell
Inhibition of the Mitochondrial Protease ClpP as a Therapeutic Strategy for Human Acute Myeloid Leukemia.
864-76
10.1016/j.ccell.2015.05.004
S1535-6108(15)00180-4
From an shRNA screen, we identified ClpP as a member of the mitochondrial proteome whose knockdown reduced the viability of K562 leukemic cells. Expression of this mitochondrial protease that has structural similarity to the cytoplasmic proteosome is increased in leukemic cells from approximately half of all patients with AML. Genetic or chemical inhibition of ClpP killed cells from both human AML cell lines and primary samples in which the cells showed elevated ClpP expression but did not affect their normal counterparts. Importantly, Clpp knockout mice were viable with normal hematopoiesis. Mechanistically, we found that ClpP interacts with mitochondrial respiratory chain proteins and metabolic enzymes, and knockdown of ClpP in leukemic cells inhibited oxidative phosphorylation and mitochondrial metabolism.
Copyright © 2015 Elsevier Inc. All rights reserved.
Cole
Alicia
A
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Wang
Zezhou
Z
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Coyaud
Etienne
E
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Voisin
Veronique
V
Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
Gronda
Marcela
M
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Jitkova
Yulia
Y
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Mattson
Rachel
R
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Hurren
Rose
R
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Babovic
Sonja
S
Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada.
Maclean
Neil
N
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Restall
Ian
I
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Wang
Xiaoming
X
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Jeyaraju
Danny V
DV
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Sukhai
Mahadeo A
MA
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Prabha
Swayam
S
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Bashir
Shaheena
S
Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
Ramakrishnan
Ashwin
A
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Leung
Elisa
E
Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada.
Qia
Yi Hua
YH
Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Zhang
Nianxian
N
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Combes
Kevin R
KR
Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Ketela
Troy
T
Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, Toronto, ON M5S 3E1, Canada.
Lin
Fengshu
F
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Houry
Walid A
WA
Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada.
Aman
Ahmed
A
Drug Discovery Program, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.
Al-Awar
Rima
R
Drug Discovery Program, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON M5S 1A8, Canada.
Zheng
Wei
W
National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD 20892, USA.
Wienholds
Erno
E
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada.
Xu
Chang Jiang
CJ
Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
Dick
John
J
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada.
Wang
Jean C Y
JC
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada.
Moffat
Jason
J
Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, Toronto, ON M5S 3E1, Canada.
Minden
Mark D
MD
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada.
Eaves
Connie J
CJ
Terry Fox Laboratory, British Columbia Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada.
Bader
Gary D
GD
Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
Hao
Zhenyue
Z
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Kornblau
Steven M
SM
Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Raught
Brian
B
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada.
Schimmer
Aaron D
AD
Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada; Department of Medicine, University of Toronto, Toronto, ON M5G 2C4, Canada. Electronic address: aaron.schimmer@utoronto.ca.
eng
NCI 1R01CA157456
CA
NCI NIH HHS
United States
R01 CA157456
CA
NCI NIH HHS
United States
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
United States
Cancer Cell
101130617
1535-6108
IM
N Engl J Med. 2005 Jun 16;352(24):2487-98
15958804
Oncogene. 2014 May 22;33(21):2690-9
23770858
Mol Cancer Ther. 2006 Oct;5(10):2512-21
17041095
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Cancer Cell. 2015 Jun 8;27(6):747-9
26058072
NIHMS690556 [Available on 06/08/16]
PMC4461837 [Available on 06/08/16]
2014
11
4
2015
2
6
2015
5
7
2015
6
10
6
0
2015
6
10
6
0
2015
6
10
6
0
2016
6
8
0
0
ppublish
S1535-6108(15)00180-4
10.1016/j.ccell.2015.05.004
26058080
PMC4461837
NIHMS690556
25938373
2015
05
29
2015
08
11
1548-7105
12
6
2015
Jun
Nature methods
Nat. Methods
MIMP: predicting the impact of mutations on kinase-substrate phosphorylation.
531-3
10.1038/nmeth.3396
Protein phosphorylation is important in cellular pathways and altered in disease. We developed MIMP (http://mimp.baderlab.org/), a machine learning method to predict the impact of missense single-nucleotide variants (SNVs) on kinase-substrate interactions. MIMP analyzes kinase sequence specificities and predicts whether SNVs disrupt existing phosphorylation sites or create new sites. This helps discover mutations that modify protein function by altering kinase networks and provides insight into disease biology and therapy development.
Wagih
Omar
O
0000000221620431
The Donnelly Centre, University of Toronto, Toronto, Canada.
Reimand
Jüri
J
The Donnelly Centre, University of Toronto, Toronto, Canada.
Bader
Gary D
GD
0000000301858861
The Donnelly Centre, University of Toronto, Toronto, Canada.
eng
MOP-84324
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2015
05
04
United States
Nat Methods
101215604
1548-7091
EC 2.7.-
Phosphotransferases
IM
Amino Acid Sequence
Artificial Intelligence
Mutation, Missense
Phosphorylation
Phosphotransferases
genetics
metabolism
Polymorphism, Single Nucleotide
genetics
Signal Transduction
Software
Substrate Specificity
2014
12
09
2015
3
30
2015
5
04
2015
5
5
6
0
2015
5
6
6
0
2015
8
12
6
0
ppublish
nmeth.3396
10.1038/nmeth.3396
25938373
25915851
2015
08
26
1470-8728
469
1
2015
Jul
1
The Biochemical journal
Biochem. J.
Metabolomic profiling in liver of adiponectin-knockout mice uncovers lysophospholipid metabolism as an important target of adiponectin action.
71-82
10.1042/BJ20141455
Adiponectin mediates anti-diabetic effects via increasing hepatic insulin sensitivity and direct metabolic effects. In the present study, we conducted a comprehensive and unbiased metabolomic profiling of liver tissue from AdKO (adiponectin-knockout) mice, with and without adiponectin supplementation, fed on an HFD (high-fat diet) to derive insight into the mechanisms and consequences of insulin resistance. Hepatic lipid accumulation and insulin resistance induced by the HFD were reduced by adiponectin. The HFD significantly altered levels of 147 metabolites, and bioinformatic analysis indicated that one of the most striking changes was the profile of increased lysophospholipids. These changes were largely corrected by adiponectin, at least in part via direct regulation of PLA2 (phospholipase A2) as palmitate-induced PLA2 activation was attenuated by adiponectin in primary hepatocytes. Notable decreases in several glycerolipids after the HFD were reversed by adiponectin, which also corrected elevations in several diacyglycerol and ceramide species. Our data also indicate that stimulation of ω-oxidation of fatty acids by the HFD is enhanced by adiponectin. In conclusion, this metabolomic profiling approach in AdKO mice identified important targets of adiponectin action, including PLA2, to regulate lysophospholipid metabolism and ω-oxidation of fatty acids.
© 2015 Authors; published by Portland Press Limited.
Liu
Ying
Y
Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3.
Sen
Sanjana
S
Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3.
Wannaiampikul
Sivaporn
S
Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3 Department of Tropical Nutrition and Food Science, Mahidol University, Bangkok 10400, Thailand.
Palanivel
Rengasamy
R
Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3.
Hoo
Ruby L C
RL
State Key Laboratory of Pharmaceutical Biotechnology, and Department of Medicine, University of Hong Kong, Pokfulam, Hong Kong.
Isserlin
Ruth
R
The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario, Canada, M5S 3E1.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario, Canada, M5S 3E1.
Tungtrongchitr
Rungsunn
R
Department of Tropical Nutrition and Food Science, Mahidol University, Bangkok 10400, Thailand.
Deshaies
Yves
Y
Department of Medicine and Québec Heart & Lung Institute Research Centre, Université Laval, Québec City, Québec, Canada, G1V 4G5.
Xu
Aimin
A
State Key Laboratory of Pharmaceutical Biotechnology, and Department of Medicine, University of Hong Kong, Pokfulam, Hong Kong.
Sweeney
Gary
G
Department of Biology, York University, Toronto, Ontario, Canada, M3J 1P3 gsweeney@yorku.ca.
eng
Journal Article
2015
04
27
England
Biochem J
2984726R
0264-6021
IM
adiponectin
high-fat diet
insulin
lipid
liver
phospholipase
2014
12
1
2015
4
27
2015
4
27
2015
4
28
6
0
2015
4
29
6
0
2015
4
29
6
0
ppublish
25915851
BJ20141455
10.1042/BJ20141455
25904639
2015
06
24
2015
07
29
1468-6244
52
7
2015
Jul
Journal of medical genetics
J. Med. Genet.
Canadian Open Genetics Repository (COGR): a unified clinical genomics database as a community resource for standardising and sharing genetic interpretations.
438-45
10.1136/jmedgenet-2014-102933
The Canadian Open Genetics Repository is a collaborative effort for the collection, storage, sharing and robust analysis of variants reported by medical diagnostics laboratories across Canada. As clinical laboratories adopt modern genomics technologies, the need for this type of collaborative framework is increasingly important.
A survey to assess existing protocols for variant classification and reporting was delivered to clinical genetics laboratories across Canada. Based on feedback from this survey, a variant assessment tool was made available to all laboratories. Each participating laboratory was provided with an instance of GeneInsight, a software featuring versioning and approval processes for variant assessments and interpretations and allowing for variant data to be shared between instances. Guidelines were established for sharing data among clinical laboratories and in the final outreach phase, data will be made readily available to patient advocacy groups for general use.
The survey demonstrated the need for improved standardisation and data sharing across the country. A variant assessment template was made available to the community to aid with standardisation. Instances of the GeneInsight tool were provided to clinical diagnostic laboratories across Canada for the purpose of uploading, transferring, accessing and sharing variant data.
As an ongoing endeavour and a permanent resource, the Canadian Open Genetics Repository aims to serve as a focal point for the collaboration of Canadian laboratories with other countries in the development of tools that take full advantage of laboratory data in diagnosing, managing and treating genetic diseases.
Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
Lerner-Ellis
Jordan
J
Laboratory Medicine and Pathobiology, University of Toronto & Mount Sinai Hospital, Toronto, Ontario, Canada Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
Wang
Marina
M
http://orcid.org/0000-0002-6051-4308
Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada.
White
Shana
S
Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts, USA Massachusetts General Hospital, Boston, Massachusetts, USA.
Lebo
Matthew S
MS
Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, Massachusetts, USA Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Canadian Open Genetics Repository Group
eng
Journal Article
Research Support, Non-U.S. Gov't
2015
04
22
England
J Med Genet
2985087R
0022-2593
IM
Nature. 2003 Apr 24;422(6934):835-47
12695777
Pharmacogenomics. 2006 Oct;7(7):969-72
17054407
Nucleic Acids Res. 2012 Jan;40(Database issue):D940-6
22080554
Nature. 2010 Sep 2;467(7311):52-8
20811451
Nature. 2010 Oct 28;467(7319):1061-73
20981092
Nucleic Acids Res. 2014 Jan;42(Database issue):D980-5
24234437
Genome Biol. 2010;11(11):309
21122162
Hum Mutat. 2011 May;32(5):532-6
21432942
Genet Med. 2008 Apr;10(4):294-300
18414213
J Biomed Inform. 2012 Oct;45(5):950-7
22521718
Clin Genet. 2013 Nov;84(5):453-63
24033266
Nature. 2011 Feb 10;470(7333):204-13
21307933
PMC4501169
Clinical genetics
Diagnostics tests
Evidence Based Practice
Genetic screening/counselling
Genetics
Agatep
Ron
R
Ainsworth
Peter
P
Akbari
Mohammad R
MR
Aronson
Melyssa
M
Bader
Gary D
GD
Basran
Raveen
R
Blavier
Andre
A
Blumenthal
Andrea
A
Buckley
Kathleen
K
Campbell
Jodi
J
Campeau
Philippe M
PM
Care
Melanie
M
Carson
Nancy
N
Carter
Ronald
R
Charames
George
G
Chitayat
David
D
Chong
George
G
Chouinard
Edmond
E
Chun
Kathy
K
Craddock
Kenneth J
KJ
Docking
Rod
R
Eisen
Andrea
A
Faghfoury
Hanna
H
Farrell
Sandra
S
Feilotter
Harriet
H
Fernandez
Bridget
B
Forster-Gibson
Cynthia
C
Foulkes
William
W
Hegele
Robert
R
Holter
Spring
S
Horsburgh
Sheri
S
Hughes
Lauren
L
Hume
Stacey
S
Jewett
Franny
F
Karsan
Aly
A
Khalouei
Sam
S
Knoll
Joan
J
Kolomeitz
Elena
E
Maire
Georges
G
Marshall
Christian
C
McCready
Elizabeth
E
Moorhouse
Michael J
MJ
Morel
Chantal
C
Nelson
Tanya
T
O'Connor
Brian
B
Ouellette
Francis
F
Parboosingh
Jillian
J
Ray
Peter
P
Rehm
Heidi
H
Riddell
Christie
C
Rosenblatt
David S
DS
Ruchon
Andrea
A
Sadikovic
Bekim
B
Semotiuk
Kara
K
Scherer
Stephen W
SW
Shuman
Cheryl
C
Silver
Josh
J
Siminovitch
Katherine
K
Solomon-Izsak
Lesley
L
Speevak
Marsha
M
Stavropoulos
James
J
Stein
Lincoln
L
Tannenbaum
Rhonda
R
Terespolsky
Deborah
D
Wintle
Richard F
RF
Wong
Beatrix
B
Wong
Nora
N
Waye
John S
JS
Woods
Michael O
MO
Wyatt
Philip
P
Young
Sean
S
2014
12
4
2015
3
15
2015
4
22
2015
4
24
6
0
2015
4
24
6
0
2015
4
24
6
0
ppublish
25904639
jmedgenet-2014-102933
10.1136/jmedgenet-2014-102933
PMC4501169
25901276
2015
04
22
2015
04
22
2015
04
24
2046-1402
4
2015
F1000Research
F1000Res
Long read nanopore sequencing for detection of HLA and CYP2D6 variants and haplotypes.
17
10.12688/f1000research.6037.1
Haplotypes are often critical for the interpretation of genetic laboratory observations into medically actionable findings. Current massively parallel DNA sequencing technologies produce short sequence reads that are often unable to resolve haplotype information. Phasing short read data typically requires supplemental statistical phasing based on known haplotype structure in the population or parental genotypic data. Here we demonstrate that the MinION nanopore sequencer is capable of producing very long reads to resolve both variants and haplotypes of HLA-A, HLA-B and CYP2D6 genes important in determining patient drug response in sample NA12878 of CEPH/UTAH pedigree 1463, without the need for statistical phasing. Long read data from a single 24-hour nanopore sequencing run was used to reconstruct haplotypes, which were confirmed by HapMap data and statistically phased Complete Genomics and Sequenom genotypes. Our results demonstrate that nanopore sequencing is an emerging standalone technology with potential utility in a clinical environment to aid in medical decision-making.
Ammar
Ron
R
The Donnelly Centre, University of Toronto, Toronto, ON, M5S3E1, Canada.
Paton
Tara A
TA
The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, M5G0A4, Canada.
Torti
Dax
D
The Donnelly Centre, University of Toronto, Toronto, ON, M5S3E1, Canada.
Shlien
Adam
A
Department of Laboratory Medicine and Pathobiology, University of Toronto; Program in Genetics and Genome Biology & Department of Paediatric Laboratory Medicine The Hospital for Sick Children, Toronto, ON, M5G1X8, Canada.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, ON, M5S3E1, Canada ; Department of Computer Science, University of Toronto, Toronto, ON, M5S3G4, Canada ; Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S1A8, Canada.
eng
Journal Article
2015
01
21
England
F1000Res
101594320
2046-1402
PMC4392832
DNA
nanopore
sequencing, haplotype, pharmacogenomics
2015
2015
1
20
2015
1
21
2015
4
23
6
0
2015
4
23
6
0
2015
4
23
6
1
epublish
10.12688/f1000research.6037.1
25901276
PMC4392832
25882982
2015
05
06
2015
07
14
2015
05
06
1474-5488
16
5
2015
May
The Lancet. Oncology
Lancet Oncol.
Molecular subgroups of atypical teratoid rhabdoid tumours in children: an integrated genomic and clinicopathological analysis.
569-82
10.1016/S1470-2045(15)70114-2
S1470-2045(15)70114-2
Rhabdoid brain tumours, also called atypical teratoid rhabdoid tumours, are lethal childhood cancers with characteristic genetic alterations of SMARCB1/hSNF5. Lack of biological understanding of the substantial clinical heterogeneity of these tumours restricts therapeutic advances. We integrated genomic and clinicopathological analyses of a cohort of patients with atypical teratoid rhabdoid tumours to find out the molecular basis for clinical heterogeneity in these tumours.
We obtained 259 rhabdoid tumours from 37 international institutions and assessed transcriptional profiles in 43 primary tumours and copy number profiles in 38 primary tumours to discover molecular subgroups of atypical teratoid rhabdoid tumours. We used gene and pathway enrichment analyses to discover group-specific molecular markers and did immunohistochemical analyses on 125 primary tumours to evaluate clinicopathological significance of molecular subgroup and ASCL1-NOTCH signalling.
Transcriptional analyses identified two atypical teratoid rhabdoid tumour subgroups with differential enrichment of genetic pathways, and distinct clinicopathological and survival features. Expression of ASCL1, a regulator of NOTCH signalling, correlated with supratentorial location (p=0·004) and superior 5-year overall survival (35%, 95% CI 13-57, and 20%, 6-34, for ASCL1-positive and ASCL1-negative tumours, respectively; p=0·033) in 70 patients who received multimodal treatment. ASCL1 expression also correlated with superior 5-year overall survival (34%, 7-61, and 9%, 0-21, for ASCL1-positive and ASCL1-negative tumours, respectively; p=0·001) in 39 patients who received only chemotherapy without radiation. Cox hazard ratios for overall survival in patients with differential ASCL1 enrichment treated with chemotherapy with or without radiation were 2·02 (95% CI 1·04-3·85; p=0·038) and 3·98 (1·71-9·26; p=0·001). Integrated analyses of molecular subgroupings with clinical prognostic factors showed three distinct clinical risk groups of tumours with different therapeutic outcomes.
An integration of clinical risk factors and tumour molecular groups can be used to identify patients who are likely to have improved long-term radiation-free survival and might help therapeutic stratification of patients with atypical teratoid rhabdoid tumours.
C17 Research Network, Genome Canada, b.r.a.i.n.child, Mitchell Duckman, Tal Doron and Suri Boon foundations.
Copyright © 2015 Elsevier Ltd. All rights reserved.
Torchia
Jonathon
J
Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Picard
Daniel
D
Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Lafay-Cousin
Lucie
L
Alberta Children's Hospital, and Departments of Oncology and Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Hawkins
Cynthia E
CE
Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pathology, Hospital for Sick Children, Toronto, ON, Canada.
Kim
Seung-Ki
SK
Department of Neurosurgery, Seoul National University Children's Hospital, Seoul, South Korea.
Letourneau
Louis
L
Genome Quebec Innovation Centre, McGill University, Montreal, QC, Canada.
Ra
Young-Shin
YS
Department of Neurosurgery, Asan Medical Center, Songpa-gu, Seoul, South Korea.
Ho
King Ching
KC
Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.
Chan
Tiffany Sin Yu
TS
Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Sin-Chan
Patrick
P
Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Dunham
Christopher P
CP
Division of Anatomic Pathology, Children's and Women's Health Centre of British Columbia, Vancouver, BC, Canada.
Yip
Stephen
S
Department of Neuropathology, Vancouver General Hospital, Vancouver, BC, Canada.
Ng
Ho-Keung
HK
Department of Anatomical and Cellular Pathology, Chinese University of Hong Kong, Hong Kong, China.
Lu
Jian-Qiang
JQ
Department of Laboratory Medicine and Pathology, University of Alberta Hospital, Edmonton, AB, Canada.
Albrecht
Steffen
S
Department of Pathology, Montreal Children's Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada.
Pimentel
José
J
Department of Neurology, Hospital de Santa Maria, Centro Hospitalar Lisboa Norte, Lisbon, Portugal.
Chan
Jennifer A
JA
Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada.
Somers
Gino R
GR
Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, ON, Canada.
Zielenska
Maria
M
Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, ON, Canada.
Faria
Claudia C
CC
Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.
Roque
Lucia
L
Cytogenetic Laboratory, Centro de Investigação em Patobiologia Molecular, Portuguese Cancer Institute, Lisbon, Portugal.
Baskin
Berivan
B
Department of Immunology, Genetics and Pathology, Uppsala University Hospital, Uppsala, Sweden.
Birks
Diane
D
Department of Pediatrics, University of Colorado Denver, Aurora, CO, USA.
Foreman
Nick
N
Department of Pediatrics, University of Colorado Denver, Aurora, CO, USA.
Strother
Douglas
D
Alberta Children's Hospital, and Departments of Oncology and Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Klekner
Almos
A
Department of Neurosurgery, University of Debrecen, Debrecen, Hungary.
Garami
Miklos
M
Second Department of Pediatrics, Semmelweis University, Budapest, Hungary.
Hauser
Peter
P
Second Department of Pediatrics, Semmelweis University, Budapest, Hungary.
Hortobágyi
Tibor
T
Department of Histopathology, Faculty of Medicine, University of Szeged, Hungary.
Bognár
Laszló
L
Department of Neurosurgery, University of Debrecen, Debrecen, Hungary.
Wilson
Beverly
B
Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Alberta, Edmonton, AB, Canada.
Hukin
Juliette
J
Division of Neurology and Oncology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Carret
Anne-Sophie
AS
Division of Hematology-Oncology, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montreal, QC, Canada.
Van Meter
Timothy E
TE
Pediatric Hematology-Oncology, Department of Pediatrics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA.
Nakamura
Hideo
H
Department of Neurosurgery, Kumamoto University, Kumamoto, Japan.
Toledano
Helen
H
Oncology Department, Schneider Hospital, Petach Tikva, Israel.
Fried
Iris
I
Pediatric Hematology Oncology Department, Hadassah Hebrew University Hospital, Jerusalem, Israel.
Fults
Daniel
D
Department of Neurosurgery, University of Utah, School of Medicine, Salt Lake City, UT, USA.
Wataya
Takafumi
T
Department of Neurosurgery, Shizuoka Children's Hospital, Aoi-ku, Shizuoka, Japan.
Fryer
Chris
C
Division of Hematology and Oncology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.
Eisenstat
David D
DD
Departments of Pediatrics and Medical Genetics, University of Alberta, Edmonton, AB, Canada.
Scheineman
Katrin
K
Department of Pediatrics, McMaster University, Hamilton, ON, Canada.
Johnston
Donna
D
Department of Pediatrics, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Michaud
Jean
J
Department of Pathology and Laboratory Medicine, Ottawa Hospital and Children's Hospital of Eastern Ontario, Ottawa, ON, Canada.
Zelcer
Shayna
S
Division of Children's Health and Therapeutics, Children's Health Research Institute, London, ON, Canada.
Hammond
Robert
R
Department of Pathology, University of Western Ontario, London, ON, Canada.
Ramsay
David A
DA
Department of Pathology, London Health Sciences Centre, London, ON, Canada.
Fleming
Adam J
AJ
Division of Pediatric Hematology/Oncology, McMaster University, Hamilton, ON, Canada.
Lulla
Rishi R
RR
Division of Pediatrics-Hematology, Oncology and Stem Cell Transplantation, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA.
Fangusaro
Jason R
JR
Division of Pediatrics-Hematology, Oncology and Stem Cell Transplantation, Ann and Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA.
Sirachainan
Nongnuch
N
Division of Hematology and Oncology, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Larbcharoensub
Noppadol
N
Department of Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Hongeng
Suradej
S
Division of Hematology and Oncology, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Barakzai
Muhammad Abrar
MA
Department of Pathology and Microbiology, Aga Khan University Hospital, Karachi, Pakistan.
Montpetit
Alexandre
A
Genome Quebec Innovation Centre, McGill University, Montreal, QC, Canada.
Stephens
Derek
D
Department of Clinical Research Services, Hospital for Sick Children, Toronto, ON, Canada.
Grundy
Richard G
RG
Children's Brain Tumour Research Centre, School of Clinical Sciences, Queen's Medical Centre, University of Nottingham, Nottingham, UK.
Schüller
Ulrich
U
Center for Neuropathology, Ludwig-Maximilians-University, Munich, Germany.
Nicolaides
Theodore
T
Department of Pediatrics Hematology/Oncology, University of California, San Francisco, CA, USA.
Tihan
Tarik
T
Department of Pathology and Laboratory Medicine, University of California, San Francisco, CA, USA.
Phillips
Joanna
J
Department of Neurological Surgery, University of California, San Francisco, CA, USA.
Taylor
Michael D
MD
Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Hospital for Sick Children, Toronto, ON, Canada.
Rutka
James T
JT
Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Hospital for Sick Children, Toronto, ON, Canada.
Dirks
Peter
P
Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Hospital for Sick Children, Toronto, ON, Canada.
Bader
Gary D
GD
Department of Computer Science, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, ON, Canada.
Warmuth-Metz
Monika
M
Department of Neuroradiology, University of Wuerzburg, Wuerzburg, Germany.
Rutkowski
Stefan
S
Department of Paediatric Haematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Pietsch
Torsten
T
Department of Neuropathology, University of Bonn Medical Center, Bonn, Germany.
Judkins
Alexander R
AR
Department of Pathology and Laboratory Medicine at Children's Hospital Los Angeles, Los Angeles, CA, USA.
Jabado
Nada
N
Department of Pediatrics, McGill University, Montreal, QC, Canada; Department of Human Genetics, McGill University, Montreal, QC, Canada.
Bouffet
Eric
E
Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada.
Huang
Annie
A
Division of Hematology-Oncology, University of Toronto, Toronto, ON, Canada; Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada. Electronic address: annie.huang@sickkids.ca.
eng
P50 CA097257
CA
NCI NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
2015
04
14
England
Lancet Oncol
100957246
1470-2045
0
ASCL1 protein, human
0
Basic Helix-Loop-Helix Transcription Factors
0
Receptors, Notch
Teratoid Tumor, Atypical
IM
Lancet Oncol. 2015 May;16(5):486-8
25882984
Basic Helix-Loop-Helix Transcription Factors
biosynthesis
genetics
Child
Child, Preschool
Female
Gene Expression Regulation, Neoplastic
Genomics
Humans
Immunohistochemistry
Infant
Male
Prognosis
Receptors, Notch
biosynthesis
genetics
Rhabdoid Tumor
genetics
pathology
Risk Factors
Signal Transduction
genetics
Teratoma
genetics
pathology
2015
4
14
2015
4
18
6
0
2015
4
18
6
0
2015
7
15
6
0
ppublish
S1470-2045(15)70114-2
10.1016/S1470-2045(15)70114-2
25882982
25759811
2015
03
11
2015
03
11
2015
06
03
2296-4185
3
2015
Frontiers in bioengineering and biotechnology
Front Bioeng Biotechnol
Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative.
19
10.3389/fbioe.2015.00019
The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.
Hucka
Michael
M
Computing and Mathematical Sciences, California Institute of Technology , Pasadena, CA , USA.
Nickerson
David P
DP
Auckland Bioengineering Institute, University of Auckland , Auckland , New Zealand.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto , Toronto, ON , Canada.
Bergmann
Frank T
FT
Computing and Mathematical Sciences, California Institute of Technology , Pasadena, CA , USA ; BioQuant/Centre for Organismal Studies (COS), University of Heidelberg , Heidelberg , Germany.
Cooper
Jonathan
J
Department of Computer Science, University of Oxford , Oxford , UK.
Demir
Emek
E
Computational Biology, Memorial Sloan-Kettering Cancer Center , New York, NY , USA.
Garny
Alan
A
Auckland Bioengineering Institute, University of Auckland , Auckland , New Zealand.
Golebiewski
Martin
M
Scientific Databases and Visualization, Heidelberg Institute for Theoretical Studies (HITS) , Heidelberg , Germany.
Myers
Chris J
CJ
Department of Electrical and Computer Engineering, University of Utah , Salt Lake City, UT , USA.
Schreiber
Falk
F
Faculty of Information Technology, Monash University , Melbourne, VIC , Australia ; Institute of Computer Science, University Halle-Wittenberg , Halle , Germany.
Waltemath
Dagmar
D
Department of Systems Biology and Bioinformatics, University of Rostock , Rostock , Germany.
Le Novère
Nicolas
N
Babraham Institute , Cambridge , UK ; European Molecular Biology Laboratory-European Bioinformatics Institute , Cambridge , UK.
eng
P50 GM094503
GM
NIGMS NIH HHS
United States
Journal Article
Review
2015
02
24
Switzerland
Front Bioeng Biotechnol
101632513
2296-4185
PMC4338824
biology
computational modeling
data sharing
file formats
reproducible science
standardization
2015
2014
11
10
2015
2
08
2015
2
24
2015
3
12
6
0
2015
3
12
6
0
2015
3
12
6
1
epublish
10.3389/fbioe.2015.00019
25759811
PMC4338824
25729925
2015
03
20
2015
05
19
2015
03
21
1529-2916
16
4
2015
Apr
Nature immunology
Nat. Immunol.
IL-7 coordinates proliferation, differentiation and Tcra recombination during thymocyte β-selection.
397-405
10.1038/ni.3122
Signaling via the pre-T cell antigen receptor (pre-TCR) and the receptor Notch1 induces transient self-renewal (β-selection) of TCRβ(+) CD4(-)CD8(-) double-negative stage 3 (DN3) and DN4 progenitor cells that differentiate into CD4(+)CD8(+) double-positive (DP) thymocytes, which then rearrange the locus encoding the TCR α-chain (Tcra). Interleukin 7 (IL-7) promotes the survival of TCRβ(-) DN thymocytes by inducing expression of the pro-survival molecule Bcl-2, but the functions of IL-7 during β-selection have remained unclear. Here we found that IL-7 signaled TCRβ(+) DN3 and DN4 thymocytes to upregulate genes encoding molecules involved in cell growth and repressed the gene encoding the transcriptional repressor Bcl-6. Accordingly, IL-7-deficient DN4 cells lacked trophic receptors and did not proliferate but rearranged Tcra prematurely and differentiated rapidly. Deletion of Bcl6 partially restored the self-renewal of DN4 cells in the absence of IL-7, but overexpression of BCL2 did not. Thus, IL-7 critically acts cooperatively with signaling via the pre-TCR and Notch1 to coordinate proliferation, differentiation and Tcra recombination during β-selection.
Boudil
Amine
A
1] Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. [2] Department of Immunology, University of Toronto, Toronto, Canada.
Matei
Irina R
IR
Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada.
Shih
Han-Yu
HY
Department of Immunology, Duke University Medical Center, Durham, North Carolina, USA.
Bogdanoski
Goce
G
Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada.
Yuan
Julie S
JS
Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada.
Chang
Stephen G
SG
Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada.
Montpellier
Bertrand
B
1] Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. [2] Department of Immunology, University of Toronto, Toronto, Canada.
Kowalski
Paul E
PE
Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada.
Voisin
Veronique
V
The Donnelly Centre, University of Toronto, Toronto, Canada.
Bashir
Shaheena
S
The Donnelly Centre, University of Toronto, Toronto, Canada.
Bader
Gary D
GD
0000000301858861
1] The Donnelly Centre, University of Toronto, Toronto, Canada. [2] Department of Molecular Genetics, University of Toronto, Toronto, Canada.
Krangel
Michael S
MS
Department of Immunology, Duke University Medical Center, Durham, North Carolina, USA.
Guidos
Cynthia J
CJ
0000000276742612
1] Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, Canada. [2] Department of Immunology, University of Toronto, Toronto, Canada.
eng
GEO
GSE63932
FRN 11530
Canadian Institutes of Health Research
Canada
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
R37 GM041052
GM
NIGMS NIH HHS
United States
R37 GM41052
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2015
03
02
United States
Nat Immunol
100941354
1529-2908
0
Antigens, CD4
0
Antigens, CD8
0
Interleukin-7
0
Notch1 protein, mouse
0
Proto-Oncogene Proteins c-bcl-2
0
Proto-Oncogene Proteins c-bcl-6
0
Receptor, Notch1
0
Receptors, Antigen, T-Cell, alpha-beta
0
interleukin-7, mouse
114100-40-2
Bcl2 protein, mouse
IM
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25789675
Animals
Antigens, CD4
genetics
immunology
Antigens, CD8
genetics
immunology
Cell Differentiation
Cell Proliferation
Cell Survival
Gene Expression Regulation
Interleukin-7
deficiency
genetics
immunology
Mice
Mice, Inbred C57BL
Mice, Knockout
Proto-Oncogene Proteins c-bcl-2
genetics
immunology
Proto-Oncogene Proteins c-bcl-6
deficiency
genetics
immunology
Receptor, Notch1
genetics
immunology
Receptors, Antigen, T-Cell, alpha-beta
genetics
immunology
Recombination, Genetic
Signal Transduction
Thymocytes
cytology
immunology
metabolism
Thymus Gland
cytology
immunology
metabolism
NIHMS663283 [Available on 10/01/15]
PMC4368453 [Available on 10/01/15]
2014
12
01
2015
2
10
2015
3
02
2015
3
3
6
0
2015
3
3
6
0
2015
5
20
6
0
2015
10
1
0
0
ppublish
ni.3122
10.1038/ni.3122
25729925
PMC4368453
NIHMS663283
25702641
2015
03
15
2015
04
04
2213-6711
4
3
2015
Mar
10
Stem cell reports
Stem Cell Reports
A progesterone-CXCR4 axis controls mammary progenitor cell fate in the adult gland.
313-22
10.1016/j.stemcr.2015.01.011
S2213-6711(15)00032-6
Progesterone drives mammary stem and progenitor cell dynamics through paracrine mechanisms that are currently not well understood. Here, we demonstrate that CXCR4, the receptor for stromal-derived factor 1 (SDF-1; CXC12), is a crucial instructor of hormone-induced mammary stem and progenitor cell function. Progesterone elicits specific changes in the transcriptome of basal and luminal mammary epithelial populations, where CXCL12 and CXCR4 represent a putative ligand-receptor pair. In situ, CXCL12 localizes to progesterone-receptor-positive luminal cells, whereas CXCR4 is induced in both basal and luminal compartments in a progesterone-dependent manner. Pharmacological inhibition of CXCR4 signaling abrogates progesterone-directed expansion of basal (CD24(+)CD49f(hi)) and luminal (CD24(+)CD49f(lo)) subsets. This is accompanied by a marked reduction in CD49b(+)SCA-1(-) luminal progenitors, their functional capacity, and lobuloalveologenesis. These findings uncover CXCL12 and CXCR4 as novel paracrine effectors of hormone signaling in the adult mammary gland, and present a new avenue for potentially targeting progenitor cell growth and malignant transformation in breast cancer.
Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Shiah
Yu-Jia
YJ
Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada.
Tharmapalan
Pirashaanthy
P
Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada.
Casey
Alison E
AE
Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada.
Joshi
Purna A
PA
Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada.
McKee
Trevor D
TD
Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada; STTARR Innovation Centre, Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON M5G 1L7, Canada.
Jackson
Hartland W
HW
Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada.
Beristain
Alexander G
AG
Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada.
Chan-Seng-Yue
Michelle A
MA
Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.
Bader
Gary D
GD
Department of Molecular Genetics, Medical Science Building, University of Toronto, Toronto, ON M5S 1A8, Canada.
Lydon
John P
JP
Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
Waterhouse
Paul D
PD
Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada.
Boutros
Paul C
PC
Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada; Informatics and Biocomputing Platform, Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.
Khokha
Rama
R
Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 2M9, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada. Electronic address: rkhokha@uhnresearch.ca.
eng
GEO
GSE59558
Journal Article
Research Support, Non-U.S. Gov't
2015
02
19
United States
Stem Cell Reports
101611300
2213-6711
IM
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2
19
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S2213-6711(15)00032-6
10.1016/j.stemcr.2015.01.011
25702641
PMC4376056
25611800
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11
1
2015
Jan
PLoS genetics
PLoS Genet.
Evolutionary constraint and disease associations of post-translational modification sites in human genomes.
e1004919
10.1371/journal.pgen.1004919
Interpreting the impact of human genome variation on phenotype is challenging. The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data, however other factors are likely relevant. We hypothesized that variants in protein post-translational modification (PTM) sites contribute to phenotype variation and disease. We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio (Ka/Ks) in 7,500 human genomes and found a significant negative selection signal in PTM regions independent of six factors, including conservation, codon usage, and GC-content, that is widely distributed across tissue-specific genes and function classes. PTM regions are also enriched in known disease mutations, suggesting that PTM variation is more likely deleterious. PTM constraint also affects flanking sequence around modified residues and increases around clustered sites, indicating presence of functionally important short linear motifs. Using target site motifs of 124 kinases, we predict that at least ∼180,000 motif-breaker amino acid residues that disrupt PTM sites when substituted, and highlight kinase motifs that show specific negative selection and enrichment of disease mutations. We provide this dataset with corresponding hypothesized mechanisms as a community resource. As an example of our integrative approach, we propose that PTPN11 variants in Noonan syndrome aberrantly activate the protein by disrupting an uncharacterized cluster of phosphorylation sites. Further, as PTMs are molecular switches that are modulated by drugs, we study mutated binding sites of PTM enzymes in disease genes and define a drug-disease network containing 413 novel predicted disease-gene links.
Reimand
Jüri
J
The Donnelly Centre, University of Toronto, Canada.
Wagih
Omar
O
The Donnelly Centre, University of Toronto, Canada.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Canada.
eng
MOP-84324
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2015
01
22
United States
PLoS Genet
101239074
1553-7390
0
Codon
0
Proteins
EC 3.1.3.48
PTPN11 protein, human
EC 3.1.3.48
Protein Tyrosine Phosphatase, Non-Receptor Type 11
IM
Genome Res. 2009 Sep;19(9):1553-61
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Base Composition
Binding Sites
Codon
genetics
Conserved Sequence
genetics
Genome, Human
Humans
Noonan Syndrome
etiology
genetics
Protein Processing, Post-Translational
genetics
Protein Tyrosine Phosphatase, Non-Receptor Type 11
genetics
Proteins
genetics
metabolism
Selection, Genetic
genetics
PMC4303425
2015
1
2014
7
4
2014
11
24
2015
1
22
2015
1
23
6
0
2015
1
23
6
0
2015
7
1
6
0
epublish
10.1371/journal.pgen.1004919
PGENETICS-D-14-01805
25611800
PMC4303425
25580224
2015
01
12
2015
01
12
2015
01
14
2046-1402
3
2014
F1000Research
F1000Res
The Cytoscape app article collection.
138
10.12688/f1000research.4642.1
As a network visualization and analysis platform, Cytoscape relies on apps to provide domain-specific features and functions. There are many resources available to support Cytoscape app development and distribution, including the Cytoscape App Store and an online "cookbook" for app developers. This article collection is another resource to help researchers find out more about relevant Cytoscape apps and to provide app developers with useful implementation tips. The collection will grow over time as new Cytoscape apps are developed and published.
Pico
Alexander R
AR
Gladstone Institutes, San Francisco, CA 95158, USA.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
Demchak
Barry
B
University of California, San Diego, La Jolla, CA 92093, USA.
Guitart Pla
Oriol
O
Institut Pasteur, Paris, 75015, France.
Hull
Timothy
T
University of California, San Diego, La Jolla, CA 92093, USA.
Longabaugh
William
W
Institute for Systems Biology, Seattle, WA 98109, USA.
Lopes
Christian
C
The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
Lotia
Samad
S
Gladstone Institutes, San Francisco, CA 95158, USA.
Molenaar
Piet
P
Amsterdam Medical Centre, Amsterdam, 1105 AZ, Netherlands.
Montojo
Jason
J
The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
Morris
John H
JH
University of California San Francisco, San Francisco, CA 94143, USA.
Ono
Keiichiro
K
University of California, San Diego, La Jolla, CA 92093, USA.
Schwikowski
Benno
B
Institut Pasteur, Paris, 75015, France.
Welker
David
D
University of California, San Diego, La Jolla, CA 92093, USA.
Ideker
Trey
T
University of California, San Diego, La Jolla, CA 92093, USA.
eng
Journal Article
2014
07
01
England
F1000Res
101594320
2046-1402
PMC4288400
2014
2014
6
26
2014
7
1
2015
1
13
6
0
2015
1
13
6
0
2015
1
13
6
1
epublish
10.12688/f1000research.4642.1
25580224
PMC4288400
25561528
2015
01
21
2015
04
27
2015
03
17
1091-6490
112
3
2015
Jan
20
Proceedings of the National Academy of Sciences of the United States of America
Proc. Natl. Acad. Sci. U.S.A.
Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity.
851-6
10.1073/pnas.1320611111
Glioblastoma (GBM) is a cancer comprised of morphologically, genetically, and phenotypically diverse cells. However, an understanding of the functional significance of intratumoral heterogeneity is lacking. We devised a method to isolate and functionally profile tumorigenic clones from patient glioblastoma samples. Individual clones demonstrated unique proliferation and differentiation abilities. Importantly, naïve patient tumors included clones that were temozolomide resistant, indicating that resistance to conventional GBM therapy can preexist in untreated tumors at a clonal level. Further, candidate therapies for resistant clones were detected with clone-specific drug screening. Genomic analyses revealed genes and pathways that associate with specific functional behavior of single clones. Our results suggest that functional clonal profiling used to identify tumorigenic and drug-resistant tumor clones will lead to the discovery of new GBM clone-specific treatment strategies.
Meyer
Mona
M
Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8;
Reimand
Jüri
J
Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; The Donnelly Centre, University of Toronto, Toronto, ON, Canada M5S 3E1 Canada;
Lan
Xiaoyang
X
Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8;
Head
Renee
R
Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8;
Zhu
Xueming
X
Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8;
Kushida
Michelle
M
Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8;
Bayani
Jane
J
Department of Transformative Pathology at the Ontario Institute for Cancer Research, Ontario Institute for Cancer Research, Toronto, ON, Canada M5G 0A3;
Pressey
Jessica C
JC
Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada M5S 3G5;
Lionel
Anath C
AC
Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada M5G 1L7;
Clarke
Ian D
ID
Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Ontario College of Art and Design, Toronto, ON, Canada M5T 1W1;
Cusimano
Michael
M
Division of Neurosurgery, St. Michael's Hospital, Toronto, ON, Canada M5B 1W8;
Squire
Jeremy A
JA
Department of Pathology, Queen's University, Kingston, ON, Canada K7L 3N6;
Scherer
Stephen W
SW
Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada M5G 1L7;
Bernstein
Mark
M
Division of Neurosurgery, Toronto Western Hospital, Toronto, ON, Canada M5T 2S8; and.
Woodin
Melanie A
MA
Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada M5S 3G5;
Bader
Gary D
GD
Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; The Donnelly Centre, University of Toronto, Toronto, ON, Canada M5S 3E1 Canada; gary.bader@utoronto.ca peter.dirks@sickkids.ca.
Dirks
Peter B
PB
Division of Neurosurgery, Program in Developmental and Stem Cell Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada M5G 1X8; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 1A8; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada M5S 1A8 gary.bader@utoronto.ca peter.dirks@sickkids.ca.
eng
P41GM103504
GM
NIGMS NIH HHS
United States
R01 CA121941
CA
NCI NIH HHS
United States
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2015
01
05
United States
Proc Natl Acad Sci U S A
7505876
0027-8424
0
Antineoplastic Agents
7GR28W0FJI
Dacarbazine
85622-93-1
temozolomide
IM
Nat Methods. 2014 Apr;11(4):396-8
24633410
PLoS One. 2010;5(11):e13984
21085593
Nat Rev Cancer. 2014 Feb;14(2):92-107
24457416
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11070098
Bioinformatics. 2003 Aug 12;19(12):1572-4
12912839
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8052651
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15549107
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15674479
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21399628
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21628563
Nucleic Acids Res. 2011 Jul;39(Web Server issue):W307-15
21646343
Cancer Cell. 2011 Dec 13;20(6):810-7
22137795
Proc Natl Acad Sci U S A. 2012 Feb 21;109(8):3041-6
22323597
Cancer Res. 2012 Apr 1;72(7):1614-20
22311673
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22495314
Clin Cancer Res. 2012 Aug 15;18(16):4240-6
22648273
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22912587
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23079654
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23412337
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24120142
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24336570
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17652770
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18772396
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18772890
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19023080
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15758009
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17417631
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19535535
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19896441
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20129251
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20399149
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20472883
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24925914
Antineoplastic Agents
therapeutic use
Brain Neoplasms
drug therapy
genetics
pathology
Cell Line, Tumor
Dacarbazine
analogs & derivatives
therapeutic use
Drug Resistance, Neoplasm
Glioblastoma
drug therapy
genetics
pathology
Humans
Single-Cell Analysis
PMC4311802
cancer
clonal heterogeneity
functional analysis
genomic analysis
glioblastoma
2015
1
5
2015
1
7
6
0
2015
1
7
6
0
2015
4
29
6
0
ppublish
25561528
1320611111
10.1073/pnas.1320611111
PMC4311802
25361207
2014
12
03
2015
02
15
1742-2051
11
1
2015
Jan
Molecular bioSystems
Mol Biosyst
Systems analysis reveals down-regulation of a network of pro-survival miRNAs drives the apoptotic response in dilated cardiomyopathy.
239-51
10.1039/c4mb00265b
Apoptosis is a hallmark of multiple etiologies of heart failure, including dilated cardiomyopathy. Since microRNAs are master regulators of cardiac development and key effectors of intracellular signaling, they represent novel candidates for understanding the mechanisms driving the increased dysfunction and loss of cardiomyocytes during cardiovascular disease progression. To determine the role of cardiac miRNAs in the apoptotic response, we used microarray technology to monitor miRNA levels in a validated murine phospholambam mutant model of dilated cardiomyopathy. 24 miRNAs were found to be differentially expressed, most of which have not been previously linked to dilated cardiomyopathy. We showed that individual silencing of 7 out of 8 significantly down-regulated miRNAs (mir-1, -29c, -30c, -30d, -149, -486, -499) led to a strong apoptotic phenotype in cell culture, suggesting they repress pro-apoptotic factors. To identify putative miRNA targets most likely relevant to cell death, we computationally integrated transcriptomic, proteomic and functional annotation data. We showed the dependency of prioritized target abundance on miRNA expression using RNA interference and quantitative mass spectrometry. We concluded that down regulation of key pro-survival miRNAs causes up-regulation of apoptotic signaling effectors that contribute to cardiac cell loss, potentially leading to system decompensation and heart failure.
Isserlin
Ruth
R
The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario, Canada M5S 3E1. andrew.emili@utoronto.ca.
Merico
Daniele
D
Wang
Dingyan
D
Vuckovic
Dajana
D
Bousette
Nicolas
N
Gramolini
Anthony O
AO
Bader
Gary D
GD
Emili
Andrew
A
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2014
10
31
England
Mol Biosyst
101251620
1742-2051
IM
2014
10
31
2014
12
2
2014
11
1
6
0
2014
11
2
6
0
2014
11
2
6
0
ppublish
10.1039/c4mb00265b
25361207
25330770
2014
12
04
2015
07
21
2015
03
17
1757-4684
6
12
2014
Dec
EMBO molecular medicine
EMBO Mol Med
Combined deletion of Pten and p53 in mammary epithelium accelerates triple-negative breast cancer with dependency on eEF2K.
1542-60
10.15252/emmm.201404402
The tumor suppressors Pten and p53 are frequently lost in breast cancer, yet the consequences of their combined inactivation are poorly understood. Here, we show that mammary-specific deletion of Pten via WAP-Cre, which targets alveolar progenitors, induced tumors with shortened latency compared to those induced by MMTV-Cre, which targets basal/luminal progenitors. Combined Pten-p53 mutations accelerated formation of claudin-low, triple-negative-like breast cancer (TNBC) that exhibited hyper-activated AKT signaling and more mesenchymal features relative to Pten or p53 single-mutant tumors. Twenty-four genes that were significantly and differentially expressed between WAP-Cre:Pten/p53 and MMTV-Cre:Pten/p53 tumors predicted poor survival for claudin-low patients. Kinome screens identified eukaryotic elongation factor-2 kinase (eEF2K) inhibitors as more potent than PI3K/AKT/mTOR inhibitors on both mouse and human Pten/p53-deficient TNBC cells. Sensitivity to eEF2K inhibition correlated with AKT pathway activity. eEF2K monotherapy suppressed growth of Pten/p53-deficient TNBC xenografts in vivo and cooperated with doxorubicin to efficiently kill tumor cells in vitro. Our results identify a prognostic signature for claudin-low patients and provide a rationale for using eEF2K inhibitors for treatment of TNBC with elevated AKT signaling.
© 2014 The Authors. Published under the terms of the CC BY 4.0 license.
Liu
Jeff C
JC
Division of Advanced Diagnostics, Toronto General Research Institute - University Health Network, Toronto, ON, Canada.
Voisin
Veronique
V
The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
Wang
Sharon
S
Division of Advanced Diagnostics, Toronto General Research Institute - University Health Network, Toronto, ON, Canada Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada.
Wang
Dong-Yu
DY
Princess Margaret Cancer Center, Toronto, ON, Canada Campbell Family Institute for Breast Cancer Research, Princess Margaret Hospital, Toronto, ON, Canada.
Jones
Robert A
RA
Division of Advanced Diagnostics, Toronto General Research Institute - University Health Network, Toronto, ON, Canada.
Datti
Alessandro
A
SMART Laboratory for High-Throughput Screening Programs, Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital, Toronto, ON, Canada Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, Italy.
Uehling
David
D
Drug Discovery Program, Department of Pharmacology and Toxicology, Ontario Institute for Cancer Research, University of Toronto, Toronto, ON, Canada.
Al-awar
Rima
R
Drug Discovery Program, Department of Pharmacology and Toxicology, Ontario Institute for Cancer Research, University of Toronto, Toronto, ON, Canada.
Egan
Sean E
SE
Program in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
Tsao
Ming
M
Princess Margaret Cancer Center, Toronto, ON, Canada Department of Medical Biophysics, University Health Network, Toronto, ON, Canada.
Mak
Tak W
TW
Campbell Family Institute for Breast Cancer Research, Princess Margaret Hospital, Toronto, ON, Canada SMART Laboratory for High-Throughput Screening Programs, Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital, Toronto, ON, Canada Department of Medical Biophysics, University Health Network, Toronto, ON, Canada.
Zacksenhaus
Eldad
E
Division of Advanced Diagnostics, Toronto General Research Institute - University Health Network, Toronto, ON, Canada Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada Department of Medical Biophysics, University Health Network, Toronto, ON, Canada eldad.zacksenhaus@utoronto.ca.
eng
GM103504
GM
NIGMS NIH HHS
United States
R01 CA121941
CA
NCI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
2014
10
20
England
EMBO Mol Med
101487380
1757-4676
0
Enzyme Inhibitors
0
Tumor Suppressor Protein p53
EC 2.7.1.17
Elongation Factor 2 Kinase
EC 2.7.11.1
Oncogene Protein v-akt
EC 3.1.3.67
PTEN Phosphohydrolase
IM
Cell. 2013 May 23;153(5):1064-79
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Animals
Elongation Factor 2 Kinase
antagonists & inhibitors
genetics
metabolism
Enzyme Inhibitors
pharmacology
Epithelium
enzymology
metabolism
Female
Gene Deletion
Humans
Mammary Glands, Human
enzymology
metabolism
Mice
Mice, Inbred C57BL
Oncogene Protein v-akt
genetics
metabolism
PTEN Phosphohydrolase
genetics
metabolism
Triple Negative Breast Neoplasms
drug therapy
enzymology
genetics
Tumor Suppressor Protein p53
genetics
metabolism
PMC4287974
Pten
eEF2K
p53
prognosis
triple‐negative breast cancer
2014
10
22
6
0
2014
10
22
6
0
2015
7
22
6
0
epublish
25330770
emmm.201404402
10.15252/emmm.201404402
PMC4287974
25254104
2014
09
25
2014
09
25
2014
09
27
2046-1402
3
2014
F1000Research
F1000Res
GeneMANIA: Fast gene network construction and function prediction for Cytoscape.
153
10.12688/f1000research.4572.1
The GeneMANIA Cytoscape app enables users to construct a composite gene-gene functional interaction network from a gene list. The resulting network includes the genes most related to the original list, and functional annotations from Gene Ontology. The edges are annotated with details about the publication or data source the interactions were derived from. The app leverages GeneMANIA's database of 1800+ networks, containing over 500 million interactions spanning 8 organisms: A. thaliana, C. elegans, D. melanogaster, D. rerio, H. sapiens, M. musculus, R. norvegicus, and S. cerevisiae. Users may also import their own organisms, networks, and expression profiles. The app is compatible with Cytoscape versions 2 and 3.
Montojo
Jason
J
Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada.
Zuberi
Khalid
K
Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada.
Rodriguez
Harold
H
Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada.
Bader
Gary D
GD
Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada.
Morris
Quaid
Q
Departments of Molecular Genetics and Computer Science, The Donnelly Centre, University of Toronto, Toronto, ON, M5S 3E1, Canada.
eng
Journal Article
2014
07
01
England
F1000Res
101594320
2046-1402
PMC4168749
2014
2014
6
20
2014
7
1
2014
9
26
6
0
2014
9
26
6
0
2014
9
26
6
1
epublish
10.12688/f1000research.4572.1
25254104
PMC4168749
25199793
2014
09
09
1934-340X
47
2014
Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.]
Curr Protoc Bioinformatics
Biological network exploration with cytoscape 3.
8.13.1-8.13.24
10.1002/0471250953.bi0813s47
Cytoscape is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene, and other types of interactions. It offers researchers a versatile and interactive visualization interface for exploring complex biological interconnections supported by diverse annotation and experimental data, thereby facilitating research tasks such as predicting gene function and constructing pathways. Cytoscape provides core functionality to load, visualize, search, filter, and save networks, and hundreds of Apps extend this functionality to address specific research needs. The latest generation of Cytoscape (version 3.0 and later) has substantial improvements in function, user interface, and performance relative to previous versions. This protocol aims to jump-start new users with specific protocols for basic Cytoscape functions, such as installing Cytoscape and Cytoscape Apps, loading data, visualizing and navigating the networks, visualizing network associated data (attributes), and identifying clusters. It also highlights new features that benefit experienced users. Curr. Protoc. Bioinform. 47:8.13.1-8.13.24. © 2014 by John Wiley & Sons, Inc.
Copyright © 2014 John Wiley & Sons, Inc.
Su
Gang
G
Molecular Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, Michigan.
Morris
John H
JH
Demchak
Barry
B
Bader
Gary D
GD
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
2014
09
08
United States
Curr Protoc Bioinformatics
101157830
1934-3396
IM
Nucleic Acids Res. 2013 Jan;41(Database issue):D816-23
23203989
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12169552
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23595664
Nucleic Acids Res. 2013 Jul;41(Web Server issue):W115-22
23794635
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D514-7
15608251
Nucleic Acids Res. 2009 Jan;37(Database issue):D642-6
18978014
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17623706
Proc Natl Acad Sci U S A. 2007 May 22;104(21):8685-90
17502601
PLoS Comput Biol. 2007 Apr 20;3(4):e59
17447836
Nat Protoc. 2006;1(2):662-71
17406294
Nucleic Acids Res. 2007 Jan;35(Database issue):D760-5
17099226
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24271398
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15608231
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23242164
NIHMS628460 [Available on 09/08/15]
PMC4174321 [Available on 09/08/15]
Cytoscape
interactive network visualization
network analysis
2014
9
10
6
0
2014
9
10
6
0
2014
9
10
6
0
2015
9
8
0
0
epublish
10.1002/0471250953.bi0813s47
25199793
PMC4174321
NIHMS628460
25075306
2014
07
30
2014
07
30
2015
03
17
2046-1402
3
2014
F1000Research
F1000Res
Enrichment Map - a Cytoscape app to visualize and explore OMICs pathway enrichment results.
141
10.12688/f1000research.4536.1
High-throughput OMICs experiments generate signals for millions of entities (i.e. genes, proteins, metabolites or any measurable biological entity) in the cell. In an effort to summarize and explore these signals, expression results are examined in the context of known pathways and processes, through enrichment analysis to generate a set of pathways and processes that is significantly enriched. Due to the high redundancy in annotation resources this often results in hundreds of sets. To facilitate the analysis of these results, we have developed the Enrichment Map app to visualize enrichments as a network. We have updated Enrichment Map to support Cytoscape 3, and have added additional features including new data formats and command line access.
Isserlin
Ruth
R
The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
Merico
Daniele
D
The Centre for Applied Genomics, Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.
Voisin
Veronique
V
The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
eng
R01 CA121941
CA
NCI NIH HHS
United States
Journal Article
2014
07
01
England
F1000Res
101594320
2046-1402
PMC4103489
2014
2014
6
20
2014
7
1
2014
7
31
6
0
2014
7
31
6
0
2014
7
31
6
1
epublish
10.12688/f1000research.4536.1
25075306
PMC4103489
25043047
2014
07
24
2014
10
09
2015
07
29
1476-4687
511
7510
2014
Jul
24
Nature
Nature
Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma.
428-34
10.1038/nature13379
Medulloblastoma is a highly malignant paediatric brain tumour currently treated with a combination of surgery, radiation and chemotherapy, posing a considerable burden of toxicity to the developing child. Genomics has illuminated the extensive intertumoral heterogeneity of medulloblastoma, identifying four distinct molecular subgroups. Group 3 and group 4 subgroup medulloblastomas account for most paediatric cases; yet, oncogenic drivers for these subtypes remain largely unidentified. Here we describe a series of prevalent, highly disparate genomic structural variants, restricted to groups 3 and 4, resulting in specific and mutually exclusive activation of the growth factor independent 1 family proto-oncogenes, GFI1 and GFI1B. Somatic structural variants juxtapose GFI1 or GFI1B coding sequences proximal to active enhancer elements, including super-enhancers, instigating oncogenic activity. Our results, supported by evidence from mouse models, identify GFI1 and GFI1B as prominent medulloblastoma oncogenes and implicate 'enhancer hijacking' as an efficient mechanism driving oncogene activation in a childhood cancer.
Northcott
Paul A
PA
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2].
Lee
Catherine
C
1] Biomedical Sciences Graduate Program, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0685, USA [2] Tumor Initiation and Maintenance Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA [3].
Zichner
Thomas
T
1] European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany [2].
Stütz
Adrian M
AM
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany.
Erkek
Serap
S
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2] European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany.
Kawauchi
Daisuke
D
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Shih
David J H
DJ
The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
Hovestadt
Volker
V
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Zapatka
Marc
M
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Sturm
Dominik
D
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Jones
David T W
DT
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Kool
Marcel
M
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Remke
Marc
M
The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
Cavalli
Florence M G
FM
The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
Zuyderduyn
Scott
S
The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.
VandenBerg
Scott
S
Department of Pathology, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.
Esparza
Lourdes Adriana
LA
Tumor Initiation and Maintenance Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA.
Ryzhova
Marina
M
Department of Neuropathology, NN Burdenko Neurosurgical Institute, 4th Tverskaya-Yamskaya 16, Moscow 125047, Russia.
Wang
Wei
W
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Wittmann
Andrea
A
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Stark
Sebastian
S
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Sieber
Laura
L
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Seker-Cin
Huriye
H
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Linke
Linda
L
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Kratochwil
Fabian
F
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Jäger
Natalie
N
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Buchhalter
Ivo
I
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Imbusch
Charles D
CD
Data Management Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Zipprich
Gideon
G
Data Management Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Raeder
Benjamin
B
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany.
Schmidt
Sabine
S
Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Diessl
Nicolle
N
Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Wolf
Stephan
S
Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Wiemann
Stefan
S
Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Brors
Benedikt
B
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Lawerenz
Chris
C
Data Management Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Eils
Jürgen
J
Data Management Facility, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Warnatz
Hans-Jörg
HJ
Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin 14195, Germany.
Risch
Thomas
T
Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin 14195, Germany.
Yaspo
Marie-Laure
ML
Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin 14195, Germany.
Weber
Ursula D
UD
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Bartholomae
Cynthia C
CC
Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, Heidelberg 69120, Germany.
von Kalle
Christof
C
1] Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, Heidelberg 69120, Germany [2] Heidelberg Center for Personalised Oncology (DKFZ-HIPO), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Turányi
Eszter
E
1st Department of Pathology and Experimental Cancer Research, Semmelweis University SE, II.sz. Gyermekklinika, Budapest 1094, Hungary.
Hauser
Peter
P
2nd Department of Pediatrics, Semmelweis University, SE, II.sz. Gyermekklinika, Budapest 1094, Hungary.
Sanden
Emma
E
1] Glioma Immunotherapy Group, Division of Neurosurgery, Lund University, Paradisgatan 2, Lund 221 00, Sweden [2] Department of Clinical Sciences, Lund University, Paradisgatan 2, Lund 221 00, Sweden.
Darabi
Anna
A
1] Glioma Immunotherapy Group, Division of Neurosurgery, Lund University, Paradisgatan 2, Lund 221 00, Sweden [2] Department of Clinical Sciences, Lund University, Paradisgatan 2, Lund 221 00, Sweden.
Siesjö
Peter
P
1] Glioma Immunotherapy Group, Division of Neurosurgery, Lund University, Paradisgatan 2, Lund 221 00, Sweden [2] Department of Clinical Sciences, Lund University, Paradisgatan 2, Lund 221 00, Sweden.
Sterba
Jaroslav
J
Department of Pediatric Oncology, Masaryk University and University Hospital, Brno, Cernopolni 9 Brno 613 00, Czech Republic.
Zitterbart
Karel
K
Department of Pediatric Oncology, Masaryk University and University Hospital, Brno, Cernopolni 9 Brno 613 00, Czech Republic.
Sumerauer
David
D
Department of Pediatric Hematology and Oncology, 2nd Faculty of Medicine, Charles University and University Hospital Motol, V Úvalu 84, Prague 150 06, Czech Republic.
van Sluis
Peter
P
Department of Oncogenomics, AMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105, AZ Netherlands.
Versteeg
Rogier
R
Department of Oncogenomics, AMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105, AZ Netherlands.
Volckmann
Richard
R
Department of Oncogenomics, AMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105, AZ Netherlands.
Koster
Jan
J
Department of Oncogenomics, AMC, University of Amsterdam, Meibergdreef 9, Amsterdam 1105, AZ Netherlands.
Schuhmann
Martin U
MU
Department of Neurosurgery, Tübingen University Hospital, Hoppe-Seyler Strasse 3, Tübingen 72076, Germany.
Ebinger
Martin
M
Department of Neurosurgery, Tübingen University Hospital, Hoppe-Seyler Strasse 3, Tübingen 72076, Germany.
Grimes
H Leighton
HL
Division of Immunobiology, Program in Cancer Pathology of the Divisions of Experimental Hematology and Pathology, Program in Hematologic Malignancies of the Cancer and Blood Disease Insitute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, Ohio 452229, USA.
Robinson
Giles W
GW
1] Department of Developmental Neurobiology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA [2] Department of Oncology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA.
Gajjar
Amar
A
Department of Oncology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA.
Mynarek
Martin
M
Department of Paediatric Haematology and Oncology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, Hamburg 20246, Germany.
von Hoff
Katja
K
Department of Paediatric Haematology and Oncology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, Hamburg 20246, Germany.
Rutkowski
Stefan
S
Department of Paediatric Haematology and Oncology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, Hamburg 20246, Germany.
Pietsch
Torsten
T
Department of Neuropathology, University of Bonn, Sigmund-Freud-Str. 25, Bonn 53105, Germany.
Scheurlen
Wolfram
W
Cnopf'sche Kinderklinik, Nürnberg Children's Hospital, St-Johannis-Mühlgasse 19, Nürnberg 90419, Germany.
Felsberg
Jörg
J
Department of Neuropathology, Heinrich-Heine-University Düsseldorf, Moorenstrasse 5, Düsseldorf 40225, Germany.
Reifenberger
Guido
G
Department of Neuropathology, Heinrich-Heine-University Düsseldorf, Moorenstrasse 5, Düsseldorf 40225, Germany.
Kulozik
Andreas E
AE
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Im Neuenheimer Feld 430, Heidelberg 69120, Germany.
von Deimling
Andreas
A
Department of Neuropathology, University of Heidelberg, Im Neuenheimer Feld 220, Heidelberg 69120, Germany.
Witt
Olaf
O
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Im Neuenheimer Feld 430, Heidelberg 69120, Germany.
Eils
Roland
R
1] Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2] Heidelberg Center for Personalised Oncology (DKFZ-HIPO), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Gilbertson
Richard J
RJ
1] Department of Developmental Neurobiology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA [2] Department of Oncology, St Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, Tennessee 38105, USA.
Korshunov
Andrey
A
Department of Neuropathology, University of Heidelberg, Im Neuenheimer Feld 220, Heidelberg 69120, Germany.
Taylor
Michael D
MD
1] The Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada [2] Division of Neurosurgery, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada.
Lichter
Peter
P
1] Division of Molecular Genetics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2] Heidelberg Center for Personalised Oncology (DKFZ-HIPO), Im Neuenheimer Feld 280, Heidelberg 69120, Germany.
Korbel
Jan O
JO
1] European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, Heidelberg 69117, Germany [2] EMBL, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Saffron Walden CB10 1SD, UK.
Wechsler-Reya
Robert J
RJ
Tumor Initiation and Maintenance Program, Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, California 92037, USA.
Pfister
Stefan M
SM
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany [2] Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Im Neuenheimer Feld 430, Heidelberg 69120, Germany.
eng
5P30CA030199
CA
NCI NIH HHS
United States
P01 CA096832
CA
NCI NIH HHS
United States
P30 CA030199
CA
NCI NIH HHS
United States
P41GM103504
GM
NIGMS NIH HHS
United States
R01 CA159859
CA
NCI NIH HHS
United States
R01 CA159859
CA
NCI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2014
06
22
England
Nature
0410462
0028-0836
0
DNA-Binding Proteins
0
GFI1 protein, human
0
GFI1B protein, human
0
Proto-Oncogene Proteins
0
Repressor Proteins
0
Transcription Factors
IM
Nat Rev Cancer. 2012 Dec;12(12):818-34
23175120
Bioinformatics. 2001 Oct;17(10):977-87
11673243
Genome Res. 2013 Feb;23(2):217-27
23132910
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23540689
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23582322
Acta Neuropathol. 2013 Jun;125(6):913-6
23670100
J Clin Oncol. 2013 Aug 10;31(23):2927-35
23835706
Nat Rev Genet. 2013 Nov;14(11):765-80
24105274
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24137015
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24119843
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20110278
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21150899
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21493656
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21835007
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11825872
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9840930
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22832583
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22962449
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23128226
Cancer Cell. 2014 Aug 11;26(2):160-1
25117708
Animals
Child
Chromosomes, Human, Pair 9
genetics
DNA-Binding Proteins
genetics
metabolism
Enhancer Elements, Genetic
genetics
Genomic Structural Variation
genetics
Humans
Medulloblastoma
classification
genetics
pathology
Mice
Oncogenes
genetics
Proto-Oncogene Proteins
genetics
metabolism
Repressor Proteins
genetics
metabolism
Transcription Factors
genetics
metabolism
NIHMS618924
PMC4201514
2014
1
12
2014
4
15
2014
6
22
2014
7
22
6
0
2014
7
22
6
0
2014
10
10
6
0
ppublish
nature13379
10.1038/nature13379
25043047
PMC4201514
NIHMS618924
25028490
2014
07
16
2015
02
09
2015
01
21
1744-4292
10
2014
Molecular systems biology
Mol. Syst. Biol.
Intercellular network structure and regulatory motifs in the human hematopoietic system.
741
10.15252/msb.20145141
The hematopoietic system is a distributed tissue that consists of functionally distinct cell types continuously produced through hematopoietic stem cell (HSC) differentiation. Combining genomic and phenotypic data with high-content experiments, we have built a directional cell-cell communication network between 12 cell types isolated from human umbilical cord blood. Network structure analysis revealed that ligand production is cell type dependent, whereas ligand binding is promiscuous. Consequently, additional control strategies such as cell frequency modulation and compartmentalization were needed to achieve specificity in HSC fate regulation. Incorporating the in vitro effects (quiescence, self-renewal, proliferation, or differentiation) of 27 HSC binding ligands into the topology of the cell-cell communication network allowed coding of cell type-dependent feedback regulation of HSC fate. Pathway enrichment analysis identified intracellular regulatory motifs enriched in these cell type- and ligand-coupled responses. This study uncovers cellular mechanisms of hematopoietic cell feedback in HSC fate regulation, provides insight into the design principles of the human hematopoietic system, and serves as a foundation for the analysis of intercellular regulation in multicellular systems.
© 2014 The Authors. Published under the terms of the CC BY 4.0 license.
Qiao
Wenlian
W
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
Wang
Weijia
W
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
Laurenti
Elisa
E
Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
Turinsky
Andrei L
AL
The Hospital for Sick Children, Toronto, ON, Canada.
Wodak
Shoshana J
SJ
The Hospital for Sick Children, Toronto, ON, Canada Department of Biochemistry, University of Toronto, Toronto, ON, Canada.
Bader
Gary D
GD
Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
Dick
John E
JE
Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
Zandstra
Peter W
PW
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada The Donnelly Centre, University of Toronto, Toronto, ON, Canada Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada McEwen Centre for Regenerative Medicine, University of Health Network, Toronto, ON, Canada Heart & Stroke/Richard Lewar Centre of Excellence, Toronto, ON, Canada peter.zandstra@utoronto.ca.
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2014
07
15
England
Mol Syst Biol
101235389
1744-4292
0
Ligands
IM
Science. 2011 Jul 8;333(6039):218-21
21737740
PLoS One. 2011;6(10):e25995
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Database (Oxford). 2011;2011:bar049
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22281595
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22281599
Cell Stem Cell. 2012 Feb 3;10(2):218-29
22305571
Stem Cells. 2012 Apr;30(4):709-18
22290873
Blood. 2012 Mar 29;119(13):2991-3002
22246037
Leukemia. 2012 May;26(5):1073-80
21941367
Cell Stem Cell. 2012 Nov 2;11(5):701-14
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21282381
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21551058
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21593866
Algorithms
Cell Communication
Cells, Cultured
Gene Expression Profiling
Hematopoiesis
Hematopoietic Stem Cells
cytology
physiology
Humans
Ligands
PMC4299490
feedback regulation
hematopoietic stem cell
intercellular signaling
2014
7
17
6
0
2014
7
17
6
0
2015
2
11
6
0
epublish
25028490
PMC4299490
24904632
2014
06
06
2014
06
06
2014
06
09
1664-8021
5
2014
Frontiers in genetics
Front Genet
Network Assessor: an automated method for quantitative assessment of a network's potential for gene function prediction.
123
10.3389/fgene.2014.00123
Significant effort has been invested in network-based gene function prediction algorithms based on the guilt by association (GBA) principle. Existing approaches for assessing prediction performance typically compute evaluation metrics, either averaged across all functions being considered, or strictly from properties of the network. Since the success of GBA algorithms depends on the specific function being predicted, evaluation metrics should instead be computed for each function. We describe a novel method for computing the usefulness of a network by measuring its impact on gene function cross validation prediction performance across all gene functions. We have implemented this in software called Network Assessor, and describe its use in the GeneMANIA (GM) quality control system. Network Assessor is part of the GM command line tools.
Montojo
Jason
J
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
Zuberi
Khalid
K
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
Shao
Quentin
Q
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
Bader
Gary D
GD
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
Morris
Quaid
Q
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto Toronto, ON, Canada.
eng
Journal Article
2014
05
16
Switzerland
Front Genet
101560621
1664-8021
PMC4032932
cross validation
function prediction
machine learning
network biology
network inference
2014
2014
1
06
2014
4
21
2014
5
16
2014
6
7
6
0
2014
6
7
6
0
2014
6
7
6
1
epublish
10.3389/fgene.2014.00123
24904632
PMC4032932
24878544
2014
06
02
2015
01
12
1932-6203
9
5
2014
PloS one
PLoS ONE
Characterizing the Late Pleistocene MSA Lithic Technology of Sibudu, KwaZulu-Natal, South Africa.
e98359
10.1371/journal.pone.0098359
Studies of the African Middle Stone Age (MSA) have become central for defining the cultural adaptations that accompanied the evolution of modern humans. While much of recent research in South Africa has focused on the Still Bay and Howiesons Poort (HP), periods following these technocomplexes were often neglected. Here we examine lithic assemblages from Sibudu that post-date the HP to further the understanding of MSA cultural variability during the Late Pleistocene. Sibudu preserves an exceptionally thick, rich, and high-resolution archaeological sequence that dates to ∼ 58 ka, which has recently been proposed as type assemblage for the "Sibudan". This study presents a detailed analysis of the six uppermost lithic assemblages from these deposits (BM-BSP) that we excavated from 2011-2013. We define the key elements of the lithic technology and compare our findings to other assemblages post-dating the HP. The six lithic assemblages provide a distinct and robust cultural signal, closely resembling each other in various technological, techno-functional, techno-economic, and typological characteristics. These results refute assertions that modern humans living after the HP possessed an unstructured and unsophisticated MSA lithic technology. While we observed several parallels with other contemporaneous MSA sites, particularly in the eastern part of southern Africa, the lithic assemblages at Sibudu demonstrate a distinct and so far unique combination of techno-typological traits. Our findings support the use of the Sibudan to help structuring this part of the southern African MSA and emphasize the need for further research to identify the spatial and temporal extent of this proposed cultural unit.
Will
Manuel
M
Department of Early Prehistory and Quaternary Ecology, University of Tübingen, Schloss Hohentübingen, Tübingen, Germany.
Bader
Gregor D
GD
Department of Early Prehistory and Quaternary Ecology, University of Tübingen, Schloss Hohentübingen, Tübingen, Germany.
Conard
Nicholas J
NJ
Department of Early Prehistory and Quaternary Ecology, University of Tübingen, Schloss Hohentübingen, Tübingen, Germany; Senckenberg Center for Human Evolution and Paleoecology, University of Tübingen, Schloss Hohentübingen, Tübingen, Germany.
eng
Journal Article
Research Support, Non-U.S. Gov't
2014
05
30
United States
PLoS One
101285081
1932-6203
IM
J Hum Evol. 2000 Nov;39(5):453-563
11102266
Proc Natl Acad Sci U S A. 2009 Jun 16;106(24):9590-4
19433786
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11786608
Nature. 2003 Jun 12;423(6941):742-7
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Curr Anthropol. 2003 Dec;44(5):627-51
14971366
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15087540
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15656934
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20934095
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16469361
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18974351
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19498164
J Hum Evol. 2001 Dec;41(6):631-78
11782112
Archaeology
Biological Evolution
Culture
Fossils
Humans
South Africa
Technology
Tool Use Behavior
PMC4039507
2014
2014
2
5
2014
5
1
2014
5
30
2014
6
1
6
0
2014
6
1
6
0
2015
1
13
6
0
epublish
10.1371/journal.pone.0098359
PONE-D-14-05289
24878544
PMC4039507
24870542
2014
05
29
2014
07
07
2015
07
27
1476-4687
509
7502
2014
May
29
Nature
Nature
A draft map of the human proteome.
575-81
10.1038/nature13302
The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.
Kim
Min-Sik
MS
1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Pinto
Sneha M
SM
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Getnet
Derese
D
1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Adrienne Helis Malvin Medical Research Foundation, New Orleans, Louisiana 70130, USA.
Nirujogi
Raja Sekhar
RS
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Manda
Srikanth S
SS
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Chaerkady
Raghothama
R
1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Madugundu
Anil K
AK
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Kelkar
Dhanashree S
DS
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Isserlin
Ruth
R
The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
Jain
Shobhit
S
The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
Thomas
Joji K
JK
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Muthusamy
Babylakshmi
B
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Leal-Rojas
Pamela
P
1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Pathology, Universidad de La Frontera, Center of Genetic and Immunological Studies-Scientific and Technological Bioresource Nucleus, Temuco 4811230, Chile.
Kumar
Praveen
P
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Sahasrabuddhe
Nandini A
NA
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Balakrishnan
Lavanya
L
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Advani
Jayshree
J
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
George
Bijesh
B
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Renuse
Santosh
S
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Selvan
Lakshmi Dhevi N
LD
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Patil
Arun H
AH
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Nanjappa
Vishalakshi
V
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Radhakrishnan
Aneesha
A
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Prasad
Samarjeet
S
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Subbannayya
Tejaswini
T
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Raju
Rajesh
R
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Kumar
Manish
M
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Sreenivasamurthy
Sreelakshmi K
SK
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Marimuthu
Arivusudar
A
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Sathe
Gajanan J
GJ
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Chavan
Sandip
S
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Datta
Keshava K
KK
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Subbannayya
Yashwanth
Y
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Sahu
Apeksha
A
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Yelamanchi
Soujanya D
SD
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Jayaram
Savita
S
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Rajagopalan
Pavithra
P
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Sharma
Jyoti
J
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Murthy
Krishna R
KR
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Syed
Nazia
N
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Goel
Renu
R
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Khan
Aafaque A
AA
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Ahmad
Sartaj
S
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Dey
Gourav
G
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Mudgal
Keshav
K
School of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
Chatterjee
Aditi
A
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Huang
Tai-Chung
TC
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Zhong
Jun
J
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Wu
Xinyan
X
1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Shaw
Patrick G
PG
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Freed
Donald
D
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Zahari
Muhammad S
MS
Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Mukherjee
Kanchan K
KK
Department of Neurosurgery, Postgraduate Institute of Medical Education & Research, Chandigarh 160012, India.
Shankar
Subramanian
S
Department of Internal Medicine Armed Forces Medical College, Pune 411040, India.
Mahadevan
Anita
A
1] Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India [2] Human Brain Tissue Repository, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India.
Lam
Henry
H
Department of Chemical and Biomolecular Engineering and Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Mitchell
Christopher J
CJ
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Shankar
Susarla Krishna
SK
1] Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India [2] Human Brain Tissue Repository, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India.
Satishchandra
Parthasarathy
P
Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India.
Schroeder
John T
JT
Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21224, USA.
Sirdeshmukh
Ravi
R
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Maitra
Anirban
A
1] The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [2] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA.
Leach
Steven D
SD
1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA.
Drake
Charles G
CG
1] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [2] Departments of Immunology and Urology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA.
Halushka
Marc K
MK
The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA.
Prasad
T S Keshava
TS
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Hruban
Ralph H
RH
1] The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [2] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA.
Kerr
Candace L
CL
1] Department of Obstetrics and Gynecology, Johns Hopkins University School of Medicine Baltimore, Maryland 21205, USA [2] Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
Iacobuzio-Donahue
Christine A
CA
1] The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [2] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [3] Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA.
Gowda
Harsha
H
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India.
Pandey
Akhilesh
A
1] McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [2] Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA [3] Institute of Bioinformatics, International Tech Park, Bangalore 560066, India [4] Adrienne Helis Malvin Medical Research Foundation, New Orleans, Louisiana 70130, USA [5] The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [6] Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA [7] Diana Helis Henry Medical Research Foundation, New Orleans, Louisiana 70130, USA.
eng
HHSN268201000032C
HL
NHLBI NIH HHS
United States
HHSN268201000032C
PHS HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
P41GM103504
GM
NIGMS NIH HHS
United States
T32 GM007814
GM
NIGMS NIH HHS
United States
U24 CA160036
CA
NCI NIH HHS
United States
U24CA160036
CA
NCI NIH HHS
United States
U54 GM103520
GM
NIGMS NIH HHS
United States
U54GM103520
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
England
Nature
0410462
0028-0836
0
Protein Isoforms
0
Protein Sorting Signals
0
Proteome
0
RNA, Untranslated
0
Untranslated Regions
IM
Cell Rep. 2013 Aug 15;4(3):609-20
23933261
Bioinformatics. 2004 Jun 12;20(9):1466-7
14976030
Nat Rev Mol Cell Biol. 2004 Sep;5(9):699-711
15340378
J Proteome Res. 2004 Nov-Dec;3(6):1234-42
15595733
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16249172
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11779829
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24240322
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24364385
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23794635
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11694880
J Proteome Res. 2014 Aug 1;13(8):3854-5
25014353
Nat Methods. 2014 Jul;11(7):709
25110780
Adult
Cells, Cultured
Databases, Protein
Fetus
metabolism
Fourier Analysis
Gene Expression Profiling
Genome, Human
genetics
Hematopoietic Stem Cells
cytology
metabolism
Humans
Internet
Mass Spectrometry
Molecular Sequence Annotation
Open Reading Frames
genetics
Organ Specificity
Protein Biosynthesis
Protein Isoforms
analysis
genetics
metabolism
Protein Sorting Signals
Protein Transport
Proteome
analysis
chemistry
genetics
metabolism
Proteomics
Pseudogenes
genetics
RNA, Untranslated
genetics
Reproducibility of Results
Untranslated Regions
genetics
NIHMS581490
PMC4403737
2013
8
09
2014
3
31
2014
5
30
6
0
2014
5
30
6
0
2014
7
8
6
0
ppublish
nature13302
10.1038/nature13302
24870542
PMC4403737
NIHMS581490
24723613
2014
04
11
2014
04
28
2015
06
01
1095-9203
344
6180
2014
Apr
11
Science (New York, N.Y.)
Science
Mapping the cellular response to small molecules using chemogenomic fitness signatures.
208-11
10.1126/science.1250217
Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes.
Lee
Anna Y
AY
The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
St Onge
Robert P
RP
Proctor
Michael J
MJ
Wallace
Iain M
IM
Nile
Aaron H
AH
Spagnuolo
Paul A
PA
Jitkova
Yulia
Y
Gronda
Marcela
M
Wu
Yan
Y
Kim
Moshe K
MK
Cheung-Ong
Kahlin
K
Torres
Nikko P
NP
Spear
Eric D
ED
Han
Mitchell K L
MK
Schlecht
Ulrich
U
Suresh
Sundari
S
Duby
Geoffrey
G
Heisler
Lawrence E
LE
Surendra
Anuradha
A
Fung
Eula
E
Urbanus
Malene L
ML
Gebbia
Marinella
M
Lissina
Elena
E
Miranda
Molly
M
Chiang
Jennifer H
JH
Aparicio
Ana Maria
AM
Zeghouf
Mahel
M
Davis
Ronald W
RW
Cherfils
Jacqueline
J
Boutry
Marc
M
Kaiser
Chris A
CA
Cummins
Carolyn L
CL
Trimble
William S
WS
Brown
Grant W
GW
Schimmer
Aaron D
AD
Bankaitis
Vytas A
VA
Nislow
Corey
C
Bader
Gary D
GD
Giaever
Guri
G
eng
GM103504
GM
NIGMS NIH HHS
United States
GM44530
GM
NIGMS NIH HHS
United States
MOP-700724
Canadian Institutes of Health Research
Canada
MOP-79368
Canadian Institutes of Health Research
Canada
MOP-81340
Canadian Institutes of Health Research
Canada
P01 HG000205
HG
NHGRI NIH HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
R01 003317-07
PHS HHS
United States
R01 CA157456
CA
NCI NIH HHS
United States
R01 GM044530
GM
NIGMS NIH HHS
United States
R01 HG003317
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
United States
Science
0404511
0036-8075
0
Small Molecule Libraries
IM
J Biol Chem. 2008 Oct 24;283(43):29563-71
18713753
Nat Chem Biol. 2014 Jan;10(1):76-84
24292071
Genetics. 2010 Aug;185(4):1221-33
20457874
Proc Natl Acad Sci U S A. 2010 Aug 17;107(33):14621-6
20679242
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22035796
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22094260
PLoS One. 2012;7(1):e29798
22253786
Hum Mol Genet. 2012 Oct 15;21(R1):R66-71
22914736
Nucleic Acids Res. 2013 Jan;41(Database issue):D816-23
23203989
Assay Drug Dev Technol. 2013 Jun;11(5):299-307
23772551
Nature. 2002 Jul 25;418(6896):387-91
12140549
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14661025
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14718172
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8978672
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Science. 2014 May 23;344(6186):1255771
Cell Line, Tumor
Cells
drug effects
Drug Evaluation, Preclinical
methods
Drug Resistance
genetics
Gene Regulatory Networks
Genome-Wide Association Study
methods
Haploinsufficiency
Humans
Pharmacogenetics
Saccharomyces cerevisiae
drug effects
genetics
Small Molecule Libraries
pharmacology
NIHMS642821
PMC4254748
2014
4
12
6
0
2014
4
12
6
0
2014
4
29
6
0
ppublish
344/6180/208
10.1126/science.1250217
24723613
PMC4254748
NIHMS642821
24722214
2014
04
11
2014
12
30
1932-6203
9
4
2014
PloS one
PLoS ONE
Prediction and experimental characterization of nsSNPs altering human PDZ-binding motifs.
e94507
10.1371/journal.pone.0094507
Single nucleotide polymorphisms (SNPs) are a major contributor to genetic and phenotypic variation within populations. Non-synonymous SNPs (nsSNPs) modify the sequence of proteins and can affect their folding or binding properties. Experimental analysis of all nsSNPs is currently unfeasible and therefore computational predictions of the molecular effect of nsSNPs are helpful to guide experimental investigations. While some nsSNPs can be accurately characterized, for instance if they fall into strongly conserved or well annotated regions, the molecular consequences of many others are more challenging to predict. In particular, nsSNPs affecting less structured, and often less conserved regions, are difficult to characterize. Binding sites that mediate protein-protein or other protein interactions are an important class of functional sites on proteins and can be used to help interpret nsSNPs. Binding sites targeted by the PDZ modular peptide recognition domain have recently been characterized. Here we use this data to show that it is possible to computationally identify nsSNPs in PDZ binding motifs that modify or prevent binding to the proteins containing the motifs. We confirm these predictions by experimentally validating a selected subset with ELISA. Our work also highlights the importance of better characterizing linear motifs in proteins as many of these can be affected by genetic variations.
Gfeller
David
D
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada; Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode, Lausanne, Switzerland.
Ernst
Andreas
A
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Jarvik
Nick
N
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Sidhu
Sachdev S
SS
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada; Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
eng
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2014
04
10
United States
PLoS One
101285081
1932-6203
0
Proteins
IM
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22709956
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21727090
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Nucleic Acids Res. 2008 Jan;36(Database issue):D820-4
17986460
Hum Mutat. 2008 Mar;29(3):361-6
18175334
Sci Signal. 2008;1(35):ra2
18765831
Nat Biotechnol. 2008 Sep;26(9):1041-5
18711339
PLoS Biol. 2008 Sep 30;6(9):e239
18828675
Nucleic Acids Res. 2009 Jan;37(Database issue):D185-90
18978024
Bioinformatics. 2009 Jan 15;25(2):167-74
19017655
Nucleic Acids Res. 2010 Jan;38(Database issue):D167-80
19920119
Nat Methods. 2010 Apr;7(4):248-9
20354512
Nature. 2010 Apr 15;464(7291):993-8
20393554
FEBS Lett. 2012 Aug 14;586(17):2764-72
22710167
Amino Acid Sequence
Binding Sites
Databases, Genetic
Genome, Human
Humans
Models, Statistical
Molecular Sequence Data
PDZ Domains
genetics
Polymorphism, Single Nucleotide
Protein Binding
Proteins
chemistry
genetics
PMC3983204
2014
2013
10
24
2014
3
17
2014
4
10
2014
4
12
6
0
2014
4
12
6
0
2014
12
31
6
0
epublish
10.1371/journal.pone.0094507
PONE-D-13-43489
24722214
PMC3983204
24713437
2014
07
19
2014
10
02
2014
11
11
1367-4811
30
15
2014
Aug
1
Bioinformatics (Oxford, England)
Bioinformatics
HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery.
2230-2
10.1093/bioinformatics/btu172
Correlating disease mutations with clinical and phenotypic information such as drug response or patient survival is an important goal of personalized cancer genomics and a first step in biomarker discovery. HyperModules is a network search algorithm that finds frequently mutated gene modules with significant clinical or phenotypic signatures from biomolecular interaction networks.
HyperModules is available in Cytoscape App Store and as a command line tool at www.baderlab.org/Sofware/HyperModules.
Juri.Reimand@utoronto.ca or Gary.Bader@utoronto.ca
Supplementary data are available at Bioinformatics online.
© The Author 2014. Published by Oxford University Press.
Leung
Alvin
A
The Donnelly Centre, University of Toronto, 160 College Street, M5S 3E1 Toronto, Ontario, Canada.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, 160 College Street, M5S 3E1 Toronto, Ontario, Canada.
Reimand
Jüri
J
The Donnelly Centre, University of Toronto, 160 College Street, M5S 3E1 Toronto, Ontario, Canada.
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
2014
04
08
England
Bioinformatics
9808944
1367-4803
0
Tumor Markers, Biological
IM
Database (Oxford). 2010;2010:baq023
20940177
N Engl J Med. 2009 Apr 23;360(17):1759-68
19369657
Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W452-9
18460544
Mol Syst Biol. 2007;3:140
17940530
Genome Res. 2003 Nov;13(11):2498-504
14597658
Nat Rev Genet. 2011 Jan;12(1):56-68
21164525
Genome Biol. 2012;13(12):R112
23228031
Nat Methods. 2013 Nov;10(11):1108-15
24037242
Sci Rep. 2013;3:2651
24089029
Nat Genet. 2013 Oct;45(10):1113-20
24071849
Nat Methods. 2013 Aug;10(8):723-9
23900255
PLoS Comput Biol. 2013;9(3):e1002975
23555212
Science. 2013 Mar 29;339(6127):1546-58
23539594
Mol Syst Biol. 2013;9:637
23340843
Mol Endocrinol. 2012 Jan;26(1):203-17
22074951
Pac Symp Biocomput. 2012;:55-66
22174262
Nature. 2011 Jun 30;474(7353):609-15
21720365
Neuron. 2011 Jun 9;70(5):898-907
21658583
J Comput Biol. 2011 Mar;18(3):507-22
21385051
Algorithms
Computer Graphics
Gene Regulatory Networks
Genomics
Humans
Internet
Mutation
Neoplasms
genetics
metabolism
Phenotype
Protein Interaction Maps
Software
Systems Biology
methods
Tumor Markers, Biological
genetics
PMC4103591
2014
4
8
2014
4
10
6
0
2014
4
10
6
0
2014
10
3
6
0
ppublish
btu172
10.1093/bioinformatics/btu172
24713437
PMC4103591
24651015
2014
03
21
2014
05
13
2015
07
08
1878-3686
25
3
2014
Mar
17
Cancer cell
Cancer Cell
Genome sequencing of SHH medulloblastoma predicts genotype-related response to smoothened inhibition.
393-405
10.1016/j.ccr.2014.02.004
S1535-6108(14)00073-7
Smoothened (SMO) inhibitors recently entered clinical trials for sonic-hedgehog-driven medulloblastoma (SHH-MB). Clinical response is highly variable. To understand the mechanism(s) of primary resistance and identify pathways cooperating with aberrant SHH signaling, we sequenced and profiled a large cohort of SHH-MBs (n = 133). SHH pathway mutations involved PTCH1 (across all age groups), SUFU (infants, including germline), and SMO (adults). Children >3 years old harbored an excess of downstream MYCN and GLI2 amplifications and frequent TP53 mutations, often in the germline, all of which were rare in infants and adults. Functional assays in different SHH-MB xenograft models demonstrated that SHH-MBs harboring a PTCH1 mutation were responsive to SMO inhibition, whereas tumors harboring an SUFU mutation or MYCN amplification were primarily resistant.
Copyright © 2014 Elsevier Inc. All rights reserved.
Kool
Marcel
M
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany. Electronic address: m.kool@dkfz.de.
Jones
David T W
DT
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Jäger
Natalie
N
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Northcott
Paul A
PA
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Pugh
Trevor J
TJ
Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.
Hovestadt
Volker
V
Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Piro
Rosario M
RM
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Esparza
L Adriana
LA
Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.
Markant
Shirley L
SL
Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.
Remke
Marc
M
The Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada.
Milde
Till
T
Department of Pediatric Oncology, Hematology and Immunology, University Hospital Heidelberg, 69120 Heidelberg, Germany.
Bourdeaut
Franck
F
Institut Curie, 75005 Paris, France; Institut Curie/INSERM U830, 75248 Paris, France.
Ryzhova
Marina
M
Department of Neuropathology, NN Burdenko Neurosurgical Institute, Moscow 125047, Russia.
Sturm
Dominik
D
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Pfaff
Elke
E
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Stark
Sebastian
S
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Hutter
Sonja
S
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Seker-Cin
Huriye
H
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Johann
Pascal
P
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Bender
Sebastian
S
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Schmidt
Christin
C
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Rausch
Tobias
T
European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany.
Shih
David
D
The Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada.
Reimand
Jüri
J
The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
Sieber
Laura
L
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Wittmann
Andrea
A
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Linke
Linda
L
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Witt
Hendrik
H
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany; Department of Pediatric Oncology, Hematology and Immunology, University Hospital Heidelberg, 69120 Heidelberg, Germany.
Weber
Ursula D
UD
Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Zapatka
Marc
M
Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
König
Rainer
R
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany; Integrated Research and Treatment Center, Center for Sepsis Control and Care, Jena University Hospital, 07747 Jena, Germany; Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute (HKI), 07745 Jena, Germany.
Beroukhim
Rameen
R
Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Bergthold
Guillaume
G
Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA; Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA; UMR 8203, CNRS Vectorology and Anticancer Therapeutics, Gustave Roussy Cancer Institute, University Paris XI, 94805 Villejuif Cedex, France.
van Sluis
Peter
P
Department of Oncogenomics, Academic Medical Center, Amsterdam 1105 AZ, the Netherlands.
Volckmann
Richard
R
Department of Oncogenomics, Academic Medical Center, Amsterdam 1105 AZ, the Netherlands.
Koster
Jan
J
Department of Oncogenomics, Academic Medical Center, Amsterdam 1105 AZ, the Netherlands.
Versteeg
Rogier
R
Department of Oncogenomics, Academic Medical Center, Amsterdam 1105 AZ, the Netherlands.
Schmidt
Sabine
S
Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Wolf
Stephan
S
Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Lawerenz
Chris
C
Data Management Facility, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Bartholomae
Cynthia C
CC
Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69121 Heidelberg, Germany.
von Kalle
Christof
C
Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69121 Heidelberg, Germany.
Unterberg
Andreas
A
Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69121 Heidelberg, Germany.
Herold-Mende
Christel
C
Division of Translational Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), 69121 Heidelberg, Germany.
Hofer
Silvia
S
Department of Oncology, University Hospital Zürich, 8006 Zürich, Switzerland.
Kulozik
Andreas E
AE
Department of Pediatric Oncology, Hematology and Immunology, University Hospital Heidelberg, 69120 Heidelberg, Germany.
von Deimling
Andreas
A
Department of Neuropathology, University of Heidelberg, 69120 Heidelberg, Germany; Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Scheurlen
Wolfram
W
Cnopf'sche Kinderklinik, Nürnberg Children's Hospital, 90419 Nürnberg, Germany.
Felsberg
Jörg
J
Department of Neuropathology, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany.
Reifenberger
Guido
G
Department of Neuropathology, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany.
Hasselblatt
Martin
M
Institute for Neuropathology, University Hospital Münster, 48149 Münster, Germany.
Crawford
John R
JR
Departments of Pediatrics and Neurosciences, University of California San Diego, La Jolla, CA 92093; Rady Children's Hospital, San Diego, CA 92123, USA.
Grant
Gerald A
GA
Division of Pediatric Hematology/Oncology, Department of Pediatrics, Duke University Medical Center, Durham, NC 27710, USA; Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA.
Jabado
Nada
N
Departments of Pediatrics and Human Genetics, McGill University Health Centre Research Institute, Montreal, QC H3H 1P3, Canada.
Perry
Arie
A
Departments of Pathology and Laboratory Medicine, University of California, San Francisco, San Francisco, CA 94143, USA.
Cowdrey
Cynthia
C
Departments of Pathology and Neurological Surgery, Brain Tumor Research Center, University of California, San Francisco, San Francisco, CA 94143, USA.
Croul
Sydney
S
Department of Neuropathology, The Arthur and Sonia Labatt Brain Tumour Research Centre, Toronto, ON M5G 1L7, Canada.
Zadeh
Gelareh
G
Department of Neuropathology, The Arthur and Sonia Labatt Brain Tumour Research Centre, Toronto, ON M5G 1L7, Canada.
Korbel
Jan O
JO
European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany.
Doz
Francois
F
Institut Curie, 75005 Paris, France; Université Paris Descartes, 75006 Paris, France.
Delattre
Olivier
O
Institut Curie, 75005 Paris, France; Institut Curie/INSERM U830, 75248 Paris, France.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
McCabe
Martin G
MG
Manchester Academic Health Science Centre, Manchester M13 9NT, UK.
Collins
V Peter
VP
Department of Pathology, University of Cambridge, Cambridge CB2 1QP, UK.
Kieran
Mark W
MW
Pediatric Medical Neuro-Oncology, Dana-Farber Cancer Institute and Boston Children's Hospital, Boston, MA 02215, USA.
Cho
Yoon-Jae
YJ
Department of Neurology and Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA.
Pomeroy
Scott L
SL
Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Witt
Olaf
O
CCU Pediatric Oncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Brors
Benedikt
B
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Taylor
Michael D
MD
The Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON M5G 1L7, Canada.
Schüller
Ulrich
U
Center for Neuropathology and Prion Research, Ludwig-Maximilians-Universität, 81377 München, Germany.
Korshunov
Andrey
A
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany; Department of Neuropathology, University of Heidelberg, 69120 Heidelberg, Germany; Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Eils
Roland
R
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Wechsler-Reya
Robert J
RJ
Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.
Lichter
Peter
P
Division of Molecular Genetics, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany.
Pfister
Stefan M
SM
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69121 Heidelberg, Germany; Department of Pediatric Oncology, Hematology and Immunology, University Hospital Heidelberg, 69120 Heidelberg, Germany.
ICGC PedBrain Tumor Project
eng
GENBANK
GSE49243
GSE49377
GSE49576
K08 NS075144
NS
NINDS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
United States
Cancer Cell
101130617
1535-6108
0
Biphenyl Compounds
0
GLI2 protein, human
0
Hedgehog Proteins
0
Kruppel-Like Transcription Factors
0
LDE225
0
MYCN protein, human
0
Nuclear Proteins
0
Oncogene Proteins
0
Pyridines
0
Receptors, Cell Surface
0
Receptors, G-Protein-Coupled
0
Repressor Proteins
0
SHH protein, human
0
SMO protein, human
0
SUFU protein, human
0
TP53 protein, human
0
Tumor Suppressor Protein p53
0
patched receptors
EC 2.7.1.-
Phosphatidylinositol 3-Kinases
EC 2.7.11.1
Proto-Oncogene Proteins c-akt
EC 2.7.7.49
TERT protein, human
EC 2.7.7.49
Telomerase
EC 3.6.1.-
DDX3X protein, human
EC 3.6.4.13
DEAD-box RNA Helicases
IM
EMBO J. 2014 Sep 17;33(18):1984-6
25061226
Adolescent
Adult
Animals
Base Sequence
Biphenyl Compounds
therapeutic use
Cerebellar Neoplasms
drug therapy
genetics
Child
Child, Preschool
DEAD-box RNA Helicases
genetics
DNA Copy Number Variations
genetics
Drug Resistance, Neoplasm
genetics
Female
Gene Expression Profiling
Hedgehog Proteins
genetics
High-Throughput Nucleotide Sequencing
Humans
Infant
Kruppel-Like Transcription Factors
genetics
Male
Medulloblastoma
drug therapy
genetics
Mice
Mice, Inbred NOD
Mice, SCID
Molecular Sequence Data
Neoplasm Transplantation
Nuclear Proteins
genetics
Oncogene Proteins
genetics
Phosphatidylinositol 3-Kinases
genetics
metabolism
Promoter Regions, Genetic
genetics
Proto-Oncogene Proteins c-akt
genetics
metabolism
Pyridines
therapeutic use
Receptors, Cell Surface
genetics
Receptors, G-Protein-Coupled
antagonists & inhibitors
genetics
Repressor Proteins
genetics
Signal Transduction
genetics
Telomerase
genetics
Tumor Suppressor Protein p53
genetics
Young Adult
NIHMS628780
PMC4493053
2013
8
5
2013
11
19
2014
2
13
2014
3
22
6
0
2014
3
22
6
0
2014
5
14
6
0
ppublish
S1535-6108(14)00073-7
10.1016/j.ccr.2014.02.004
24651015
PMC4493053
NIHMS628780
24596128
2014
04
15
2014
12
09
2015
01
27
1615-9861
14
9
2014
May
Proteomics
Proteomics
Highlights of B/D-HPP and HPP Resource Pillar Workshops at 12th Annual HUPO World Congress of Proteomics: September 14-18, 2013, Yokohama, Japan.
975-88
10.1002/pmic.201400041
At the 12th Annual HUPO World Congress of Proteomics in Japan, the Human Proteome Project (HPP) presented 16 scientific workshop sessions. Here we summarize highlights of ten workshops from the Biology and Disease-driven HPP (B/D-HPP) teams and three from the HPP Resource Pillars. Highlights of the three Chromosome-centric HPP sessions appeared in the many articles of the 2014 C-HPP special issue of the Journal of Proteome Research .
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Aebersold
Ruedi
R
Institute of Molecular Systems Biology, Seattle, WA, USA.
Bader
Gary D
GD
Edwards
Aled M
AM
van Eyk
Jennifer
J
Kussman
Martin
M
Qin
Jun
J
Omenn
Gilbert S
GS
eng
092809
Wellcome Trust
United Kingdom
Journal Article
Germany
Proteomics
101092707
1615-9853
IM
Computational Biology
Databases, Protein
Humans
Japan
Proteomics
Bioinformatics
Biology and Disease-driven Human Proteome Project
HUPO-2013 Yokohama Congress
Human Proteome Project
Organ and biofluid proteomes
ProteomeAnalyzer
2014
1
31
2014
2
20
2014
3
6
6
0
2014
3
7
6
0
2014
12
15
6
0
ppublish
24596128
10.1002/pmic.201400041
24553142
2014
02
27
2014
03
13
2015
05
01
1476-4687
506
7489
2014
Feb
27
Nature
Nature
Epigenomic alterations define lethal CIMP-positive ependymomas of infancy.
445-50
10.1038/nature13108
Ependymomas are common childhood brain tumours that occur throughout the nervous system, but are most common in the paediatric hindbrain. Current standard therapy comprises surgery and radiation, but not cytotoxic chemotherapy as it does not further increase survival. Whole-genome and whole-exome sequencing of 47 hindbrain ependymomas reveals an extremely low mutation rate, and zero significant recurrent somatic single nucleotide variants. Although devoid of recurrent single nucleotide variants and focal copy number aberrations, poor-prognosis hindbrain ependymomas exhibit a CpG island methylator phenotype. Transcriptional silencing driven by CpG methylation converges exclusively on targets of the Polycomb repressive complex 2 which represses expression of differentiation genes through trimethylation of H3K27. CpG island methylator phenotype-positive hindbrain ependymomas are responsive to clinical drugs that target either DNA or H3K27 methylation both in vitro and in vivo. We conclude that epigenetic modifiers are the first rational therapeutic candidates for this deadly malignancy, which is epigenetically deregulated but genetically bland.
Mack
S C
SC
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada [4].
Witt
H
H
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [3] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [4].
Piro
R M
RM
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Gu
L
L
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Zuyderduyn
S
S
Department of Molecular Genetics, Banting and Best Department of Medical Research, The Donnelly Centre, University of Toronto, Toronto, Ontario M4N 1X8, Canada.
Stütz
A M
AM
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Genome Biology, European Molecular Biology, Laboratory Meyerhofstr. 1, Heidelberg 69117, Germany.
Wang
X
X
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Gallo
M
M
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Garzia
L
L
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Zayne
K
K
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Zhang
X
X
Department of Genetics, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, New Hampshire 03756, USA.
Ramaswamy
V
V
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Jäger
N
N
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Jones
D T W
DT
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Sill
M
M
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Pugh
T J
TJ
Department of Neurology, Harvard Medical School, Children's Hospital Boston, MIT, Boston, Massachusetts 02115, USA.
Ryzhova
M
M
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Wani
K M
KM
Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Shih
D J H
DJ
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Head
R
R
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Remke
M
M
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Bailey
S D
SD
1] Ontario Cancer Institute, Princess Margaret Cancer Centre-University Health Network, Toronto, Ontario M5G 1L7, Canada [2] Ontario Institute for Cancer Research, Toronto, Ontario M5G 1L7, Canada.
Zichner
T
T
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Genome Biology, European Molecular Biology, Laboratory Meyerhofstr. 1, Heidelberg 69117, Germany.
Faria
C C
CC
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Barszczyk
M
M
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Stark
S
S
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Seker-Cin
H
H
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Hutter
S
S
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Johann
P
P
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Bender
S
S
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Hovestadt
V
V
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Tzaridis
T
T
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Dubuc
A M
AM
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Northcott
P A
PA
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Peacock
J
J
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Bertrand
K C
KC
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Agnihotri
S
S
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Cavalli
F M G
FM
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Clarke
I
I
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Nethery-Brokx
K
K
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Creasy
C L
CL
Cancer Epigenetics Discovery Performance Unit, GlaxoSmithKline Pharmaceuticals, Collegeville, Pennsylvania 19426, USA.
Verma
S K
SK
Cancer Epigenetics Discovery Performance Unit, GlaxoSmithKline Pharmaceuticals, Collegeville, Pennsylvania 19426, USA.
Koster
J
J
Department of Oncogenomics, Academic Medical Center, Amsterdam 1105, The Netherlands.
Wu
X
X
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Yao
Y
Y
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Milde
T
T
1] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [3] CCU Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Sin-Chan
P
P
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Zuccaro
J
J
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Lau
L
L
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Pereira
S
S
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Castelo-Branco
P
P
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Hirst
M
M
1] Centre for High-Throughput Biology, Department of Microbiology & Immunology, University of British Columbia, Vancouver, V6T 1Z4 British Columbia, Canada [2] Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada.
Marra
M A
MA
1] Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada [2] Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia V6H 3N1, Canada.
Roberts
S S
SS
Department of Pediatrics and National Capital Consortium, Uniformed Services University, Bethesda, Maryland 20814, USA.
Fults
D
D
Department of Neurosurgery, University of Utah School of Medicine, Salt Lake City, Utah 84132, USA.
Massimi
L
L
Pediatric Neurosurgery, Catholic University Medical School, Gemelli Hospital, Rome 00168, Italy.
Cho
Y J
YJ
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California 94305, USA.
Van Meter
T
T
Department of Pediatrics, Virginia Commonwealth, Richmond, Virginia 23298-0646, USA.
Grajkowska
W
W
Department of Pathology, University of Warsaw, Children's Memorial Health Institute University of Warsaw, Warsaw 04-730, Poland.
Lach
B
B
Division of Anatomical Pathology, Department of Pathology and Molecular Medicine, McMaster University, Hamilton General Hospital, Hamilton, Ontario L8S 4K1, Canada.
Kulozik
A E
AE
1] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
von Deimling
A
A
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Department of Neuropathology Ruprecht-Karls-University Heidelberg, Institute of Pathology, Heidelberg 69120, Germany.
Witt
O
O
1] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [3] CCU Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Scherer
S W
SW
Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Fan
X
X
1] University of Michigan Cell and Developmental Biology, Ann Arbor, Michigan 48109-2200, USA [2] Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA.
Muraszko
K M
KM
Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA.
Kool
M
M
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Pomeroy
S L
SL
Department of Neurology, Harvard Medical School, Children's Hospital Boston, MIT, Boston, Massachusetts 02115, USA.
Gupta
N
N
Department of Neurosurgery, University of California San Francisco, San Francisco, California 94143-0112, USA.
Phillips
J
J
Departments of Neurology, Pediatrics, and Neurosurgery, University of California, San Francisco, The Helen Diller Family Cancer Research Building, San Francisco, California 94158, USA.
Huang
A
A
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Department of Neuro-oncology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada.
Tabori
U
U
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Department of Neuro-oncology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada.
Hawkins
C
C
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Malkin
D
D
Department of Haematology and Oncology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada.
Kongkham
P N
PN
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Weiss
W A
WA
Departments of Neurology, Pediatrics, and Neurosurgery, University of California, San Francisco, The Helen Diller Family Cancer Research Building, San Francisco, California 94158, USA.
Jabado
N
N
Departments of Pediatrics and Human Genetics, McGill University and the McGill University Health Center Research Institute, Montreal, Quebec H3Z 2Z3, Canada.
Rutka
J T
JT
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Bouffet
E
E
Department of Neuro-oncology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada.
Korbel
J O
JO
Genome Biology, European Molecular Biology, Laboratory Meyerhofstr. 1, Heidelberg 69117, Germany.
Lupien
M
M
1] Ontario Cancer Institute, Princess Margaret Cancer Centre-University Health Network, Toronto, Ontario M5G 1L7, Canada [2] Ontario Institute for Cancer Research, Toronto, Ontario M5G 1L7, Canada [3] Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1X8, Canada.
Aldape
K D
KD
Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Bader
G D
GD
Department of Molecular Genetics, Banting and Best Department of Medical Research, The Donnelly Centre, University of Toronto, Toronto, Ontario M4N 1X8, Canada.
Eils
R
R
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Lichter
P
P
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Dirks
P B
PB
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada [4] Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
Pfister
S M
SM
1] Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany [2] Department of Pediatric Oncology, Hematology and Immunology, University of Heidelberg, Heidelberg 69120, Germany [3] German Cancer Consortium (DKTK), Heidelberg 69120, Germany.
Korshunov
A
A
1] German Cancer Consortium (DKTK), Heidelberg 69120, Germany [2] University of Michigan Cell and Developmental Biology, Ann Arbor, Michigan 48109-2200, USA [3] CCU Neuropathology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany.
Taylor
M D
MD
1] Developmental & Stem Cell Biology Program, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada [2] Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada [3] Division of Neurosurgery, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
eng
GEO
GSE43353
P30 CA016672
CA
NCI NIH HHS
United States
P50 CA097257
CA
NCI NIH HHS
United States
R01 CA121941
CA
NCI NIH HHS
United States
R01 CA148621
CA
NCI NIH HHS
United States
R01 CA163737
CA
NCI NIH HHS
United States
R01CA148699
CA
NCI NIH HHS
United States
R01CA159859
CA
NCI NIH HHS
United States
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2014
02
19
England
Nature
0410462
0028-0836
0
Histones
EC 2.1.1.43
Polycomb Repressive Complex 2
IM
Nature. 2012 Sep 27;489(7417):519-25
22960745
Nature. 2012 Oct 4;490(7418):61-70
23000897
Sci Transl Med. 2012 Oct 17;4(156):156ra140
23076357
Nature. 2012 Dec 6;492(7427):108-12
23051747
Nat Genet. 2013 Jan;45(1):12-7
23202128
Cancer Res. 2013 Jan 1;73(1):417-27
23108137
Nat Genet. 2013 Mar;45(3):279-84
23334666
Nature. 2013 Jul 11;499(7457):214-8
23770567
Cell. 2013 Oct 10;155(2):462-77
24120142
Nature. 2012 Aug 2;488(7409):49-56
22832581
Childs Nerv Syst. 1999 Oct;15(10):563-70
10550587
Nature. 1998 Jul 9;394(6689):203-6
9671307
Proc Natl Acad Sci U S A. 1999 Jul 20;96(15):8681-6
10411935
J Clin Oncol. 2005 Oct 1;23(28):7043-9
16192589
Cancer Cell. 2005 Oct;8(4):323-35
16226707
Cancer. 2006 Apr 15;106(8):1794-803
16532500
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Acta Neuropathol. 2012 May;123(5):727-38
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Int J Radiat Oncol Biol Phys. 2012 Aug 1;83(5):1541-8
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22759861
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22820256
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22980975
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22941188
Nature. 2014 Feb 27;506(7489):438-9
24553138
Animals
Brain Neoplasms
drug therapy
genetics
CpG Islands
genetics
DNA Methylation
drug effects
Embryonic Stem Cells
metabolism
Ependymoma
drug therapy
genetics
Epigenesis, Genetic
genetics
Epigenomics
Female
Gene Expression Regulation, Neoplastic
Gene Silencing
drug effects
Histones
drug effects
metabolism
Humans
Infant
Mice
Mice, Inbred NOD
Mice, SCID
Mutation
genetics
Phenotype
Polycomb Repressive Complex 2
metabolism
Prognosis
Rhombencephalon
pathology
Xenograft Model Antitumor Assays
NIHMS576734
PMC4174313
2013
3
10
2014
1
28
2014
2
19
2014
2
21
6
0
2014
2
21
6
0
2014
3
14
6
0
ppublish
nature13108
10.1038/nature13108
24553142
PMC4174313
NIHMS576734
24391925
2014
01
06
2014
09
02
2014
11
11
1932-6203
8
12
2013
PloS one
PLoS ONE
Coordinate microRNA-mediated regulation of protein complexes in prostate cancer.
e84261
10.1371/journal.pone.0084261
MicroRNAs are a class of small non-coding regulatory RNA molecules that regulate mRNAs post-transcriptionally. Recent evidence has shown that miRNAs target entire functionally related proteins such as protein complexes and biological pathways. However, characterizing the influence of miRNAs on genes whose encoded proteins are part of protein complexes has not been studied in the context of disease. We propose an entropy-based framework to identify miRNA-mediated dysregulation of functionally related proteins during prostate cancer progression. The proposed framework uses experimentally verified miRNA-target interactions, functionally related proteins and expression data to identify miRNA-influenced protein complexes in prostate cancer, and identify genes that are dysregulated as a result. The framework constructs correlation matrixes between functionally related proteins and miRNAs that have targets in the complex, and assesses the changes in the Shannon entropy of the modules across different stages of prostate cancer. Results reveal that SMAD4 and HDAC containing protein complexes are highly affected and disrupted by miRNAs, particularly miRNA-1 and miRNA-16. Using biological pathways to define functionally related proteins reveals that NF-kB-, RAS-, and Syndecan-mediated pathways are dysregulated due to miRNA-1- and miRNA-16-mediated regulation. These results suggest that miRNA-1 and miRNA-16 are important master regulators of miRNA-mediated regulation in prostate cancer. Moreover, results reveal that miRNAs with high-influence on the disrupted protein complexes are diagnostic and prognostic biomarker candidates for prostate cancer progression. The observation of miRNA-mediated protein complex regulation and miRNA-mediated pathway regulation, with partial experimental verification from previous studies, demonstrates that our framework is a promising approach for the identification of novel miRNAs and protein complexes related to disease progression.
Alshalalfa
Mohammed
M
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada ; Biotechnology Research Centre, Palestine Polytechnic University, Hebron, Palestine.
Bader
Gary D
GD
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada and the Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
Bismar
Tarek A
TA
Departments of Pathology, Oncology and Molecular Biology and Biochemistry, Faculty of Medicine, University of Calgary, Alberta, Canada.
Alhajj
Reda
R
Department of Computer Science, University of Calgary, Calgary, Alberta, Canada.
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2013
12
31
United States
PLoS One
101285081
1932-6203
0
MIRN1 microRNA, human
0
MIRN16 microRNA, human
0
MicroRNAs
0
Multiprotein Complexes
0
SMAD4 protein, human
0
Smad4 Protein
0
Tumor Markers, Biological
EC 3.5.1.98
HDAC1 protein, human
EC 3.5.1.98
Histone Deacetylase 1
IM
Mol Cancer Ther. 2007 May;6(5):1483-91
17483436
Cancer Res. 2007 Jul 1;67(13):6130-5
17616669
RNA. 2007 Sep;13(9):1402-8
17652130
Nat Methods. 2007 Dec;4(12):1045-9
18026111
Nucleic Acids Res. 2008 Jan;36(Database issue):D646-50
17965090
Nucleic Acids Res. 2008 Jan;36(Database issue):D154-8
17991681
Oncogene. 2008 Mar 13;27(12):1788-93
17891175
Proteomics. 2008 May;8(10):1975-9
18491312
Nucleic Acids Res. 2009 Jan;37(Database issue):D105-10
18996891
PLoS One. 2011;6(3):e17911
21445301
Biosystems. 2011 Sep;105(3):201-9
21524683
BMC Syst Biol. 2011;5:136
21867514
Mol Cancer Ther. 2011 Oct;10(10):1857-66
21768329
Cell. 2011 Oct 14;147(2):370-81
22000015
BMC Syst Biol. 2011;5:183
22050994
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21400514
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22180412
Oncogene. 2012 Feb 23;31(8):978-91
21765474
Nucleic Acids Res. 2012 Apr;40(8):3689-703
22210864
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15056224
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15254046
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16199517
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16286247
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16557279
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19029799
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19628766
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19956180
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19676045
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20087440
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20233430
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21085593
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20473869
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21071411
Nature. 2011 Feb 10;470(7333):269-73
21289624
Entropy
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
genetics
physiology
Histone Deacetylase 1
metabolism
Humans
Male
MicroRNAs
metabolism
Multiprotein Complexes
metabolism
Prostatic Neoplasms
metabolism
Signal Transduction
genetics
physiology
Smad4 Protein
metabolism
Tumor Markers, Biological
metabolism
PMC3877262
2013
2013
5
6
2013
11
21
2013
12
31
2014
1
7
6
0
2014
1
7
6
0
2014
9
3
6
0
epublish
10.1371/journal.pone.0084261
PONE-D-13-18403
24391925
PMC3877262
24089029
2013
10
03
2045-2322
3
2013
Scientific reports
Sci Rep
The mutational landscape of phosphorylation signaling in cancer.
2651
10.1038/srep02651
Somatic mutations in cancer genomes include drivers that provide selective advantages to tumor cells and passengers present due to genome instability. Discovery of pan-cancer drivers will help characterize biological systems important in multiple cancers and lead to development of better therapies. Driver genes are most often identified by their recurrent mutations across tumor samples. However, some mutations are more important for protein function than others. Thus considering the location of mutations with respect to functional protein sites can predict their mechanisms of action and improve the sensitivity of driver gene detection. Protein phosphorylation is a post-translational modification central to cancer biology and treatment, and frequently altered by driver mutations. Here we used our ActiveDriver method to analyze known phosphorylation sites mutated by single nucleotide variants (SNVs) in The Cancer Genome Atlas Research Network (TCGA) pan-cancer dataset of 3,185 genomes and 12 cancer types. Phosphorylation-related SNVs (pSNVs) occur in ~90% of tumors, show increased conservation and functional mutation impact compared to other protein-coding mutations, and are enriched in cancer genes and pathways. Gene-centric analysis found 150 known and candidate cancer genes with significant pSNV recurrence. Using a novel computational method, we predict that 29% of these mutations directly abolish phosphorylation or modify kinase target sites to rewire signaling pathways. This analysis shows that incorporation of information about protein signaling sites will improve computational pipelines for variant function prediction.
Reimand
Jüri
J
The Donnelly Centre, University of Toronto, Canada.
Wagih
Omar
O
Bader
Gary D
GD
eng
GM103504
GM
NIGMS NIH HHS
United States
MOP-84324
Canadian Institutes of Health Research
Canada
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 RR031228
RR
NCRR NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2013
10
02
England
Sci Rep
101563288
2045-2322
IM
Nucleic Acids Res. 2010 Sep;38(16):e164
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Sci Rep. 2013;3:2650
24084849
PMC3788619
2013
7
17
2013
8
23
2013
10
4
6
0
2013
10
4
6
0
2013
10
4
6
0
epublish
srep02651
10.1038/srep02651
24089029
PMC3788619
24084849
2013
10
02
2014
07
06
2014
11
12
2045-2322
3
2013
Scientific reports
Sci Rep
Comprehensive identification of mutational cancer driver genes across 12 tumor types.
2650
10.1038/srep02650
With the ability to fully sequence tumor genomes/exomes, the quest for cancer driver genes can now be undertaken in an unbiased manner. However, obtaining a complete catalog of cancer genes is difficult due to the heterogeneous molecular nature of the disease and the limitations of available computational methods. Here we show that the combination of complementary methods allows identifying a comprehensive and reliable list of cancer driver genes. We provide a list of 291 high-confidence cancer driver genes acting on 3,205 tumors from 12 different cancer types. Among those genes, some have not been previously identified as cancer drivers and 16 have clear preference to sustain mutations in one specific tumor type. The novel driver candidates complement our current picture of the emergence of these diseases. In summary, the catalog of driver genes and the methodology presented here open new avenues to better understand the mechanisms of tumorigenesis.
Tamborero
David
D
1] Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, Barcelona, Spain [2].
Gonzalez-Perez
Abel
A
Perez-Llamas
Christian
C
Deu-Pons
Jordi
J
Kandoth
Cyriac
C
Reimand
Jüri
J
Lawrence
Michael S
MS
Getz
Gad
G
Bader
Gary D
GD
Ding
Li
L
Lopez-Bigas
Nuria
N
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
U01 HG006517
HG
NHGRI NIH HHS
United States
U54 HG003079
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2013
10
02
England
Sci Rep
101563288
2045-2322
0
Carcinogens
IM
Nature. 2013 Jul 11;499(7457):214-8
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Carcinogens
Cell Transformation, Neoplastic
genetics
metabolism
DNA Mutational Analysis
methods
Gene Regulatory Networks
Genetic Association Studies
Genomics
methods
Humans
Mutation
Neoplasms
genetics
metabolism
Reproducibility of Results
Signal Transduction
PMC3788361
2013
6
27
2013
8
23
2013
10
3
6
0
2013
10
3
6
0
2014
7
7
6
0
epublish
srep02650
10.1038/srep02650
24084849
PMC3788361
24068901
2013
09
26
2014
04
21
2014
11
12
1553-7358
9
9
2013
PLoS computational biology
PLoS Comput. Biol.
Using biological pathway data with paxtools.
e1003194
10.1371/journal.pcbi.1003194
A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools.
Demir
Emek
E
Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
Babur
Ozgün
O
Rodchenkov
Igor
I
Aksoy
Bülent Arman
BA
Fukuda
Ken I
KI
Gross
Benjamin
B
Sümer
Onur Selçuk
OS
Bader
Gary D
GD
Sander
Chris
C
eng
5U41 HG006623-02
HG
NHGRI NIH HHS
United States
U41 HG006623
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
2013
09
19
United States
PLoS Comput Biol
101238922
1553-734X
IM
Nucleic Acids Res. 2009 Jul;37(Web Server issue):W305-11
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BMC Syst Biol. 2010;4:92
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PLoS Biol. 2010;8(8). pii: e1000472. doi: 10.1371/journal.pbio.1000472
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Algorithms
Computational Biology
methods
Programming Languages
PMC3777916
2013
9
2013
1
21
2013
6
25
2013
9
19
2013
9
27
6
0
2013
9
27
6
0
2014
4
22
6
0
ppublish
10.1371/journal.pcbi.1003194
PCOMPBIOL-D-13-00124
24068901
PMC3777916
23918249
2013
10
04
2014
04
07
2014
11
13
1367-4811
29
20
2013
Oct
15
Bioinformatics (Oxford, England)
Bioinformatics
The BioPAX Validator.
2659-60
10.1093/bioinformatics/btt452
BioPAX is a community-developed standard language for biological pathway data. A key functionality required for efficient BioPAX data exchange is validation-detecting errors and inconsistencies in BioPAX documents. The BioPAX Validator is a command-line tool, Java library and online web service for BioPAX that performs >100 classes of consistency checks.
The validator recognizes common syntactic errors and semantic inconsistencies and reports them in a customizable human readable format. It can also automatically fix some errors and normalize BioPAX data. Since its release, the validator has become a critical tool for the pathway informatics community, detecting thousands of errors and helping substantially increase the conformity and uniformity of BioPAX-formatted data. The BioPAX Validator is open source and released under LGPL v3 license. All sources, binaries and documentation can be found at sf.net/p/biopax, and the latest stable version of the web application is available at biopax.org/validator.
Rodchenkov
Igor
I
The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, USA.
Demir
Emek
E
Sander
Chris
C
Bader
Gary D
GD
eng
1U41HG006623
HG
NHGRI NIH HHS
United States
U41 HG006623
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
2013
08
05
England
Bioinformatics
9808944
1367-4803
IM
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6
16381921
Bioinformatics. 2008 Mar 15;24(6):880-1
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Proteomics. 2009 Nov;9(22):5112-9
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Internet
Software
PMC3789551
2013
8
5
2013
8
29
2013
8
7
6
0
2013
8
7
6
0
2014
4
8
6
0
ppublish
btt452
10.1093/bioinformatics/btt452
23918249
PMC3789551
23900255
2013
07
31
2013
12
11
2014
11
13
1548-7105
10
8
2013
Aug
Nature methods
Nat. Methods
Computational approaches to identify functional genetic variants in cancer genomes.
723-9
10.1038/nmeth.2562
The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.
Gonzalez-Perez
Abel
A
Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain.
Mustonen
Ville
V
Reva
Boris
B
Ritchie
Graham R S
GR
Creixell
Pau
P
Karchin
Rachel
R
Vazquez
Miguel
M
Fink
J Lynn
JL
Kassahn
Karin S
KS
Pearson
John V
JV
Bader
Gary D
GD
Boutros
Paul C
PC
0000000305537520
Muthuswamy
Lakshmi
L
Ouellette
B F Francis
BF
Reimand
Jüri
J
Linding
Rune
R
0000000304204839
Shibata
Tatsuhiro
T
Valencia
Alfonso
A
Butler
Adam
A
Dronov
Serge
S
Flicek
Paul
P
Shannon
Nick B
NB
Carter
Hannah
H
Ding
Li
L
Sander
Chris
C
Stuart
Josh M
JM
Stein
Lincoln D
LD
Lopez-Bigas
Nuria
N
International Cancer Genome Consortium Mutation Pathways and Consequences Subgroup of the Bioinformatics Analyses Working Group
eng
095908
Wellcome Trust
United Kingdom
R01 CA180778
CA
NCI NIH HHS
United States
U01 HG006517
HG
NHGRI NIH HHS
United States
U54 HG003079
HG
NHGRI NIH HHS
United States
Journal Article
United States
Nat Methods
101215604
1548-7091
IM
Mol Syst Biol. 2013;9:637
23340843
Nucleic Acids Res. 2012 Nov;40(21):e169
22904074
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23348506
Bioinformatics. 2013 Mar 1;29(5):647-8
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BMC Genomics. 2013;14 Suppl 3:S3
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Computational Biology
methods
Genetic Variation
Genome, Human
Humans
Mutation
Neoplasms
genetics
NIHMS543406
PMC3919555
2013
1
25
2013
6
07
2013
8
1
6
0
2013
8
1
6
0
2013
12
16
6
0
ppublish
nmeth.2562
10.1038/nmeth.2562
23900255
PMC3919555
NIHMS543406
23794635
2013
06
24
2013
09
06
2014
11
13
1362-4962
41
Web Server issue
2013
Jul
Nucleic acids research
Nucleic Acids Res.
GeneMANIA prediction server 2013 update.
W115-22
10.1093/nar/gkt533
GeneMANIA (http://www.genemania.org) is a flexible user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query gene list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. GeneMANIA can also be used in a function prediction setting: given a query gene, GeneMANIA finds a small set of genes that are most likely to share function with that gene based on their interactions with it. Enriched Gene Ontology categories among this set can sometimes point to the function of the gene. Seven organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens, Rattus norvegicus and Saccharomyces cerevisiae), and hundreds of data sets have been collected from GEO, BioGRID, IRefIndex and I2D, as well as organism-specific functional genomics data sets. Users can customize their search by selecting specific data sets to query and by uploading their own data sets to analyze.
Zuberi
Khalid
K
The Donnelly Centre, University of Toronto, Ontario, Canada.
Franz
Max
M
Rodriguez
Harold
H
Montojo
Jason
J
Lopes
Christian Tannus
CT
Bader
Gary D
GD
Morris
Quaid
Q
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
England
Nucleic Acids Res
0411011
0305-1048
IM
Nucleic Acids Res. 2012 Jan;40(Database issue):D306-12
22096229
Nucleic Acids Res. 2012 Jan;40(Database issue):D857-61
22096227
Nucleic Acids Res. 2012 Jan;40(Database issue):D700-5
22110037
Proteomics. 2012 May;12(10):1687-96
22589215
Nucleic Acids Res. 2012 Jul;40(Web Server issue):W484-90
22684505
Plant J. 2012 Sep;71(6):1038-50
22607031
Nucleic Acids Res. 2013 Jan;41(Database issue):D808-15
23203871
Nucleic Acids Res. 2013 Jan;41(Database issue):D816-23
23203989
PLoS One. 2012;7(12):e51947
23284828
Genome Biol. 2012;13(12):R125
23268829
Nucleic Acids Res. 2002 Jan 1;30(1):207-10
11752295
Genome Biol. 2005;6(13):R114
16420673
Genome Biol. 2008;9 Suppl 1:S2
18613946
Genome Biol. 2008;9 Suppl 1:S4
18613948
Curr Protoc Bioinformatics. 2008 Sep;Chapter 9:Unit 9.11
18819079
BMC Bioinformatics. 2008;9:405
18823568
Nat Protoc. 2009;4(1):44-57
19131956
Bioinformatics. 2010 Jan 1;26(1):111-9
19850753
Genome Biol. 2009;10(11):R130
19919682
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W214-20
20576703
Bioinformatics. 2010 Jul 15;26(14):1806-7
20495000
Bioinformatics. 2010 Jul 15;26(14):1759-65
20507895
Genome Biol. 2010;11(5):R53
20482850
Bioinformatics. 2010 Sep 15;26(18):2347-8
20656902
Bioinformatics. 2010 Nov 15;26(22):2927-8
20926419
PLoS One. 2010;5(11):e13984
21085593
PLoS One. 2011;6(2):e17258
21364756
Bioinformatics. 2011 Jun 15;27(12):1739-40
21546393
Bioinformatics. 2011 Jul 1;27(13):1882-3
21561920
Nat Methods. 2011 Jul;8(7):528-9
21716279
Genome Res. 2011 Jul;21(7):1109-21
21536720
Methods Mol Biol. 2011;781:399-414
21877293
Nucleic Acids Res. 2011 Nov 1;39(20):8677-88
21785136
Nucleic Acids Res. 2012 Jan;40(Database issue):D735-41
22067452
Nucleic Acids Res. 2012 Jan;40(Database issue):D821-8
22110034
Algorithms
Animals
Gene Regulatory Networks
Genes
Humans
Internet
Mice
Rats
Software
PMC3692113
2013
6
25
6
0
2013
6
26
6
0
2013
9
7
6
0
ppublish
gkt533
10.1093/nar/gkt533
23794635
PMC3692113
23595664
2013
05
16
2013
11
04
2014
11
16
1367-4811
29
10
2013
May
15
Bioinformatics (Oxford, England)
Bioinformatics
Cytoscape app store.
1350-1
10.1093/bioinformatics/btt138
Cytoscape is an open source software tool for biological network visualization and analysis, which can be extended with independently developed apps. We launched the Cytoscape App Store to highlight the important features that apps add to Cytoscape, enable researchers to find and install apps they need and help developers promote their apps.
The App Store is available at http://apps.cytoscape.org.
apico@gladstone.ucsf.edu.
Lotia
Samad
S
Gladstone Institutes, San Francisco, CA 94158, USA.
Montojo
Jason
J
Dong
Yue
Y
Bader
Gary D
GD
Pico
Alexander R
AR
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2013
04
16
England
Bioinformatics
9808944
1367-4803
IM
Genome Res. 2003 Nov;13(11):2498-504
14597658
Bioinformatics. 2009 Apr 15;25(8):1091-3
19237447
Nat Protoc. 2007;2(10):2366-82
17947979
Computational Biology
instrumentation
Internet
Metabolic Networks and Pathways
Software
PMC3654709
2013
4
16
2013
4
19
6
0
2013
4
19
6
0
2013
11
5
6
0
ppublish
btt138
10.1093/bioinformatics/btt138
23595664
PMC3654709
23549480
2013
04
03
2013
09
13
2014
11
16
1744-4292
9
2013
Molecular systems biology
Mol. Syst. Biol.
SH3 interactome conserves general function over specific form.
652
10.1038/msb.2013.9
Src homology 3 (SH3) domains bind peptides to mediate protein-protein interactions that assemble and regulate dynamic biological processes. We surveyed the repertoire of SH3 binding specificity using peptide phage display in a metazoan, the worm Caenorhabditis elegans, and discovered that it structurally mirrors that of the budding yeast Saccharomyces cerevisiae. We then mapped the worm SH3 interactome using stringent yeast two-hybrid and compared it with the equivalent map for yeast. We found that the worm SH3 interactome resembles the analogous yeast network because it is significantly enriched for proteins with roles in endocytosis. Nevertheless, orthologous SH3 domain-mediated interactions are highly rewired. Our results suggest a model of network evolution where general function of the SH3 domain network is conserved over its specific form.
Xin
Xiaofeng
X
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Gfeller
David
D
Cheng
Jackie
J
Tonikian
Raffi
R
Sun
Lin
L
Guo
Ailan
A
Lopez
Lianet
L
Pavlenco
Alevtina
A
Akintobi
Adenrele
A
Zhang
Yingnan
Y
Rual
Jean-François
JF
Currell
Bridget
B
Seshagiri
Somasekar
S
Hao
Tong
T
Yang
Xinping
X
Shen
Yun A
YA
Salehi-Ashtiani
Kourosh
K
Li
Jingjing
J
Cheng
Aaron T
AT
Bouamalay
Dryden
D
Lugari
Adrien
A
Hill
David E
DE
Grimes
Mark L
ML
Drubin
David G
DG
Grant
Barth D
BD
Vidal
Marc
M
Boone
Charles
C
Sidhu
Sachdev S
SS
Bader
Gary D
GD
eng
MOP-84324
Canadian Institutes of Health Research
Canada
MOP-93725
Canadian Institutes of Health Research
Canada
P41 GM103504
GM
NIGMS NIH HHS
United States
R01 GM065462
GM
NIGMS NIH HHS
United States
R01 GM067237
GM
NIGMS NIH HHS
United States
R01 GM067237
GM
NIGMS NIH HHS
United States
R01 GM65462
GM
NIGMS NIH HHS
United States
R01 HG001715
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
England
Mol Syst Biol
101235389
1744-4292
0
Caenorhabditis elegans Proteins
0
Saccharomyces cerevisiae Proteins
IM
Sci STKE. 2003 Apr 22;2003(179):RE8
12709533
Nat Genet. 2003 May;34(1):35-41
12679813
Genome Biol. 2003;4(5):P3
12734009
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12835272
Nature. 2003 Dec 11;426(6967):676-80
14668868
Science. 2004 Jan 23;303(5657):540-3
14704431
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15093836
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10592234
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11731503
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Science. 2003 Apr 18;300(5618):445-52
12702867
Amino Acid Sequence
Animals
Caenorhabditis elegans
genetics
metabolism
Caenorhabditis elegans Proteins
genetics
metabolism
Conserved Sequence
Endocytosis
genetics
Evolution, Molecular
Molecular Sequence Data
Protein Interaction Mapping
Saccharomyces cerevisiae
genetics
metabolism
Saccharomyces cerevisiae Proteins
genetics
metabolism
Structural Homology, Protein
Two-Hybrid System Techniques
src Homology Domains
genetics
PMC3658277
2012
9
27
2013
2
20
2013
4
4
6
0
2013
4
4
6
0
2013
9
14
6
0
ppublish
msb20139
10.1038/msb.2013.9
23549480
PMC3658277
23520503
2013
03
22
2013
09
10
2014
11
20
1932-6203
8
3
2013
PloS one
PLoS ONE
Metabolic adaptation to chronic inhibition of mitochondrial protein synthesis in acute myeloid leukemia cells.
e58367
10.1371/journal.pone.0058367
Recently, we demonstrated that the anti-bacterial agent tigecycline preferentially induces death in leukemia cells through the inhibition of mitochondrial protein synthesis. Here, we sought to understand mechanisms of resistance to tigecycline by establishing a leukemia cell line resistant to the drug. TEX leukemia cells were treated with increasing concentrations of tigecycline over 4 months and a population of cells resistant to tigecycline (RTEX+TIG) was selected. Compared to wild type cells, RTEX+TIG cells had undetectable levels of mitochondrially translated proteins Cox-1 and Cox-2, reduced oxygen consumption and increased rates of glycolysis. Moreover, RTEX+TIG cells were more sensitive to inhibitors of glycolysis and more resistant to hypoxia. By electron microscopy, RTEX+TIG cells had abnormally swollen mitochondria with irregular cristae structures. RNA sequencing demonstrated a significant over-representation of genes with binding sites for the HIF1α:HIF1β transcription factor complex in their promoters. Upregulation of HIF1α mRNA and protein in RTEX+TIG cells was confirmed by Q-RTPCR and immunoblotting. Strikingly, upon removal of tigecycline from RTEX+TIG cells, the cells re-established aerobic metabolism. Levels of Cox-1 and Cox-2, oxygen consumption, glycolysis, mitochondrial mass and mitochondrial membrane potential returned to wild type levels, but HIF1α remained elevated. However, upon re-treatment with tigecycline for 72 hours, the glycolytic phenotype was re-established. Thus, we have generated cells with a reversible metabolic phenotype by chronic treatment with an inhibitor of mitochondrial protein synthesis. These cells will provide insight into cellular adaptations used to cope with metabolic stress.
Jhas
Bozhena
B
The Princess Margaret Hospital and The Ontario Cancer Institute, University Health Network, Toronto, Canada.
Sriskanthadevan
Shrivani
S
Skrtic
Marko
M
Sukhai
Mahadeo A
MA
Voisin
Veronique
V
Jitkova
Yulia
Y
Gronda
Marcela
M
Hurren
Rose
R
Laister
Rob C
RC
Bader
Gary D
GD
Minden
Mark D
MD
Schimmer
Aaron D
AD
eng
1R01CA157456
CA
NCI NIH HHS
United States
GM103504
GM
NIGMS NIH HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
R01 CA121941
CA
NCI NIH HHS
United States
R01 CA157456
CA
NCI NIH HHS
United States
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2013
03
08
United States
PLoS One
101285081
1932-6203
0
Anti-Bacterial Agents
0
HIF1A protein, human
0
Hypoxia-Inducible Factor 1, alpha Subunit
0
Mitochondrial Proteins
0
Neoplasm Proteins
70JE2N95KR
tigecycline
EC 1.9.3.1
Electron Transport Complex IV
FYY3R43WGO
Minocycline
IM
Anal Biochem. 2000 Nov 15;286(2):214-23
11067743
Cytometry A. 2004 Oct;61(2):162-9
15382028
J Histochem Cytochem. 1966 Apr;14(4):291-302
5962951
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4887876
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7219534
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2541396
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2549452
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2814477
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2359136
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7834868
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7539918
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8965721
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8959582
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9345305
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10231371
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Leukemia. 2005 Oct;19(10):1794-805
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22094260
Anti-Bacterial Agents
pharmacology
Cell Line, Tumor
Drug Resistance, Neoplasm
Electron Transport Complex IV
biosynthesis
genetics
Gene Expression Regulation, Leukemic
drug effects
genetics
Glycolysis
drug effects
genetics
Humans
Hypoxia-Inducible Factor 1, alpha Subunit
biosynthesis
genetics
Leukemia, Myeloid, Acute
drug therapy
genetics
metabolism
pathology
Minocycline
analogs & derivatives
pharmacology
Mitochondrial Proteins
biosynthesis
genetics
Neoplasm Proteins
biosynthesis
genetics
Oxygen Consumption
drug effects
genetics
Protein Biosynthesis
PMC3592803
2012
8
24
2013
2
4
2013
3
8
2013
3
23
6
0
2013
3
23
6
0
2013
9
11
6
0
ppublish
10.1371/journal.pone.0058367
PONE-D-12-25681
23520503
PMC3592803
23340843
2013
01
23
2013
09
16
2015
03
26
1744-4292
9
2013
Molecular systems biology
Mol. Syst. Biol.
Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers.
637
10.1038/msb.2012.68
Large-scale cancer genome sequencing has uncovered thousands of gene mutations, but distinguishing tumor driver genes from functionally neutral passenger mutations is a major challenge. We analyzed 800 cancer genomes of eight types to find single-nucleotide variants (SNVs) that precisely target phosphorylation machinery, important in cancer development and drug targeting. Assuming that cancer-related biological systems involve unexpectedly frequent mutations, we used novel algorithms to identify genes with significant phosphorylation-associated SNVs (pSNVs), phospho-mutated pathways, kinase networks, drug targets, and clinically correlated signaling modules. We highlight increased survival of patients with TP53 pSNVs, hierarchically organized cancer kinase modules, a novel pSNV in EGFR, and an immune-related network of pSNVs that correlates with prolonged survival in ovarian cancer. Our findings include multiple actionable cancer gene candidates (FLNB, GRM1, POU2F1), protein complexes (HCF1, ASF1), and kinases (PRKCZ). This study demonstrates new ways of interpreting cancer genomes and presents new leads for cancer research.
Reimand
Jüri
J
The Donnelly Centre, University of Toronto, Toronto, Canada. Juri.Reimand@utoronto.ca
Bader
Gary D
GD
eng
GM103504
GM
NIGMS NIH HHS
United States
MOP-84324
Canadian Institutes of Health Research
Canada
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 RR031228
RR
NCRR NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
England
Mol Syst Biol
101235389
1744-4292
0
Contractile Proteins
0
FLNB protein, human
0
Filamins
0
Microfilament Proteins
0
Octamer Transcription Factor-1
0
POU2F1 protein, human
0
Proteins
EC 2.7.-
Protein Kinases
EC 2.7.10.1
EGFR protein, human
EC 2.7.10.1
Receptor, Epidermal Growth Factor
EC 2.7.11.1
protein kinase C zeta
EC 2.7.11.13
Protein Kinase C
IM
Bioinformatics. 2004 Sep 1;20(13):2138-9
15044227
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1684878
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Nucleic Acids Res. 2011 Jan;39(Database issue):D1035-41
21059682
Nucleic Acids Res. 2011 Jan;39(Database issue):D261-7
21062810
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20959462
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Nucleic Acids Res. 2008 Jan;36(Database issue):D154-8
17991681
J Proteome Res. 2008 May;7(5):2140-50
18452278
Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W452-9
18460544
Proc Natl Acad Sci U S A. 2008 Aug 5;105(31):10762-7
18669648
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18772396
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18772397
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18772890
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18948947
Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22
18981052
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18988627
Eur J Cancer. 2009 Feb;45(3):321-7
19114024
Nat Rev Genet. 2009 Apr;10(4):252-63
19274049
Proteomics. 2009 May;9(10):2861-74
19415658
Genome Res. 2009 Jul;19(7):1316-23
19498102
J Mol Biol. 2009 Jul 31;390(5):1030-47
19505475
Sci Signal. 2009;2(81):ra39
19638616
Cell Stem Cell. 2009 Aug 7;5(2):214-26
19664995
PLoS One. 2009;4(11):e7830
19915675
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19884131
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22582013
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22561014
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10802651
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10823959
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11285227
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15286780
Algorithms
Contractile Proteins
genetics
metabolism
Female
Filamins
Genes, p53
Glioblastoma
genetics
Humans
Microfilament Proteins
genetics
metabolism
Models, Statistical
Mutation
Neoplasms
genetics
mortality
Octamer Transcription Factor-1
genetics
metabolism
Ovarian Neoplasms
genetics
mortality
Phosphorylation
Polymorphism, Single Nucleotide
Predictive Value of Tests
Protein Kinase C
genetics
metabolism
Protein Kinases
genetics
metabolism
Proteins
genetics
metabolism
Receptor, Epidermal Growth Factor
genetics
metabolism
Signal Transduction
genetics
PMC3564258
2012
5
04
2012
12
06
2013
1
24
6
0
2013
1
24
6
0
2013
9
17
6
0
ppublish
msb201268
10.1038/msb.2012.68
23340843
PMC3564258
23336252
2013
03
20
2013
09
18
2014
11
04
1471-2105
14
2013
BMC bioinformatics
BMC Bioinformatics
Predicting PDZ domain mediated protein interactions from structure.
27
10.1186/1471-2105-14-27
PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors.
We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training-testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling.
We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training-testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW.
Hui
Shirley
S
The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
Xing
Xiang
X
Bader
Gary D
GD
eng
MOP-84324
Canadian Institutes of Health Research
Canada
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
2013
01
21
England
BMC Bioinformatics
100965194
1471-2105
0
Ligands
0
Peptides
0
Proteome
IM
J Mol Biol. 2009 May 15;388(4):902-16
19324052
Adv Enzyme Regul. 2009;49(1):98-106
19534027
Mol Cells. 2009 Jun 30;27(6):629-34
19533032
Bioinformatics. 2009 Nov 1;25(21):2843-4
19759195
Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31
19850723
Nucleic Acids Res. 2010 Jan;38(Database issue):D497-501
19884131
Nucleic Acids Res. 2010 Jan;38(Database issue):D532-9
19897547
BMC Bioinformatics. 2010;11:144
20298601
Adv Exp Med Biol. 2010;674:107-23
20549944
J Mol Biol. 2010 Sep 17;402(2):460-74
20654621
Database (Oxford). 2010;2010:baq023
20940177
BMC Bioinformatics. 2010;11:507
20939902
PLoS One. 2010;5(11):e13984
21085593
Mol Vis. 2010;16:2259-72
21139678
Nucleic Acids Res. 2011 Jan;39(Database issue):D561-8
21045058
Nucleic Acids Res. 2011 Jan;39(Database issue):D698-704
21071413
J Mol Model. 2011 Feb;17(2):315-24
20461427
Bioinformatics. 2011 Feb 1;27(3):383-90
21127034
Toxicol Sci. 2011 Mar;120 Suppl 1:S49-75
21059794
Proc Natl Acad Sci U S A. 2011 Aug 30;108(35):14560-5
21841138
PLoS Comput Biol. 2011 Oct;7(10):e1002195
22039361
PLoS One. 2011;6(11):e25376
22069443
J Invest Dermatol. 2012 Jan;132(1):226-36
21881583
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D411-4
16381900
J Clin Invest. 1999 Nov;104(10):1353-61
10562297
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10592235
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10802651
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12519993
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12446668
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12702867
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3499612
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18389064
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18711339
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18591935
BMC Bioinformatics. 2008;9:405
18823568
Nucleic Acids Res. 2009 Jan;37(Database issue):D690-7
19033362
Wound Repair Regen. 2008 Sep-Oct;16(5):585-601
19128254
J Struct Funct Genomics. 2009 Mar;10(1):1-8
19037750
BMC Bioinformatics. 2009;10:32
19166624
Nat Chem Biol. 2009 Apr;5(4):217-9
19252499
Curr Opin Struct Biol. 2009 Apr;19(2):145-55
19327982
Humans
Ligands
Models, Molecular
PDZ Domains
Peptides
chemistry
metabolism
Protein Interaction Mapping
methods
Protein Interaction Maps
Proteome
chemistry
metabolism
Sequence Analysis, Protein
Support Vector Machines
PMC3602153
2012
2
15
2012
12
19
2013
1
21
2013
1
23
6
0
2013
1
23
6
0
2013
9
21
6
0
epublish
1471-2105-14-27
10.1186/1471-2105-14-27
23336252
PMC3602153
23324130
2013
01
28
2013
07
08
2014
11
04
1471-2261
13
2013
BMC cardiovascular disorders
BMC Cardiovasc Disord
Integrative pathway dissection of molecular mechanisms of moxLDL-induced vascular smooth muscle phenotype transformation.
4
10.1186/1471-2261-13-4
Atherosclerosis (AT) is a chronic inflammatory disease characterized by the accumulation of inflammatory cells, lipoproteins and fibrous tissue in the walls of arteries. AT is the primary cause of heart attacks and stroke and is the leading cause of death in Western countries. To date, the pathogenesis of AT is not well-defined. Studies have shown that the dedifferentiation of contractile and quiescent vascular smooth muscle cells (SMC) to the proliferative, migratory and synthetic phenotype in the intima is pivotal for the onset and progression of AT. To further delineate the mechanisms underlying the pathogenesis of AT, we analyzed the early molecular pathways and networks involved in the SMC phenotype transformation.
Quiescent human coronary artery SMCs were treated with minimally-oxidized LDL (moxLDL), for 3 hours and 21 hours, respectively. Transcriptomic data was generated for both time-points using microarrays and was subjected to pathway analysis using Gene Set Enrichment Analysis, GeneMANIA and Ingenuity software tools. Gene expression heat maps and pathways enriched in differentially expressed genes were compared to identify functional biological themes to elucidate early and late molecular mechanisms of moxLDL-induced SMC dedifferentiation.
Differentially expressed genes were found to be enriched in cholesterol biosynthesis, inflammatory cytokines, chemokines, growth factors, cell cycle control and myogenic contraction themes. These pathways are consistent with inflammatory responses, cell proliferation, migration and ECM production, which are characteristic of SMC dedifferentiation. Furthermore, up-regulation of cholesterol synthesis and dysregulation of cholesterol metabolism was observed in moxLDL-induced SMC. These observations are consistent with the accumulation of cholesterol and oxidized cholesterol esters, which induce proinflammatory reactions during atherogenesis. Our data implicate for the first time IL12, IFN-α, HGF, CSF3, and VEGF signaling in SMC phenotype transformation. GPCR signaling, HBP1 (repressor of cyclin D1 and CDKN1B), and ID2 and ZEB1 transcriptional regulators were also found to have important roles in SMC dedifferentiation. Several microRNAs were observed to regulate the SMC phenotype transformation via an interaction with IFN-γ pathway. Also, several "nexus" genes in complex networks, including components of the multi-subunit enzyme complex involved in the terminal stages of cholesterol synthesis, microRNAs (miR-203, miR-511, miR-590-3p, miR-346*/miR- 1207-5p/miR-4763-3p), GPCR proteins (GPR1, GPR64, GPRC5A, GPR171, GPR176, GPR32, GPR25, GPR124) and signal transduction pathways, were found to be regulated.
The systems biology analysis of the in vitro model of moxLDL-induced VSMC phenotype transformation was associated with the regulation of several genes not previously implicated in SMC phenotype transformation. The identification of these potential candidate genes enable hypothesis generation and in vivo functional experimentation (such as gain and loss-of-function studies) to establish causality with the process of SMC phenotype transformation and atherogenesis.
Karagiannis
George S
GS
Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, and Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON, M5S 1A8, Canada.
Weile
Jochen
J
Bader
Gary D
GD
Minta
Joe
J
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 RR031228
RR
NCRR NIH HHS
United States
P41HG04118
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2013
01
16
England
BMC Cardiovasc Disord
100968539
1471-2261
0
Lipoproteins, LDL
0
oxidized low density lipoprotein
IM
Atherosclerosis. 1998 Jun;138(2):247-53
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Atherosclerosis
genetics
metabolism
pathology
Cell Dedifferentiation
genetics
Cells, Cultured
Coronary Vessels
metabolism
pathology
Gene Expression Profiling
methods
Gene Expression Regulation
Gene Regulatory Networks
Genotype
Humans
Lipoproteins, LDL
metabolism
Muscle, Smooth, Vascular
metabolism
pathology
Myocytes, Smooth Muscle
metabolism
Oligonucleotide Array Sequence Analysis
Phenotype
Systems Biology
Time Factors
PMC3556327
2012
2
23
2012
12
29
2013
1
16
2013
1
18
6
0
2013
1
18
6
0
2013
7
9
6
0
epublish
1471-2261-13-4
10.1186/1471-2261-13-4
23324130
PMC3556327
23292740
2013
02
13
2013
08
12
2014
11
04
1367-4811
29
4
2013
Feb
15
Bioinformatics (Oxford, England)
Bioinformatics
GESTODIFFERENT: a Cytoscape plugin for the generation and the identification of gene regulatory networks describing a stochastic cell differentiation process.
513-4
10.1093/bioinformatics/bts726
The characterization of the complex phenomenon of cell differentiation is a key goal of both systems and computational biology. GeStoDifferent is a Cytoscape plugin aimed at the generation and the identification of gene regulatory networks (GRNs) describing an arbitrary stochastic cell differentiation process. The (dynamical) model adopted to describe general GRNs is that of noisy random Boolean networks (NRBNs), with a specific focus on their emergent dynamical behavior. GeStoDifferent explores the space of GRNs by filtering the NRBN instances inconsistent with a stochastic lineage differentiation tree representing the cell lineages that can be obtained by following the fate of a stem cell descendant. Matched networks can then be analyzed by Cytoscape network analysis algorithms or, for instance, used to define (multiscale) models of cellular dynamics.
Freely available at http://bimib.disco.unimib.it/index.php/Retronet#GESTODifferent or at the Cytoscape App Store http://apps.cytoscape.org/.
Antoniotti
Marco
M
Department of Informatics, Systems and Communication, University of Milan Bicocca, Viale Sarca 336, 20126, Milano, Italy. marco.antoniotti@unimib.it
Bader
Gary D
GD
Caravagna
Giulio
G
Crippa
Silvia
S
Graudenzi
Alex
A
Mauri
Giancarlo
G
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
2013
01
03
England
Bioinformatics
9808944
1367-4803
IM
J Theor Biol. 1969 Mar;22(3):437-67
5803332
PLoS One. 2008;3(8):e2922
18698344
PLoS One. 2011;6(3):e17703
21464974
Bioinformatics. 2011 Feb 1;27(3):431-2
21149340
Nature. 2009 Jul 2;460(7251):49-52
19571877
Cell Differentiation
genetics
Cell Lineage
Gene Regulatory Networks
Models, Genetic
Software
Stochastic Processes
PMC3888149
2013
1
3
2013
1
25
2013
1
8
6
0
2013
1
8
6
0
2013
8
13
6
0
ppublish
bts726
10.1093/bioinformatics/bts726
23292740
PMC3888149
23259511
2013
01
04
2013
06
07
1535-3907
12
1
2013
Jan
4
Journal of proteome research
J. Proteome Res.
The biology/disease-driven human proteome project (B/D-HPP): enabling protein research for the life sciences community.
23-7
10.1021/pr301151m
The biology and disease oriented branch of the Human Proteome Project (B/D-HPP) was established by the Human Proteome Organization (HUPO) with the main goal of supporting the broad application of state-of the-art measurements of proteins and proteomes by life scientists studying the molecular mechanisms of biological processes and human disease. This will be accomplished through the generation of research and informational resources that will support the routine and definitive measurement of the process or disease relevant proteins. The B/D-HPP is highly complementary to the C-HPP and will provide datasets and biological characterization useful to the C-HPP teams. In this manuscript we describe the goals, the plans, and the current status of the of the B/D-HPP.
Aebersold
Ruedi
R
Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. aebersold@imsb.biol.ethz.ch
Bader
Gary D
GD
Edwards
Aled M
AM
van Eyk
Jennifer E
JE
Kussmann
Martin
M
Qin
Jun
J
Omenn
Gilbert S
GS
eng
Journal Article
Research Support, Non-U.S. Gov't
2012
12
21
United States
J Proteome Res
101128775
1535-3893
0
Proteome
IM
Biological Science Disciplines
Disease
classification
genetics
Gene Expression
Genome, Human
Human Genome Project
Humans
Mass Spectrometry
Proteome
genetics
metabolism
2012
12
21
2012
12
25
6
0
2012
12
25
6
0
2013
6
8
6
0
ppublish
10.1021/pr301151m
23259511
23240084
2012
12
14
2015
05
14
2050-084X
1
2012
eLife
Elife
Chromatin is an ancient innovation conserved between Archaea and Eukarya.
e00078
10.7554/eLife.00078
The eukaryotic nucleosome is the fundamental unit of chromatin, comprising a protein octamer that wraps ∼147 bp of DNA and has essential roles in DNA compaction, replication and gene expression. Nucleosomes and chromatin have historically been considered to be unique to eukaryotes, yet studies of select archaea have identified homologs of histone proteins that assemble into tetrameric nucleosomes. Here we report the first archaeal genome-wide nucleosome occupancy map, as observed in the halophile Haloferax volcanii. Nucleosome occupancy was compared with gene expression by compiling a comprehensive transcriptome of Hfx. volcanii. We found that archaeal transcripts possess hallmarks of eukaryotic chromatin structure: nucleosome-depleted regions at transcriptional start sites and conserved -1 and +1 promoter nucleosomes. Our observations demonstrate that histones and chromatin architecture evolved before the divergence of Archaea and Eukarya, suggesting that the fundamental role of chromatin in the regulation of gene expression is ancient.DOI:http://dx.doi.org/10.7554/eLife.00078.001.
Ammar
Ron
R
Department of Molecular Genetics , University of Toronto , Toronto , Canada ; Donnelly Centre , University of Toronto , Toronto , Canada.
Torti
Dax
D
Tsui
Kyle
K
Gebbia
Marinella
M
Durbic
Tanja
T
Bader
Gary D
GD
Giaever
Guri
G
Nislow
Corey
C
eng
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2012
12
13
United States
Elife
101579614
2050-084X
0
Histones
0
Nucleosomes
0
Transcription Factors
IM
J Mol Biol. 2000 Oct 13;303(1):25-34
11021967
Genome Res. 2002 Oct;12(10):1619-23
12368255
Mol Microbiol. 2004 Jan;51(2):579-88
14756795
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14973331
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14976258
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1190944
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3806678
J Mol Biol. 1990 Oct 5;215(3):403-10
2231712
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8253798
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20644199
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20852648
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Nat Genet. 2011 May;43(5):491-8
21478889
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21785133
Methods Mol Biol. 2012;833:389-411
22183606
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18204304
Nat Rev Genet. 2013 Feb;14(2):78
23281445
Biological Evolution
Chromatin Assembly and Disassembly
DNA Replication
Eukaryotic Cells
cytology
metabolism
Gene Expression Profiling
Gene Expression Regulation, Archaeal
Genome, Archaeal
Haloferax volcanii
chemistry
genetics
metabolism
Histones
genetics
metabolism
Molecular Sequence Annotation
Nucleosomes
chemistry
metabolism
Promoter Regions, Genetic
Transcription Factors
genetics
metabolism
Transcription Initiation Site
Transcription, Genetic
PMC3510453
Archaea
Chromatin
Haloferax volcanii
Nucleosome
Other
RNA-seq
Transcriptome
Original DateCompleted: 20121217
2012
7
16
2012
9
25
2012
12
13
2012
12
15
6
0
2012
12
15
6
0
2012
12
15
6
1
epublish
10.7554/eLife.00078
00078
23240084
PMC3510453
23142521
2012
12
10
2013
07
30
2014
11
04
1875-9777
11
6
2012
Dec
7
Cell stem cell
Cell Stem Cell
Attenuation of miR-126 activity expands HSC in vivo without exhaustion.
799-811
10.1016/j.stem.2012.09.001
S1934-5909(12)00537-1
Lifelong blood cell production is governed through the poorly understood integration of cell-intrinsic and -extrinsic control of hematopoietic stem cell (HSC) quiescence and activation. MicroRNAs (miRNAs) coordinately regulate multiple targets within signaling networks, making them attractive candidate HSC regulators. We report that miR-126, a miRNA expressed in HSC and early progenitors, plays a pivotal role in restraining cell-cycle progression of HSC in vitro and in vivo. miR-126 knockdown by using lentiviral sponges increased HSC proliferation without inducing exhaustion, resulting in expansion of mouse and human long-term repopulating HSC. Conversely, enforced miR-126 expression impaired cell-cycle entry, leading to progressively reduced hematopoietic contribution. In HSC/early progenitors, miR-126 regulates multiple targets within the PI3K/AKT/GSK3β pathway, attenuating signal transduction in response to extrinsic signals. These data establish that miR-126 sets a threshold for HSC activation and thus governs HSC pool size, demonstrating the importance of miRNA in the control of HSC function.
Copyright © 2012 Elsevier Inc. All rights reserved.
Lechman
Eric R
ER
Campbell Family Institute, Ontario Cancer Institute, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada.
Gentner
Bernhard
B
van Galen
Peter
P
Giustacchini
Alice
A
Saini
Massimo
M
Boccalatte
Francesco E
FE
Hiramatsu
Hidefumi
H
Restuccia
Umberto
U
Bachi
Angela
A
Voisin
Veronique
V
Bader
Gary D
GD
Dick
John E
JE
Naldini
Luigi
L
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
TGT11D02
Telethon
Italy
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2012
11
08
United States
Cell Stem Cell
101311472
0
MIRN126 microRNA, human
0
MicroRNAs
EC 2.7.1.-
Phosphatidylinositol 3-Kinases
EC 2.7.11.1
Proto-Oncogene Proteins c-akt
EC 2.7.11.1
glycogen synthase kinase 3 beta
EC 2.7.11.26
Glycogen Synthase Kinase 3
IM
Curr Opin Hematol. 2011 Jul;18(4):226-30
21519240
Exp Hematol. 2011 May;39(5):511-20
21288477
Nat Med. 2011 Sep;17(9):1086-93
21873988
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16598206
Cell. 2007 Jan 26;128(2):325-39
17254970
J Immunol. 2008 Feb 15;180(4):2045-53
18250409
Dev Cell. 2008 Aug;15(2):261-71
18694565
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18694566
Genes Chromosomes Cancer. 2008 Nov;47(11):939-46
18663744
Biochem Biophys Res Commun. 2008 Dec 5;377(1):136-40
18834857
J Immunol. 2008 Dec 1;181(11):7514-24
19017941
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18987025
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19062086
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18767981
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19567251
Cell. 2009 Jul 23;138(2):328-39
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19703394
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19895320
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19959876
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20212066
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20354168
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20543838
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20577206
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21118986
Science. 2011 Jul 8;333(6039):218-21
21737740
Animals
Cell Line
Cell Proliferation
Gene Knockdown Techniques
Glycogen Synthase Kinase 3
metabolism
Hematopoiesis
genetics
Hematopoietic Stem Cells
cytology
enzymology
metabolism
Humans
Mice
MicroRNAs
genetics
metabolism
Phosphatidylinositol 3-Kinases
metabolism
Proto-Oncogene Proteins c-akt
metabolism
Signal Transduction
Transplantation, Heterologous
PMC3517970
2011
11
16
2012
6
30
2012
8
30
2012
11
8
2012
11
13
6
0
2012
11
13
6
0
2013
7
31
6
0
ppublish
S1934-5909(12)00537-1
10.1016/j.stem.2012.09.001
23142521
PMC3517970
23132118
2012
11
07
2013
01
14
2014
11
04
1548-7105
9
11
2012
Nov
Nature methods
Nat. Methods
A travel guide to Cytoscape plugins.
1069-76
10.1038/nmeth.2212
Cytoscape is open-source software for integration, visualization and analysis of biological networks. It can be extended through Cytoscape plugins, enabling a broad community of scientists to contribute useful features. This growth has occurred organically through the independent efforts of diverse authors, yielding a powerful but heterogeneous set of tools. We present a travel guide to the world of plugins, covering the 152 publicly available plugins for Cytoscape 2.5-2.8. We also describe ongoing efforts to distribute, organize and maintain the quality of the collection.
Saito
Rintaro
R
Department of Medicine, University of California, San Diego, La Jolla, California, USA.
Smoot
Michael E
ME
Ono
Keiichiro
K
Ruscheinski
Johannes
J
Wang
Peng-Liang
PL
Lotia
Samad
S
Pico
Alexander R
AR
Bader
Gary D
GD
Ideker
Trey
T
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 RR031228
RR
NCRR NIH HHS
United States
P50 GM085764
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
2012
11
06
United States
Nat Methods
101215604
1548-7091
IM
Bioinformatics. 2010 Apr 1;26(7):971-3
20139469
BMC Bioinformatics. 2010;11:5
20047655
Bioinformatics. 2010 Jul 15;26(14):1790-1
20507893
Cancer Res. 2010 Aug 15;70(16):6437-47
20663907
Nat Biotechnol. 2010 Sep;28(9):977-82
20802497
Nat Biotechnol. 2010 Sep;28(9):935-42
20829833
Bioinformatics. 2010 Nov 1;26(21):2796-7
20847220
Bioinformatics. 2010 Nov 15;26(22):2927-8
20926419
Bioinformatics. 2010 Dec 1;26(23):2995-6
20947524
PLoS One. 2010;5(11):e13984
21085593
Nucleic Acids Res. 2011 Jan;39(Database issue):D685-90
21071392
Nucleic Acids Res. 2011 Jan;39(Database issue):D698-704
21071413
BMC Syst Biol. 2010;4:164
21118483
Nat Protoc. 2011 Mar;6(3):285-95
21372810
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21257609
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21278188
Trends Biochem Sci. 2011 Apr;36(4):179-82
21345680
BMC Bioinformatics. 2011;12:192
21605434
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21716279
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21886098
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22070249
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20158874
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W755-62
20513647
Algorithms
Computational Biology
Computer Simulation
Data Mining
Database Management Systems
Gene Expression Profiling
Gene Regulatory Networks
Genes
physiology
Genomics
methods
Models, Biological
Software
NIHMS462839
PMC3649846
2011
12
13
2012
9
27
2012
11
06
2012
11
8
6
0
2012
11
8
6
0
2013
1
15
6
0
ppublish
nmeth.2212
10.1038/nmeth.2212
23132118
PMC3649846
NIHMS462839
22929553
2012
11
07
2013
02
20
2014
11
05
1752-0509
6
2012
BMC systems biology
BMC Syst Biol
Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study.
112
10.1186/1752-0509-6-112
The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment.
Our findings demonstrate that a miRNA's functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set.
We describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment.
Alshalalfa
Mohammed
M
Department of Computer Science, University of Calgary, Calgary, AB, Canada. msalshal@ucalgary.ca
Bader
Gary D
GD
Goldenberg
Anna
A
Morris
Quaid
Q
Alhajj
Reda
R
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
2012
08
28
England
BMC Syst Biol
101301827
1752-0509
0
MicroRNAs
0
Tumor Markers, Biological
IM
Mol Cancer Ther. 2011 Oct;10(10):1857-66
21768329
PLoS One. 2011;6(9):e24950
21980368
Nucleic Acids Res. 2011 Nov 1;39(20):e136
21835775
Bioinformatics. 2011 Nov 15;27(22):3166-72
21965819
Int J Cancer. 2012 Feb 1;130(3):611-21
21400514
Mol Biosyst. 2012 Apr;8(5):1492-8
22362105
Nucleic Acids Res. 2012 Apr;40(8):3689-703
22210864
Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21
11309499
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D497-501
14681466
Cell. 2003 Dec 26;115(7):787-98
14697198
Nat Genet. 2004 Jun;36(6):559-64
15167932
Nat Rev Genet. 2004 Jul;5(7):522-31
15211354
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15372042
J Biopharm Stat. 2004 Aug;14(3):701-21
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Nat Rev Cancer. 2006 Apr;6(4):259-69
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16969338
Nat Rev Cancer. 2006 Nov;6(11):857-66
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Biochem Biophys Res Commun. 2007 Jan 19;352(3):733-8
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Mol Cancer Ther. 2007 May;6(5):1483-91
17483436
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17612493
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17652130
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Mol Syst Biol. 2007;3:152
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17891175
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18085825
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18491312
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18613948
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19956180
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20404830
Acta Biochim Biophys Sin (Shanghai). 2010 Jun 15;42(6):363-9
20539944
Genome Biol. 2010;11(5):R53
20482850
Cancer Cell. 2010 Jul 13;18(1):11-22
20579941
Prostate Cancer Prostatic Dis. 2010 Sep;13(3):208-17
20585343
Nucleic Acids Res. 2010 Aug;38(15):e160
20576699
PLoS One. 2010;5(11):e13984
21085593
Nucleic Acids Res. 2011 Jan;39(Database issue):D163-9
21071411
Bioinformatics. 2011 Feb 1;27(3):431-2
21149340
BMC Med Genomics. 2011;4:8
21241464
Nat Commun. 2010;1:34
20975711
BMC Genomics. 2011;12:138
21375780
Nat Genet. 2011 Apr;43(4):288-9
21445071
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21524683
Nat Genet. 2011 Sep;43(9):854-9
21857679
BMC Syst Biol. 2011;5:136
21867514
Cell. 2011 Oct 14;147(2):370-81
22000015
Computational Biology
methods
Gene Expression Regulation, Neoplastic
Humans
Male
MicroRNAs
genetics
metabolism
Prognosis
Prostatic Neoplasms
diagnosis
genetics
metabolism
pathology
Protein Interaction Mapping
Protein Interaction Maps
Recurrence
Tumor Markers, Biological
metabolism
PMC3490713
2012
5
23
2012
8
14
2012
8
28
2012
8
30
6
0
2012
8
30
6
0
2013
2
21
6
0
epublish
1752-0509-6-112
10.1186/1752-0509-6-112
22929553
PMC3490713
22832581
2012
08
03
2012
09
04
2015
07
08
1476-4687
488
7409
2012
Aug
2
Nature
Nature
Subgroup-specific structural variation across 1,000 medulloblastoma genomes.
49-56
10.1038/nature11327
Medulloblastoma, the most common malignant paediatric brain tumour, is currently treated with nonspecific cytotoxic therapies including surgery, whole-brain radiation, and aggressive chemotherapy. As medulloblastoma exhibits marked intertumoural heterogeneity, with at least four distinct molecular variants, previous attempts to identify targets for therapy have been underpowered because of small samples sizes. Here we report somatic copy number aberrations (SCNAs) in 1,087 unique medulloblastomas. SCNAs are common in medulloblastoma, and are predominantly subgroup-enriched. The most common region of focal copy number gain is a tandem duplication of SNCAIP, a gene associated with Parkinson's disease, which is exquisitely restricted to Group 4α. Recurrent translocations of PVT1, including PVT1-MYC and PVT1-NDRG1, that arise through chromothripsis are restricted to Group 3. Numerous targetable SCNAs, including recurrent events targeting TGF-β signalling in Group 3, and NF-κB signalling in Group 4, suggest future avenues for rational, targeted therapy.
Northcott
Paul A
PA
Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Shih
David J H
DJ
Peacock
John
J
Garzia
Livia
L
Morrissy
A Sorana
AS
Zichner
Thomas
T
Stütz
Adrian M
AM
Korshunov
Andrey
A
Reimand
Jüri
J
Schumacher
Steven E
SE
Beroukhim
Rameen
R
Ellison
David W
DW
Marshall
Christian R
CR
Lionel
Anath C
AC
Mack
Stephen
S
Dubuc
Adrian
A
Yao
Yuan
Y
Ramaswamy
Vijay
V
Luu
Betty
B
Rolider
Adi
A
Cavalli
Florence M G
FM
Wang
Xin
X
Remke
Marc
M
Wu
Xiaochong
X
Chiu
Readman Y B
RY
Chu
Andy
A
Chuah
Eric
E
Corbett
Richard D
RD
Hoad
Gemma R
GR
Jackman
Shaun D
SD
Li
Yisu
Y
Lo
Allan
A
Mungall
Karen L
KL
Nip
Ka Ming
KM
Qian
Jenny Q
JQ
Raymond
Anthony G J
AG
Thiessen
Nina T
NT
Varhol
Richard J
RJ
Birol
Inanc
I
Moore
Richard A
RA
Mungall
Andrew J
AJ
Holt
Robert
R
Kawauchi
Daisuke
D
Roussel
Martine F
MF
Kool
Marcel
M
Jones
David T W
DT
Witt
Hendrick
H
Fernandez-L
Africa
A
Kenney
Anna M
AM
Wechsler-Reya
Robert J
RJ
Dirks
Peter
P
Aviv
Tzvi
T
Grajkowska
Wieslawa A
WA
Perek-Polnik
Marta
M
Haberler
Christine C
CC
Delattre
Olivier
O
Reynaud
Stéphanie S
SS
Doz
François F
FF
Pernet-Fattet
Sarah S
SS
Cho
Byung-Kyu
BK
Kim
Seung-Ki
SK
Wang
Kyu-Chang
KC
Scheurlen
Wolfram
W
Eberhart
Charles G
CG
Fèvre-Montange
Michelle
M
Jouvet
Anne
A
Pollack
Ian F
IF
Fan
Xing
X
Muraszko
Karin M
KM
Gillespie
G Yancey
GY
Di Rocco
Concezio
C
Massimi
Luca
L
Michiels
Erna M C
EM
Kloosterhof
Nanne K
NK
French
Pim J
PJ
Kros
Johan M
JM
Olson
James M
JM
Ellenbogen
Richard G
RG
Zitterbart
Karel
K
Kren
Leos
L
Thompson
Reid C
RC
Cooper
Michael K
MK
Lach
Boleslaw
B
McLendon
Roger E
RE
Bigner
Darell D
DD
Fontebasso
Adam
A
Albrecht
Steffen
S
Jabado
Nada
N
Lindsey
Janet C
JC
Bailey
Simon
S
Gupta
Nalin
N
Weiss
William A
WA
Bognár
László
L
Klekner
Almos
A
Van Meter
Timothy E
TE
Kumabe
Toshihiro
T
Tominaga
Teiji
T
Elbabaa
Samer K
SK
Leonard
Jeffrey R
JR
Rubin
Joshua B
JB
Liau
Linda M
LM
Van Meir
Erwin G
EG
Fouladi
Maryam
M
Nakamura
Hideo
H
Cinalli
Giuseppe
G
Garami
Miklós
M
Hauser
Peter
P
Saad
Ali G
AG
Iolascon
Achille
A
Jung
Shin
S
Carlotti
Carlos G
CG
Vibhakar
Rajeev
R
Ra
Young Shin
YS
Robinson
Shenandoah
S
Zollo
Massimo
M
Faria
Claudia C
CC
Chan
Jennifer A
JA
Levy
Michael L
ML
Sorensen
Poul H B
PH
Meyerson
Matthew
M
Pomeroy
Scott L
SL
Cho
Yoon-Jae
YJ
Bader
Gary D
GD
Tabori
Uri
U
Hawkins
Cynthia E
CE
Bouffet
Eric
E
Scherer
Stephen W
SW
Rutka
James T
JT
Malkin
David
D
Clifford
Steven C
SC
Jones
Steven J M
SJ
Korbel
Jan O
JO
Pfister
Stefan M
SM
Marra
Marco A
MA
Taylor
Michael D
MD
eng
GEO
GSE37385
AT1-112286
Canadian Institutes of Health Research
Canada
CA116804
CA
NCI NIH HHS
United States
CA138292
CA
NCI NIH HHS
United States
CA159859
CA
NCI NIH HHS
United States
CA86335
CA
NCI NIH HHS
United States
K08 NS059790
NS
NINDS NIH HHS
United States
P20 CA151129
CA
NCI NIH HHS
United States
P30 CA138292
CA
NCI NIH HHS
United States
P30 HD018655
HD
NICHD NIH HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
R01 CA086335
CA
NCI NIH HHS
United States
R01 CA109467
CA
NCI NIH HHS
United States
R01 CA114567
CA
NCI NIH HHS
United States
R01 CA116804
CA
NCI NIH HHS
United States
R01 CA148621
CA
NCI NIH HHS
United States
R01 CA155360
CA
NCI NIH HHS
United States
R01 CA159859
CA
NCI NIH HHS
United States
R01 CA163737
CA
NCI NIH HHS
United States
R01 NS061070
NS
NINDS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
England
Nature
0410462
0028-0836
0
Carrier Proteins
0
Hedgehog Proteins
0
NF-kappa B
0
Nerve Tissue Proteins
0
Oncogene Proteins, Fusion
0
PVT1 long-non-coding RNA, human
0
Proteins
0
RNA, Long Noncoding
0
SHH protein, human
0
SNCAIP protein, human
0
Transforming Growth Factor beta
IM
J Cell Physiol. 2007 Nov;213(2):511-8
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22029577
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22265402
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22134537
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17012043
Carrier Proteins
genetics
Cerebellar Neoplasms
classification
genetics
metabolism
Child
DNA Copy Number Variations
genetics
Gene Duplication
genetics
Genes, myc
genetics
Genome, Human
genetics
Genomic Structural Variation
genetics
Genomics
Hedgehog Proteins
metabolism
Humans
Medulloblastoma
classification
genetics
metabolism
NF-kappa B
metabolism
Nerve Tissue Proteins
genetics
Oncogene Proteins, Fusion
genetics
Proteins
genetics
RNA, Long Noncoding
Signal Transduction
Transforming Growth Factor beta
metabolism
Translocation, Genetic
genetics
NIHMS436539
PMC3683624
2012
2
29
2012
6
14
2012
7
25
2012
7
27
6
0
2012
7
27
6
0
2012
9
5
6
0
ppublish
nature11327
10.1038/nature11327
22832581
PMC3683624
NIHMS436539
22737063
2012
06
27
2012
10
25
2014
10
16
1553-7358
8
6
2012
PLoS computational biology
PLoS Comput. Biol.
Multiple genetic interaction experiments provide complementary information useful for gene function prediction.
e1002559
10.1371/journal.pcbi.1002559
Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.
Michaut
Magali
M
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Bader
Gary D
GD
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
2012
06
21
United States
PLoS Comput Biol
101238922
1553-734X
IM
Science. 2004 Feb 6;303(5659):832-5
14764878
Science. 2004 Feb 6;303(5659):808-13
14764870
Genome Biol. 2005;6(4):R38
15833125
Cell. 2005 Nov 4;123(3):507-19
16269340
Nat Genet. 2006 Aug;38(8):896-903
16845399
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17206143
Mol Syst Biol. 2007;3:96
17389876
Nature. 2007 Apr 12;446(7137):806-10
17314980
Nat Methods. 2007 Oct;4(10):861-6
17893680
Genome Biol. 2007;8(9):R186
17845715
Proc Natl Acad Sci U S A. 2008 Mar 4;105(9):3461-6
18305163
Science. 2008 Apr 18;320(5874):362-5
18420932
Genome Biol. 2008;9 Suppl 1:S4
18613948
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18931302
Nat Methods. 2008 Sep;5(9):789-95
18677321
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19325107
PLoS Comput Biol. 2009 Apr;5(4):e1000347
19343223
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19486505
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19506040
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20065090
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20093466
PLoS Comput Biol. 2010 Mar;6(3):e1000698
20221257
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20831804
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20926419
Nat Methods. 2010 Dec;7(12):1017-24
21076421
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21127252
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21378980
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J Biol. 2007;6(3):8
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14597658
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D258-61
14681407
Genome Res. 2004 Jun;14(6):1085-94
15173114
Computational Biology
Computer Simulation
Epistasis, Genetic
Gene Regulatory Networks
Genome, Fungal
Models, Genetic
Mutation
Phenotype
Saccharomyces cerevisiae
genetics
growth & development
PMC3380825
2011
11
2
2012
5
1
2012
6
21
2012
6
28
6
0
2012
6
28
6
0
2012
10
26
6
0
ppublish
10.1371/journal.pcbi.1002559
PCOMPBIOL-D-11-01633
22737063
PMC3380825
22696458
2012
07
25
2013
02
28
2014
10
16
1549-4918
30
8
2012
Aug
Stem cells (Dayton, Ohio)
Stem Cells
A comparative transcriptomic analysis reveals conserved features of stem cell pluripotency in planarians and mammals.
1734-45
10.1002/stem.1144
Many long-lived species of animals require the function of adult stem cells throughout their lives. However, the transcriptomes of stem cells in invertebrates and vertebrates have not been compared, and consequently, ancestral regulatory circuits that control stem cell populations remain poorly defined. In this study, we have used data from high-throughput RNA sequencing to compare the transcriptomes of pluripotent adult stem cells from planarians with the transcriptomes of human and mouse pluripotent embryonic stem cells. From a stringently defined set of 4,432 orthologs shared between planarians, mice and humans, we identified 123 conserved genes that are ≥5-fold differentially expressed in stem cells from all three species. Guided by this gene set, we used RNAi screening in adult planarians to discover novel stem cell regulators, which we found to affect the stem cell-associated functions of tissue homeostasis, regeneration, and stem cell maintenance. Examples of genes that disrupted these processes included the orthologs of TBL3, PSD12, TTC27, and RACK1. From these analyses, we concluded that by comparing stem cell transcriptomes from diverse species, it is possible to uncover conserved factors that function in stem cell biology. These results provide insights into which genes comprised the ancestral circuitry underlying the control of stem cell self-renewal and pluripotency.
Copyright © 2012 AlphaMed Press.
Labbé
Roselyne M
RM
The Hospital for Sick Children, Program in Developmental and Stem Cell Biology, University of Toronto, Toronto, Ontario, Canada.
Irimia
Manuel
M
Currie
Ko W
KW
Lin
Alexander
A
Zhu
Shu Jun
SJ
Brown
David D R
DD
Ross
Eric J
EJ
Voisin
Veronique
V
Bader
Gary D
GD
Blencowe
Benjamin J
BJ
Pearson
Bret J
BJ
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
United States
Stem Cells
9304532
1066-5099
IM
Genome Biol. 2012;13(3):R19
22439894
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22337995
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18456843
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18786419
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20040488
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20078655
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20124416
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20194744
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20436462
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21085593
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21848913
Genome Biol. 2011;12(8):R76
21846378
Nature. 2001 Nov 1;414(6859):105-11
11689955
Animals
Cell Differentiation
genetics
Gene Expression Profiling
Humans
Mammals
Mice
Planarians
Pluripotent Stem Cells
cytology
physiology
NIHMS622648
PMC4161212
2012
6
15
6
0
2012
6
15
6
0
2013
3
1
6
0
ppublish
10.1002/stem.1144
22696458
PMC4161212
NIHMS622648
22561014
2012
08
07
2012
10
29
1873-3468
586
17
2012
Aug
14
FEBS letters
FEBS Lett.
Domain-mediated protein interaction prediction: From genome to network.
2751-63
10.1016/j.febslet.2012.04.027
Protein-protein interactions (PPIs), involved in many biological processes such as cellular signaling, are ultimately encoded in the genome. Solving the problem of predicting protein interactions from the genome sequence will lead to increased understanding of complex networks, evolution and human disease. We can learn the relationship between genomes and networks by focusing on an easily approachable subset of high-resolution protein interactions that are mediated by peptide recognition modules (PRMs) such as PDZ, WW and SH3 domains. This review focuses on computational prediction and analysis of PRM-mediated networks and discusses sequence- and structure-based interaction predictors, techniques and datasets for identifying physiologically relevant PPIs, and interpreting high-resolution interaction networks in the context of evolution and human disease.
Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
Reimand
Jüri
J
The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario, Canada. Juri.Reimand@utoronto.ca
Hui
Shirley
S
Jain
Shobhit
S
Law
Brian
B
Bader
Gary D
GD
eng
Journal Article
Review
2012
05
03
Netherlands
FEBS Lett
0155157
0014-5793
0
Peptides
0
Proteins
9007-49-2
DNA
IM
Amino Acid Sequence
Animals
Artificial Intelligence
Computational Biology
methods
DNA
chemistry
Genome
Humans
Molecular Conformation
Molecular Sequence Data
Mutation
Peptides
chemistry
Protein Binding
Protein Interaction Domains and Motifs
Protein Interaction Mapping
methods
Protein Structure, Tertiary
Proteins
chemistry
src Homology Domains
2012
3
25
2012
4
17
2012
5
3
2012
5
8
6
0
2012
5
9
6
0
2012
10
30
6
0
ppublish
S0014-5793(12)00316-X
10.1016/j.febslet.2012.04.027
22561014
22460789
2012
04
12
2012
06
04
2014
11
20
1091-6490
109
15
2012
Apr
10
Proceedings of the National Academy of Sciences of the United States of America
Proc. Natl. Acad. Sci. U.S.A.
Seventeen-gene signature from enriched Her2/Neu mammary tumor-initiating cells predicts clinical outcome for human HER2+:ERα- breast cancer.
5832-7
10.1073/pnas.1201105109
Human Epidermal Growth Factor Receptor 2-positive (HER2(+)) breast cancer (BC) is a highly aggressive disease commonly treated with chemotherapy and anti-HER2 drugs, including trastuzumab. There is currently no way to predict which HER2(+) BC patients will benefit from these treatments. Previous prognostic signatures for HER2(+) BC were developed irrespective of the subtype or the hierarchical organization of cancer in which only a fraction of cells, tumor-initiating cells (TICs), can sustain tumor growth. Here, we used serial dilution and single-cell transplantation assays to identify MMTV-Her2/Neu mouse mammary TICs as CD24(+):JAG1(-) at a frequency of 2-4.5%. A 17-gene Her2-TIC-enriched signature (HTICS), generated on the basis of differentially expressed genes in TIC versus non-TIC fractions and trained on one HER2(+) BC cohort, predicted clinical outcome on multiple independent HER2(+) cohorts. HTICS included up-regulated genes involved in S/G2/M transition and down-regulated genes involved in immune response. Its prognostic power was independent of other predictors, stratified lymph node(+) HER2(+) BC into low and high-risk subgroups, and was specific for HER2(+):estrogen receptor alpha-negative (ERα(-)) patients (10-y overall survival of 83.6% for HTICS(-) and 24.0% for HTICS(+) tumors; hazard ratio = 5.57; P = 0.002). Whereas HTICS was specific to HER2(+):ERα(-) tumors, a previously reported stroma-derived signature was predictive for HER2(+):ERα(+) BC. Retrospective analyses revealed that patients with HTICS(+) HER2(+):ERα(-) tumors resisted chemotherapy but responded to chemotherapy plus trastuzumab. HTICS is, therefore, a powerful prognostic signature for HER2(+):ERα(-) BC that can be used to identify high risk patients that would benefit from anti-HER2 therapy.
Liu
Jeff C
JC
Division of Cell and Molecular Biology, Toronto General Research Institute, University Health Network, Toronto, ON, Canada M5G 2M9.
Voisin
Veronique
V
Bader
Gary D
GD
Deng
Tao
T
Pusztai
Lajos
L
Symmans
William Fraser
WF
Esteva
Francisco J
FJ
Egan
Sean E
SE
Zacksenhaus
Eldad
E
eng
GEO
GSE29590
GSE29616
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
2012
03
28
United States
Proc Natl Acad Sci U S A
7505876
0027-8424
0
Antibodies, Monoclonal, Humanized
0
Antigens, CD24
0
Antineoplastic Agents
0
Calcium-Binding Proteins
0
Estrogen Receptor alpha
0
Intercellular Signaling Peptides and Proteins
0
Membrane Proteins
0
estrogen receptor alpha, human
134324-36-0
Serrate proteins
EC 2.7.10.1
Receptor, ErbB-2
P188ANX8CK
trastuzumab
IM
N Engl J Med. 2001 Mar 15;344(11):783-92
11248153
Breast Cancer Res Treat. 2012 Apr;132(3):781-91
21373875
Proc Natl Acad Sci U S A. 1992 Nov 15;89(22):10578-82
1359541
N Engl J Med. 2004 Dec 30;351(27):2817-26
15591335
Cancer Res. 2005 Sep 15;65(18):8530-7
16166334
Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
16199517
Nat Rev Cancer. 2006 Feb;6(2):99-106
16491069
N Engl J Med. 2007 Jan 18;356(3):217-26
17229949
Eur J Immunol. 2007 Mar;37(3):675-85
17304628
Cancer Res. 2007 Sep 15;67(18):8671-81
17875707
Nat Biotechnol. 2008 Mar;26(3):317-25
18278033
Nat Med. 2008 May;14(5):518-27
18438415
Clin Cancer Res. 2008 Aug 15;14(16):5158-65
18698033
Oncogene. 2008 Aug 28;27(37):5019-32
18469855
Cancer Res. 2008 Oct 1;68(19):7711-7
18829523
Oncogene. 2008 Oct 16;27(47):6120-30
18591932
Eur J Cancer. 2008 Dec;44(18):2806-12
19022660
Oncologist. 2009 Jan;14(1):1-11
19147689
Semin Radiat Oncol. 2009 Apr;19(2):71-7
19249644
Cancer Res. 2009 Apr 15;69(8):3405-14
19351845
Cell. 2009 Sep 18;138(6):1083-95
19766563
J Clin Oncol. 2010 Apr 10;28(11):1813-20
20231686
J Clin Invest. 2010 Sep;120(9):3296-309
20679727
PLoS One. 2010;5(11):e13984
21085593
Nature. 2011 Jan 20;469(7330):362-7
21248843
Clin Cancer Res. 2011 Mar 1;17(5):952-8
21248299
Lancet Oncol. 2011 Mar;12(3):236-44
21354370
Cell. 2011 Oct 14;147(2):275-92
22000009
Cell. 1988 Jul 1;54(1):105-15
2898299
Animals
Antibodies, Monoclonal, Humanized
pharmacology
therapeutic use
Antigens, CD24
metabolism
Antineoplastic Agents
pharmacology
therapeutic use
Breast Neoplasms
drug therapy
genetics
Calcium-Binding Proteins
metabolism
Cell Differentiation
drug effects
Cell Division
drug effects
Estrogen Receptor alpha
metabolism
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
drug effects
Genes, Neoplasm
genetics
Humans
Intercellular Signaling Peptides and Proteins
metabolism
Membrane Proteins
metabolism
Mice
Neoadjuvant Therapy
Neoplastic Stem Cells
drug effects
metabolism
pathology
Prognosis
Receptor, ErbB-2
metabolism
Signal Transduction
drug effects
Treatment Outcome
PMC3326451
2012
3
28
2012
3
31
6
0
2012
3
31
6
0
2012
6
5
6
0
ppublish
1201105109
10.1073/pnas.1201105109
22460789
PMC3326451
22453906
2012
03
28
2012
06
25
1548-7105
9
4
2012
Apr
Nature methods
Nat. Methods
Phosphorylation sites of higher stoichiometry are more conserved.
317; author reply 318
10.1038/nmeth.1941
Tan
Chris Soon Heng
CS
Bader
Gary D
GD
eng
MOP-84324
Canadian Institutes of Health Research
Canada
Comment
Letter
Research Support, Non-U.S. Gov't
2012
03
27
United States
Nat Methods
101215604
1548-7091
0
Proteome
EC 3.1.3.-
Phosphoric Monoester Hydrolases
IM
Nat Methods. 2011 Aug;8(8):677-83
21725298
Isotope Labeling
methods
Mass Spectrometry
methods
Phosphoric Monoester Hydrolases
chemistry
metabolism
Phosphorylation
physiology
Proteome
chemistry
metabolism
2012
3
29
6
0
2012
3
29
6
0
2012
6
26
6
0
epublish
nmeth.1941
10.1038/nmeth.1941
22453906
22438817
2012
03
22
2012
09
04
2014
10
19
1553-7404
8
3
2012
PLoS genetics
PLoS Genet.
Mapping the Hsp90 genetic interaction network in Candida albicans reveals environmental contingency and rewired circuitry.
e1002562
10.1371/journal.pgen.1002562
The molecular chaperone Hsp90 regulates the folding of diverse signal transducers in all eukaryotes, profoundly affecting cellular circuitry. In fungi, Hsp90 influences development, drug resistance, and evolution. Hsp90 interacts with -10% of the proteome in the model yeast Saccharomyces cerevisiae, while only two interactions have been identified in Candida albicans, the leading fungal pathogen of humans. Utilizing a chemical genomic approach, we mapped the C. albicans Hsp90 interaction network under diverse stress conditions. The chaperone network is environmentally contingent, and most of the 226 genetic interactors are important for growth only under specific conditions, suggesting that they operate downstream of Hsp90, as with the MAPK Hog1. Few interactors are important for growth in many environments, and these are poised to operate upstream of Hsp90, as with the protein kinase CK2 and the transcription factor Ahr1. We establish environmental contingency in the first chaperone network of a fungal pathogen, novel effectors upstream and downstream of Hsp90, and network rewiring over evolutionary time.
Diezmann
Stephanie
S
Department of Molecular Genetics, University of Toronto, Toronto, Canada.
Michaut
Magali
M
Shapiro
Rebecca S
RS
Bader
Gary D
GD
Cowen
Leah E
LE
eng
MOP-86452
Canadian Institutes of Health Research
Canada
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
2012
03
15
United States
PLoS Genet
101239074
1553-7390
0
Benzoquinones
0
Culture Media
0
HSP90 Heat-Shock Proteins
0
Lactams, Macrocyclic
8L70Q75FXE
Adenosine Triphosphate
EC 2.7.-
Phosphotransferases
Z3K3VJ16KU
geldanamycin
IM
Clin Infect Dis. 2005 Nov 1;41(9):1232-9
16206095
Science. 2005 Sep 30;309(5744):2185-9
16195452
Nature. 2005 Nov 3;438(7064):108-12
16267557
PLoS Pathog. 2006 Apr;2(4):e35
16652171
Proteomics. 2009 Oct;9(20):4686-703
19824012
PLoS Genet. 2009 Dec;5(12):e1000783
20041210
Crit Rev Microbiol. 2010;36(1):1-53
20088682
PLoS Pathog. 2010 Feb;6(2):e1000752
20140194
Circ Res. 2010 Apr 30;106(8):1404-12
20299663
Nat Rev Mol Cell Biol. 2010 Jul;11(7):515-28
20531426
Nat Genet. 2010 Jul;42(7):590-8
20543849
Nat Rev Cancer. 2010 Aug;10(8):537-49
20651736
PLoS Pathog. 2010;6(8):e1001069
20865172
Annu Rev Genet. 2010;44:189-216
21047258
J Biol Chem. 2010 Dec 3;285(49):37964-75
20837488
Annu Rev Biochem. 2006;75:271-94
16756493
PLoS Pathog. 2006 Jul;2(7):e63
16839200
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16939537
Clin Microbiol Rev. 2007 Jan;20(1):133-63
17223626
Eukaryot Cell. 2007 Mar;6(3):521-32
17220467
Cell Microbiol. 2007 Jul;9(7):1647-59
17346314
Cell. 2007 Oct 5;131(1):121-35
17923092
PLoS Comput Biol. 2007 Sep;3(9):1701-15
17941702
Nat Rev Microbiol. 2008 Jan;6(1):67-78
18079743
Nat Rev Microbiol. 2008 Mar;6(3):187-98
18246082
Eukaryot Cell. 2008 May;7(5):747-64
18375617
J Biol Chem. 2008 Jul 4;283(27):18473-7
18442971
Cell Stress Chaperones. 2009 Jan;14(1):83-94
18636345
BMC Genomics. 2008;9:578
19055720
Proc Natl Acad Sci U S A. 2009 Feb 24;106(8):2818-23
19196973
Curr Biol. 2009 Apr 28;19(8):621-9
19327993
PLoS Pathog. 2009 Jul;5(7):e1000532
19649312
Mol Microbiol. 2011 Feb;79(4):940-53
21299649
Science. 2010 Dec 24;330(6012):1820-4
21205668
Bioinformatics. 2009 Nov 15;25(22):3043-4
19717575
Mol Cell. 2011 Mar 18;41(6):672-81
21419342
Oncotarget. 2011 May;2(5):407-17
21576760
BMC Microbiol. 2011;11:162
21745372
EMBO J. 2002 May 15;21(10):2343-53
12006487
Nature. 2002 Jun 6;417(6889):618-24
12050657
J Biol Chem. 2003 Jan 31;278(5):2829-36
12435747
Mol Biol Cell. 2003 Oct;14(10):4296-305
14517337
Genome Res. 2003 Nov;13(11):2498-504
14597658
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1551911
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8078881
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9845070
J Bacteriol. 1999 May;181(10):3058-68
10322006
Nat Biotechnol. 1999 Oct;17(10):1030-2
10504710
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15180930
Cell. 2005 Mar 11;120(5):715-27
15766533
Mol Microbiol. 2005 Apr;56(2):559-73
15813744
Eukaryot Cell. 2005 May;4(5):849-60
15879519
Nature. 2005 Nov 3;438(7064):103-7
16267556
Adenosine Triphosphate
metabolism
Benzoquinones
pharmacology
Candida albicans
genetics
growth & development
metabolism
Culture Media
Environmental Microbiology
Gene Expression Regulation, Bacterial
Gene Regulatory Networks
drug effects
genetics
HSP90 Heat-Shock Proteins
genetics
metabolism
Lactams, Macrocyclic
pharmacology
Phosphotransferases
metabolism
Protein Interaction Maps
drug effects
genetics
Saccharomyces cerevisiae
genetics
metabolism
Signal Transduction
drug effects
genetics
Stress, Physiological
genetics
PMC3305360
2011
8
17
2012
1
13
2012
3
15
2012
3
23
6
0
2012
3
23
6
0
2012
9
5
6
0
ppublish
10.1371/journal.pgen.1002562
PGENETICS-D-11-01760
22438817
PMC3305360
22210894
2012
03
29
2012
05
29
2014
10
19
1362-4962
40
6
2012
Mar
Nucleic acids research
Nucleic Acids Res.
MUSI: an integrated system for identifying multiple specificity from very large peptide or nucleic acid data sets.
e47
10.1093/nar/gkr1294
Peptide recognition domains and transcription factors play crucial roles in cellular signaling. They bind linear stretches of amino acids or nucleotides, respectively, with high specificity. Experimental techniques that assess the binding specificity of these domains, such as microarrays or phage display, can retrieve thousands of distinct ligands, providing detailed insight into binding specificity. In particular, the advent of next-generation sequencing has recently increased the throughput of such methods by several orders of magnitude. These advances have helped reveal the presence of distinct binding specificity classes that co-exist within a set of ligands interacting with the same target. Here, we introduce a software system called MUSI that can rapidly analyze very large data sets of binding sequences to determine the relevant binding specificity patterns. Our pipeline provides two major advances. First, it can detect previously unrecognized multiple specificity patterns in any data set. Second, it offers integrated processing of very large data sets from next-generation sequencing machines. The results are visualized as multiple sequence logos describing the different binding preferences of the protein under investigation. We demonstrate the performance of MUSI by analyzing recent phage display data for human SH3 domains as well as microarray data for mouse transcription factors.
Kim
Taehyung
T
The Donnelly Centre, Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada M5S 3E1.
Tyndel
Marc S
MS
Huang
Haiming
H
Sidhu
Sachdev S
SS
Bader
Gary D
GD
Gfeller
David
D
Kim
Philip M
PM
eng
MOP-84324
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
Validation Studies
2011
12
30
England
Nucleic Acids Res
0411011
0305-1048
0
Ligands
0
Peptide Library
0
Peptides
0
Transcription Factors
IM
PLoS Biol. 2009 Oct;7(10):e1000218
19841731
BMC Bioinformatics. 2010;11:243
20459839
J Cell Sci. 2001 Apr;114(Pt 7):1253-63
11256992
J Cell Sci. 2001 Sep;114(Pt 18):3219-31
11591811
Mol Cell. 2001 Nov;8(5):937-46
11741530
J Comput Biol. 2002;9(2):447-64
12015892
Science. 2003 Apr 18;300(5618):445-52
12702867
Nucleic Acids Res. 2003 Jul 1;31(13):3586-8
12824371
Nucleic Acids Res. 2003 Jul 1;31(13):3635-41
12824383
Mol Cell Biol. 2004 Mar;24(6):2546-59
14993291
Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W199-203
15215380
J Mol Biol. 2004 Oct 22;343(3):703-18
15465056
Science. 1989 Jul 28;245(4916):371-8
2667136
Nucleic Acids Res. 1990 Oct 25;18(20):6097-100
2172928
Proc Int Conf Intell Syst Mol Biol. 1995;3:21-9
7584439
Proc Int Conf Intell Syst Mol Biol. 1994;2:28-36
7584402
Cell. 1996 Jun 28;85(7):1067-76
8674113
Comput Appl Biosci. 1997 Aug;13(4):365-76
9283751
EMBO J. 1998 Jun 1;17(11):3091-100
9606191
Mol Cell Biol. 1998 Dec;18(12):7038-51
9819391
Nat Genet. 2004 Dec;36(12):1331-9
15543148
Nat Methods. 2004 Oct;1(1):27-9
15782149
Bioinformatics. 2005 Jun 1;21(11):2657-66
15797905
Annu Rev Cell Dev Biol. 2005;21:659-93
16212511
Nat Protoc. 2007;2(6):1368-86
17545975
Science. 2007 Jul 20;317(5836):364-9
17641200
Cell. 2008 Jun 27;133(7):1266-76
18585359
Sci Signal. 2008;1(35):ra2
18765831
Bioinformatics. 2008 Sep 15;24(18):2096-7
18689808
Nat Biotechnol. 2008 Sep;26(9):1041-5
18711339
Nucleic Acids Res. 2009 Jan;37(Database issue):D77-82
18842628
Nat Methods. 2008 Sep;5(9):829-34
19160518
Nat Chem Biol. 2009 Apr;5(4):217-9
19252499
Science. 2009 Jun 26;324(5935):1720-3
19443739
Nat Rev Genet. 2009 Sep;10(9):605-16
19668247
PLoS Comput Biol. 2009 Dec;5(12):e1000590
19997485
Nucleic Acids Res. 2010 Jan;38(Database issue):D105-10
19906716
Sci Signal. 2010;3(104):ra3
20068231
Proc Natl Acad Sci U S A. 2010 Mar 9;107(10):4544-9
20176964
EMBO J. 2010 Jul 7;29(13):2147-60
20517297
Bioinformatics. 2010 Aug 1;26(15):1899-900
20427515
Nat Methods. 2010 Sep;7(9):741-6
20711194
Mol Biosyst. 2010 Oct;6(10):1782-90
20714644
Mol Syst Biol. 2011 Apr 26;7:484
21525870
Nucleic Acids Res. 2010 Apr;38(6):1767-71
20015970
Proc Natl Acad Sci U S A. 1999 Nov 9;96(23):13130-5
10557285
Animals
Binding Sites
High-Throughput Nucleotide Sequencing
Humans
Ligands
Mice
Peptide Library
Peptides
chemistry
Position-Specific Scoring Matrices
Protein Interaction Domains and Motifs
Sequence Analysis, Protein
Software
Transcription Factors
metabolism
src Homology Domains
PMC3315295
2011
12
30
2012
1
3
6
0
2012
1
3
6
0
2012
5
30
6
0
ppublish
gkr1294
10.1093/nar/gkr1294
22210894
PMC3315295
23552839
2013
04
04
2013
04
04
2015
07
01
2157-9024
1
2012
Oncogenesis
Oncogenesis
Disruption of Abi1/Hssh3bp1 expression induces prostatic intraepithelial neoplasia in the conditional Abi1/Hssh3bp1 KO mice.
e26
10.1038/oncsis.2012.28
Prostate cancer is one of the leading causes of cancer-related deaths in the United States and a leading diagnosed non-skin cancer in American men. Genetic mutations underlying prostate tumorigenesis include alterations of tumor suppressor genes. We tested the tumor suppressor hypothesis for ABI1/hSSH3BP1 by searching for gene mutations in primary prostate tumors from patients, and by analyzing the consequences of prostate-specific disruption of the mouse Abi1/Hssh3bp1 ortholog. We sequenced the ABI1/hSSH3BP1 gene and identified recurring mutations in 6 out of 35 prostate tumors. Moreover, complementation and anchorage-independent growth, proliferation, cellular adhesion and xenograft assays using the LNCaP cell line, which contains a loss-of-function Abi1 mutation, and a stably expressed wild-type or mutated ABI gene, were consistent with the tumor suppressor hypothesis. To test the hypothesis further, we disrupted the gene in the mouse prostate by breeding the Abi1 floxed strain with the probasin promoter-driven Cre recombinase strain. Histopathological evaluation of mice indicated development of prostatic intraepithelial neoplasia (PIN) in Abi1/Hssh3bp1 knockout mouse as early as the eighth month, but no progression beyond PIN was observed in mice as old as 12 months. Observed decreased levels of E-cadherin, β-catenin and WAVE2 in mouse prostate suggest abnormal cellular adhesion as the mechanism underlying PIN development owing to Abi1 disruption. Analysis of syngeneic cell lines point to the possibility that upregulation of phospho-Akt underlies the enhanced cellular proliferation phenotype of cells lacking Abi1. This study provides proof-of-concept for the hypothesis that Abi1 downregulation has a role in the development of prostate cancer.
Xiong
X
X
Laboratory of Cell Signaling, New York Blood Center, New York, NY, USA.
Chorzalska
A
A
Dubielecka
P M
PM
White
J R
JR
Vedvyas
Y
Y
Hedvat
C V
CV
Haimovitz-Friedman
A
A
Koutcher
J A
JA
Reimand
J
J
Bader
G D
GD
Sawicki
J A
JA
Kotula
L
L
eng
R01 NS044968
NS
NINDS NIH HHS
United States
Journal Article
2012
09
03
United States
Oncogenesis
101580004
2157-9024
PMC3503296
2013
4
5
6
0
2012
1
1
0
0
2012
1
1
0
1
epublish
oncsis201228
10.1038/oncsis.2012.28
23552839
PMC3503296
22180826
2011
12
19
2012
08
23
1944-3277
5
2
2011
Nov
30
Standards in genomic sciences
Stand Genomic Sci
Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE).
230-42
10.4056/sigs.2034671
The Computational Modeling in Biology Network (COMBINE), is an initiative to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarizes the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathon, held in New York on April 18-22 2011. The first of those meetings hosted 81 attendees. Discussions covered both official COMBINE standards-(BioPAX, SBGN and SBML), as well as emerging efforts and interoperability between different formats. The second meeting, oriented towards software developers, welcomed 59 participants and witnessed many technical discussions, development of improved standards support in community software systems and conversion between the standards. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner.
Le Novère
Nicolas
N
Hucka
Michael
M
Anwar
Nadia
N
Bader
Gary D
GD
Demir
Emek
E
Moodie
Stuart
S
Sorokin
Anatoly
A
eng
R01 GM070923
GM
NIGMS NIH HHS
United States
Journal Article
United States
Stand Genomic Sci
101530505
1944-3277
PMC3235518
2011
12
20
6
0
2011
12
20
6
0
2011
12
20
6
1
ppublish
10.4056/sigs.2034671
sigs.2034671
22180826
PMC3235518
22070249
2012
01
23
2012
05
14
2014
10
21
1471-2105
12
2011
BMC bioinformatics
BMC Bioinformatics
clusterMaker: a multi-algorithm clustering plugin for Cytoscape.
436
10.1186/1471-2105-12-436
In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.
Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.
The Cytoscape plugin clusterMaker provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the clusterMaker plugin. clusterMaker is available via the Cytoscape plugin manager.
Morris
John H
JH
Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu
Apeltsin
Leonard
L
Newman
Aaron M
AM
Baumbach
Jan
J
Wittkop
Tobias
T
Su
Gang
G
Bader
Gary D
GD
Ferrin
Thomas E
TE
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
P41 RR001081
RR
NCRR NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
2011
11
09
England
BMC Bioinformatics
100965194
1471-2105
EC 5.1.-
Racemases and Epimerases
EC 5.1.99.1
methylmalonyl-coenzyme A racemase
IM
Proc Natl Acad Sci U S A. 2003 Feb 4;100(3):1128-33
12538875
Biotechniques. 2003 Feb;34(2):374-8
12613259
BMC Bioinformatics. 2003 Jan 13;4:2
12525261
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Algorithms
Animals
Cluster Analysis
Genomics
Mice
Protein Interaction Maps
Racemases and Epimerases
genetics
Saccharomyces cerevisiae
enzymology
genetics
Software
PMC3262844
2011
8
8
2011
11
9
2011
11
9
2011
11
11
6
0
2011
11
11
6
0
2012
5
15
6
0
epublish
1471-2105-12-436
10.1186/1471-2105-12-436
22070249
PMC3262844
22035796
2011
10
31
2012
04
02
2014
10
21
1879-1301
18
10
2011
Oct
28
Chemistry & biology
Chem. Biol.
Compound prioritization methods increase rates of chemical probe discovery in model organisms.
1273-83
10.1016/j.chembiol.2011.07.018
Preselection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in Saccharomyces cerevisiae and identified ~7500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. These data were used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes, we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ~7500 growth-inhibitory molecules have been made commercially available and the computational model and filter used are provided.
Copyright © 2011 Elsevier Ltd. All rights reserved.
Wallace
Iain M
IM
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
Urbanus
Malene L
ML
Luciani
Genna M
GM
Burns
Andrew R
AR
Han
Mitchell K L
MK
Wang
Hao
H
Arora
Kriti
K
Heisler
Lawrence E
LE
Proctor
Michael
M
St Onge
Robert P
RP
Roemer
Terry
T
Roy
Peter J
PJ
Cummins
Carolyn L
CL
Bader
Gary D
GD
Nislow
Corey
C
Giaever
Guri
G
eng
MOP-68813
Canadian Institutes of Health Research
Canada
MOP-97904
Canadian Institutes of Health Research
Canada
MOPS-81340
Canadian Institutes of Health Research
Canada
MOPS-84305
Canadian Institutes of Health Research
Canada
R01 HG003317
HG
NHGRI NIH HHS
United States
R01 HG003317-02
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
United States
Chem Biol
9500160
1074-5521
0
Benzofurans
0
ERG7.153
0
Enzyme Inhibitors
0
Piperazines
0
Small Molecule Libraries
EC 1.14.19.-
Fatty Acid Desaturases
EC 1.14.99.-
delta-9 fatty acid desaturase
EC 5.4.-
Intramolecular Transferases
EC 5.4.99.7
lanosterol synthase
IM
J Biol Chem. 2009 Jul 3;284(27):17968-74
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22035786
Bacillus subtilis
drug effects
growth & development
Bayes Theorem
Benzofurans
chemistry
metabolism
pharmacology
Candida albicans
drug effects
growth & development
Computer Simulation
Enzyme Inhibitors
chemistry
pharmacology
Escherichia coli
drug effects
growth & development
Fatty Acid Desaturases
antagonists & inhibitors
metabolism
HeLa Cells
Humans
Intramolecular Transferases
antagonists & inhibitors
metabolism
Models, Biological
Phenotype
Piperazines
chemistry
metabolism
pharmacology
Saccharomyces cerevisiae
chemistry
growth & development
metabolism
Small Molecule Libraries
NIHMS330010
PMC4193902
2010
11
22
2011
6
29
2011
7
15
2011
11
1
6
0
2011
11
1
6
0
2012
4
3
6
0
ppublish
S1074-5521(11)00276-6
10.1016/j.chembiol.2011.07.018
22035796
PMC4193902
NIHMS330010
21959865
2011
09
30
2012
01
18
2015
02
09
1758-0463
2011
2011
Database : the journal of biological databases and curation
Database (Oxford)
NetSlim: high-confidence curated signaling maps.
bar032
10.1093/database/bar032
We previously developed NetPath as a resource for comprehensive manually curated signal transduction pathways. The pathways in NetPath contain a large number of molecules and reactions which can sometimes be difficult to visualize or interpret given their complexity. To overcome this potential limitation, we have developed a set of more stringent curation and inclusion criteria for pathway reactions to generate high-confidence signaling maps. NetSlim is a new resource that contains this 'core' subset of reactions for each pathway for easy visualization and manipulation. The pathways in NetSlim are freely available at http://www.netpath.org/netslim.
Raju
Rajesh
R
Institute of Bioinformatics, International Tech Park, Bangalore, India.
Nanjappa
Vishalakshi
V
Balakrishnan
Lavanya
L
Radhakrishnan
Aneesha
A
Thomas
Joji Kurian
JK
Sharma
Jyoti
J
Tian
Maozhen
M
Palapetta
Shyam Mohan
SM
Subbannayya
Tejaswini
T
Sekhar
Nirujogi Raja
NR
Muthusamy
Babylakshmi
B
Goel
Renu
R
Subbannayya
Yashwanth
Y
Telikicherla
Deepthi
D
Bhattacharjee
Mitali
M
Pinto
Sneha M
SM
Syed
Nazia
N
Srikanth
Manda Srinivas
MS
Sathe
Gajanan J
GJ
Ahmad
Sartaj
S
Chavan
Sandip N
SN
Kumar
Ghantasala S Sameer
GS
Marimuthu
Arivusudar
A
Prasad
T S K
TS
Harsha
H C
HC
Rahiman
B Abdul
BA
Ohara
Osamu
O
Bader
Gary D
GD
Sujatha Mohan
S
S
Schiemann
William P
WP
Pandey
Akhilesh
A
eng
Journal Article
Research Support, Non-U.S. Gov't
2011
09
29
England
Database (Oxford)
101517697
1758-0463
0
Transforming Growth Factor beta
IM
Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9
18832364
EMBO J. 2007 Sep 5;26(17):3957-67
17673906
Future Oncol. 2009 Mar;5(2):259-71
19284383
Nat Immunol. 2009 Apr;10(4):327-31
19295628
Nat Cell Biol. 2009 Jul;11(7):881-9
19543271
Nat Biotechnol. 2009 Aug;27(8):735-41
19668183
Bioinformatics. 2009 Nov 1;25(21):2860-2
19628504
Methods Enzymol. 2009;467:281-306
19897097
BMC Bioinformatics. 2009;10:370
19895694
Breast Cancer Res. 2009;11(5):R68
19740433
Bioinformatics. 2010 Feb 1;26(3):429-31
20007251
Genome Biol. 2010;11(1):R3
20067622
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W150-4
20460470
Nat Biotechnol. 2010 Sep;28(9):935-42
20829833
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Database Management Systems
Databases, Factual
Internet
Signal Transduction
Transforming Growth Factor beta
metabolism
User-Computer Interface
PMC3263596
2011
2011
10
1
6
0
2011
10
1
6
0
2012
1
19
6
0
epublish
bar032
10.1093/database/bar032
21959865
PMC3263596
21877291
2011
08
30
2012
01
24
2013
09
19
1940-6029
781
2011
Methods in molecular biology (Clifton, N.J.)
Methods Mol. Biol.
Displaying chemical information on a biological network using Cytoscape.
363-76
10.1007/978-1-61779-276-2_18
Cytoscape is an open-source software package that is widely used to integrate and visualize diverse data sets in biology. This chapter explains how to use Cytoscape to integrate open-source chemical information with a biological network. By visualizing information about known compound-target interactions in the context of a biological network of interest, one can rapidly identify novel avenues to perturb the system with compounds and, for example, potentially identify therapeutically relevant targets. Herein, two different protocols are explained in detail, with no prior knowledge of Cytoscape assumed, which demonstrate how to incorporate data from the ChEMBL database with either a gene-gene or a protein-protein interaction network. ChEMBL is a very large, open-source repository of compound-target information available from the European Molecular Biology Laboratory.
Wallace
Iain M
IM
Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
Bader
Gary D
GD
Giaever
Guri
G
Nislow
Corey
C
eng
MOPS-81340
Canadian Institutes of Health Research
Canada
MOPS-84305
Canadian Institutes of Health Research
Canada
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
United States
Methods Mol Biol
9214969
1064-3745
IM
Computational Biology
methods
Databases, Genetic
Databases, Protein
Gene Regulatory Networks
drug effects
Molecular Targeted Therapy
Protein Interaction Maps
drug effects
Software
2011
8
31
6
0
2011
8
31
6
0
2012
1
25
6
0
ppublish
10.1007/978-1-61779-276-2_18
21877291
21877285
2011
08
30
2012
01
24
2013
09
19
1940-6029
781
2011
Methods in molecular biology (Clifton, N.J.)
Methods Mol. Biol.
Visualizing gene-set enrichment results using the Cytoscape plug-in enrichment map.
257-77
10.1007/978-1-61779-276-2_12
Gene-set enrichment analysis finds functionally coherent gene-sets, such as pathways, that are statistically overrepresented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of -gene-sets used by many current enrichment analysis resources work against this ideal. "Enrichment Map" is a Cytoscape plug-in that helps overcome gene-set redundancy and aids in the interpretation of enrichment results. Gene-sets are organized in a network, where each set is a node and links represent gene overlap between sets. Automated network layout groups related gene-sets into -network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret enrichment results.
Merico
Daniele
D
Banting and Best Department of Medical Research, Centre for Cellular and Biomolecular Research (CCBR), Toronto, ON, Canada.
Isserlin
Ruth
R
Bader
Gary D
GD
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
United States
Methods Mol Biol
9214969
1064-3745
IM
Computational Biology
methods
Databases, Genetic
Gene Regulatory Networks
Software
2011
8
31
6
0
2011
8
31
6
0
2012
1
25
6
0
ppublish
10.1007/978-1-61779-276-2_12
21877285
21840481
2011
08
15
2011
10
13
2014
10
22
1878-3686
20
2
2011
Aug
16
Cancer cell
Cancer Cell
Delineation of two clinically and molecularly distinct subgroups of posterior fossa ependymoma.
143-57
10.1016/j.ccr.2011.07.007
Despite the histological similarity of ependymomas from throughout the neuroaxis, the disease likely comprises multiple independent entities, each with a distinct molecular pathogenesis. Transcriptional profiling of two large independent cohorts of ependymoma reveals the existence of two demographically, transcriptionally, genetically, and clinically distinct groups of posterior fossa (PF) ependymomas. Group A patients are younger, have laterally located tumors with a balanced genome, and are much more likely to exhibit recurrence, metastasis at recurrence, and death compared with Group B patients. Identification and optimization of immunohistochemical (IHC) markers for PF ependymoma subgroups allowed validation of our findings on a third independent cohort, using a human ependymoma tissue microarray, and provides a tool for prospective prognostication and stratification of PF ependymoma patients.
Copyright © 2011 Elsevier Inc. All rights reserved.
Witt
Hendrik
H
Division Molecular Genetics, German Cancer Research Center, 69120 Heidelberg, Germany.
Mack
Stephen C
SC
Ryzhova
Marina
M
Bender
Sebastian
S
Sill
Martin
M
Isserlin
Ruth
R
Benner
Axel
A
Hielscher
Thomas
T
Milde
Till
T
Remke
Marc
M
Jones
David T W
DT
Northcott
Paul A
PA
Garzia
Livia
L
Bertrand
Kelsey C
KC
Wittmann
Andrea
A
Yao
Yuan
Y
Roberts
Stephen S
SS
Massimi
Luca
L
Van Meter
Tim
T
Weiss
William A
WA
Gupta
Nalin
N
Grajkowska
Wiesia
W
Lach
Boleslaw
B
Cho
Yoon-Jae
YJ
von Deimling
Andreas
A
Kulozik
Andreas E
AE
Witt
Olaf
O
Bader
Gary D
GD
Hawkins
Cynthia E
CE
Tabori
Uri
U
Guha
Abhijit
A
Rutka
James T
JT
Lichter
Peter
P
Korshunov
Andrey
A
Taylor
Michael D
MD
Pfister
Stefan M
SM
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
R01 CA121941
CA
NCI NIH HHS
United States
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
Validation Studies
United States
Cancer Cell
101130617
1535-6108
IM
J Clin Oncol. 2001 Mar 1;19(5):1288-96
11230470
Cancer Res. 2011 May 15;71(10):3447-52
21270108
Br J Cancer. 2002 Mar 18;86(6):929-39
11953826
Bioinformatics. 2002;18 Suppl 1:S96-104
12169536
J Neurooncol. 2002 Jul;58(3):255-70
12187959
Clin Cancer Res. 2002 Oct;8(10):3054-64
12374672
Am J Pathol. 2002 Dec;161(6):2133-41
12466129
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9408757
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9589080
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16226707
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16199517
Clin Cancer Res. 2006 Apr 1;12(7 Pt 1):2070-9
16609018
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16642009
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16874765
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18781180
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19274783
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19289631
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19305393
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19531565
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21840477
Adult
Brain Neoplasms
classification
genetics
Chromosome Aberrations
Cranial Fossa, Posterior
pathology
Ependymoma
classification
genetics
Female
Gene Expression Profiling
Humans
Male
Middle Aged
NIHMS622647
PMC4154494
2011
2
15
2011
5
30
2011
7
11
2011
8
16
6
0
2011
8
16
6
0
2011
10
14
6
0
ppublish
S1535-6108(11)00262-5
10.1016/j.ccr.2011.07.007
21840481
PMC4154494
NIHMS622647
21742767
2011
07
11
2011
09
20
2015
02
09
1758-0463
2011
2011
Database : the journal of biological databases and curation
Database (Oxford)
A comprehensive manually curated reaction map of RANKL/RANK-signaling pathway.
bar021
10.1093/database/bar021
Receptor activator of nuclear factor-kappa B ligand (RANKL) is a member of tumor necrosis factor (TNF) superfamily that plays a key role in the regulation of differentiation, activation and survival of osteoclasts and also in tumor cell migration and bone metastasis. Osteoclast activation induced by RANKL regulates hematopoietic stem cell mobilization as part of homeostasis and host defense mechanisms thereby linking regulation of hematopoiesis with bone remodeling. Binding of RANKL to its receptor, Receptor activator of nuclear factor-kappa B (RANK) activates molecules such as NF-kappa B, mitogen activated protein kinase (MAPK), nuclear factor of activated T cells (NFAT) and phosphatidyl 3-kinase (PI3K). Although the molecular and cellular roles of these molecules have been reported previously, a systematic cataloging of the molecular events induced by RANKL/RANK interaction has not been attempted. Here, we present a comprehensive reaction map of the RANKL/RANK-signaling pathway based on an extensive manual curation of the published literature. We hope that the curated RANKL/RANK-signaling pathway model would enable new biomedical discoveries, which can provide novel insights into disease processes and development of novel therapeutic interventions.
Raju
Rajesh
R
Institute of Bioinformatics, International Technology Park, Bangalore 560066, India.
Balakrishnan
Lavanya
L
Nanjappa
Vishalakshi
V
Bhattacharjee
Mitali
M
Getnet
Derese
D
Muthusamy
Babylakshmi
B
Kurian Thomas
Joji
J
Sharma
Jyoti
J
Rahiman
B Abdul
BA
Harsha
H C
HC
Shankar
Subramanian
S
Prasad
T S Keshava
TS
Mohan
S Sujatha
SS
Bader
Gary D
GD
Wani
Mohan R
MR
Pandey
Akhilesh
A
eng
Wellcome Trust
United Kingdom
Journal Article
Research Support, Non-U.S. Gov't
2011
07
08
England
Database (Oxford)
101517697
1758-0463
0
RANK Ligand
0
Receptor Activator of Nuclear Factor-kappa B
0
TNFRSF11A protein, human
0
TNFSF11 protein, human
IM
Genome Res. 2003 Nov;13(11):2498-504
14597658
Nat Biotechnol. 2004 Feb;22(2):177-83
14755292
J Clin Invest. 2004 Aug;114(4):475-84
15314684
Cell. 1991 Feb 22;64(4):693-702
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16785428
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17485464
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17632511
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18025196
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18322009
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18382763
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12682046
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14530334
Am J Pathol. 2003 Nov;163(5):2021-31
14578201
Database Management Systems
Databases, Factual
Humans
RANK Ligand
genetics
metabolism
Receptor Activator of Nuclear Factor-kappa B
genetics
metabolism
Signal Transduction
PMC3170171
2011
2011
7
12
6
0
2011
7
12
6
0
2011
9
21
6
0
epublish
bar021
10.1093/database/bar021
21742767
PMC3170171
21716279
2011
06
30
2011
09
09
2014
10
22
1548-7105
8
7
2011
Jul
Nature methods
Nat. Methods
PSICQUIC and PSISCORE: accessing and scoring molecular interactions.
528-9
10.1038/nmeth.1637
Aranda
Bruno
B
Blankenburg
Hagen
H
Kerrien
Samuel
S
Brinkman
Fiona S L
FS
Ceol
Arnaud
A
Chautard
Emilie
E
Dana
Jose M
JM
De Las Rivas
Javier
J
Dumousseau
Marine
M
Galeota
Eugenia
E
Gaulton
Anna
A
Goll
Johannes
J
Hancock
Robert E W
RE
Isserlin
Ruth
R
Jimenez
Rafael C
RC
Kerssemakers
Jules
J
Khadake
Jyoti
J
Lynn
David J
DJ
Michaut
Magali
M
O'Kelly
Gavin
G
Ono
Keiichiro
K
Orchard
Sandra
S
Prieto
Carlos
C
Razick
Sabry
S
Rigina
Olga
O
Salwinski
Lukasz
L
Simonovic
Milan
M
Velankar
Sameer
S
Winter
Andrew
A
Wu
Guanming
G
Bader
Gary D
GD
Cesareni
Gianni
G
Donaldson
Ian M
IM
Eisenberg
David
D
Kleywegt
Gerard J
GJ
Overington
John
J
Ricard-Blum
Sylvie
S
Tyers
Mike
M
Albrecht
Mario
M
Hermjakob
Henning
H
eng
086151
Wellcome Trust
United Kingdom
R01 GM071909
GM
NIGMS NIH HHS
United States
R01 OD010929
OD
NIH HHS
United States
R01 RR024031
RR
NCRR NIH HHS
United States
R01 RR024031-05
RR
NCRR NIH HHS
United States
Letter
Research Support, Non-U.S. Gov't
2011
06
29
United States
Nat Methods
101215604
1548-7091
0
Proteins
IM
BMC Biol. 2007;5:44
17925023
Bioinformatics. 2009 May 15;25(10):1321-8
19420069
Proteomics. 2007 Oct;7(19):3436-40
17907277
Nat Biotechnol. 2004 Feb;22(2):177-83
14755292
Genome Res. 2003 Nov;13(11):2498-504
14597658
Animals
Computational Biology
Databases, Factual
Humans
Protein Binding
Proteins
chemistry
metabolism
Software
NIHMS345023
PMC3246345
2011
7
1
6
0
2011
7
1
6
0
2011
9
10
6
0
epublish
nmeth.1637
10.1038/nmeth.1637
21716279
PMC3246345
NIHMS345023
21525870
2011
04
28
2011
09
08
2014
08
20
1744-4292
7
2011
Apr
26
Molecular systems biology
Mol. Syst. Biol.
The multiple-specificity landscape of modular peptide recognition domains.
484
10.1038/msb.2011.18
Modular protein interaction domains form the building blocks of eukaryotic signaling pathways. Many of them, known as peptide recognition domains, mediate protein interactions by recognizing short, linear amino acid stretches on the surface of their cognate partners with high specificity. Residues in these stretches are usually assumed to contribute independently to binding, which has led to a simplified understanding of protein interactions. Conversely, we observe in large binding peptide data sets that different residue positions display highly significant correlations for many domains in three distinct families (PDZ, SH3 and WW). These correlation patterns reveal a widespread occurrence of multiple binding specificities and give novel structural insights into protein interactions. For example, we predict a new binding mode of PDZ domains and structurally rationalize it for DLG1 PDZ1. We show that multiple specificity more accurately predicts protein interactions and experimentally validate some of the predictions for the human proteins DLG1 and SCRIB. Overall, our results reveal a rich specificity landscape in peptide recognition domains, suggesting new ways of encoding specificity in protein interaction networks.
Gfeller
David
D
Banting and Best Department of Medical Research, The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Butty
Frank
F
Wierzbicka
Marta
M
Verschueren
Erik
E
Vanhee
Peter
P
Huang
Haiming
H
Ernst
Andreas
A
Dar
Nisa
N
Stagljar
Igor
I
Serrano
Luis
L
Sidhu
Sachdev S
SS
Bader
Gary D
GD
Kim
Philip M
PM
eng
MOP-84324
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
England
Mol Syst Biol
101235389
1744-4292
0
Adaptor Proteins, Signal Transducing
0
DLG1 protein, human
0
Membrane Proteins
0
SCRIB protein, human
0
Tumor Suppressor Proteins
IM
PLoS Biol. 2009 Oct;7(10):e1000218
19841731
Science. 2009 Jun 26;324(5935):1720-3
19443739
Nucleic Acids Res. 2010 Jan;38(Database issue):D167-80
19920119
J Mol Biol. 1999 Dec 17;294(5):1351-62
10600390
J Mol Biol. 2000 Jul 21;300(4):1005-16
10891285
J Cell Sci. 2001 Apr;114(Pt 7):1253-63
11256992
J Cell Sci. 2001 Sep;114(Pt 18):3219-31
11591811
Science. 2002 Jan 11;295(5553):321-4
11743162
Science. 2003 Apr 18;300(5618):445-52
12702867
Nucleic Acids Res. 2003 Jul 1;31(13):3635-41
12824383
Proteomics. 2004 Mar;4(3):643-55
14997488
Nucleic Acids Res. 2004;32(5):1792-7
15034147
J Mol Biol. 2004 Oct 22;343(3):703-18
15465056
Nature. 1994 Nov 24;372(6504):375-9
7802869
Science. 1994 Nov 18;266(5188):1241-7
7526465
Proc Int Conf Intell Syst Mol Biol. 1994;2:28-36
7584402
Cell. 1996 Mar 8;84(5):757-67
8625413
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9144168
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9545443
Protein Eng. 1999 Jan;12(1):3-9
10065704
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10221915
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Protein Sci. 2007 Apr;16(4):683-94
17384233
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17545975
Nat Med. 2007 Jul;13(7):862-7
17589520
Sci Signal. 2009;2(102):ra84
20029029
Acta Crystallogr Sect F Struct Biol Cryst Commun. 2009 Dec 1;65(Pt 12):1254-7
20054121
Sci Signal. 2010;3(104):ra3
20068231
Protein Sci. 2010 Apr;19(4):731-41
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20356847
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20595957
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20711194
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15475968
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15513994
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20714644
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20654621
BMC Bioinformatics. 2010;11:507
20939902
Bioinformatics. 2011 Feb 1;27(3):383-90
21127034
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J Biol Chem. 2008 Jan 25;283(4):1974-84
18048362
PLoS Comput Biol. 2008;4(8):e1000154
18725950
Sci Signal. 2008;1(35):ra2
18765831
Nat Biotechnol. 2008 Sep;26(9):1041-5
18711339
PLoS Biol. 2008 Sep 30;6(9):e239
18828675
Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31
19850723
Adaptor Proteins, Signal Transducing
chemistry
metabolism
Amino Acid Sequence
Animals
Binding Sites
Cluster Analysis
Humans
Membrane Proteins
chemistry
metabolism
Mice
Models, Molecular
Molecular Sequence Data
PDZ Domains
Protein Binding
Protein Interaction Mapping
Signal Transduction
Systems Biology
Tumor Suppressor Proteins
chemistry
metabolism
src Homology Domains
PMC3097085
2010
9
27
2011
3
11
2011
4
29
6
0
2011
4
29
6
0
2011
9
9
6
0
ppublish
msb201118
10.1038/msb.2011.18
21525870
PMC3097085
21473782
2011
04
28
2011
07
14
2013
09
19
1751-0473
6
2011
Source code for biology and medicine
Source Code Biol Med
WordCloud: a Cytoscape plugin to create a visual semantic summary of networks.
7
10.1186/1751-0473-6-7
When biological networks are studied, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To understand the biological meaning of a cluster, the user usually has to sift through many textual annotations that are associated with biological entities.
The WordCloud Cytoscape plugin generates a visual summary of these annotations by displaying them as a tag cloud, where more frequent words are displayed using a larger font size. Word co-occurrence in a phrase can be visualized by arranging words in clusters or as a network.
WordCloud provides a concise visual summary of annotations which is helpful for network analysis and interpretation. WordCloud is freely available at http://baderlab.org/Software/WordCloudPlugin.
Oesper
Layla
L
Department of Computer Science, Brown University, Providence, RI, USA. layla@cs.brown.edu.
Merico
Daniele
D
Isserlin
Ruth
R
Bader
Gary D
GD
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
2011
04
07
England
Source Code Biol Med
101276533
1751-0473
Nat Genet. 2000 May;25(1):25-9
10802651
Genome Res. 2003 Nov;13(11):2498-504
14597658
Nature. 2006 Mar 30;440(7084):637-43
16554755
Brief Bioinform. 2008 May;9(3):189-97
18202032
PLoS One. 2010;5(11):e13984
21085593
Nat Biotechnol. 2009 Oct;27(10):921-4
19816451
Bioinformatics. 2010 Feb 15;26(4):456-63
20007254
Proteomics. 2010 Mar;10(6):1316-27
20127684
Bioinformatics. 2009 Apr 15;25(8):1091-3
19237447
PMC3083346
2011
2
23
2011
4
7
2011
4
7
2011
4
9
6
0
2011
4
9
6
0
2011
4
9
6
1
epublish
1751-0473-6-7
10.1186/1751-0473-6-7
21473782
PMC3083346
21390331
2011
03
10
2011
06
22
2014
08
21
1553-7358
7
2
2011
Feb
PLoS computational biology
PLoS Comput. Biol.
Protein complexes are central in the yeast genetic landscape.
e1001092
10.1371/journal.pcbi.1001092
If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ∼5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.
Michaut
Magali
M
The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
Baryshnikova
Anastasia
A
Costanzo
Michael
M
Myers
Chad L
CL
Andrews
Brenda J
BJ
Boone
Charles
C
Bader
Gary D
GD
eng
P41 GM103504
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
2011
02
24
United States
PLoS Comput Biol
101238922
1553-734X
0
Saccharomyces cerevisiae Proteins
IM
Nat Genet. 2000 May;25(1):25-9
10802651
Genome Res. 2003 Nov;13(11):2498-504
14597658
Science. 2001 Dec 14;294(5550):2364-8
11743205
Nature. 2002 Jul 25;418(6896):387-91
12140549
Nat Genet. 2003 Nov;35(3):204-5
14593402
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14764870
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8341614
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15592468
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15877074
Trends Genet. 2005 Aug;21(8):424-7
15982781
Genome Res. 2005 Oct;15(10):1456-61
16169922
Cell. 2005 Nov 4;123(3):507-19
16269340
Mol Syst Biol. 2005;1:2005.0026
16729061
Nat Genet. 2006 Aug;38(8):896-903
16845399
BMC Genomics. 2006;7:187
16869964
Methods. 2007 Feb;41(2):206-21
17189863
Genome Biol. 2006;7(7):R63
16859555
Mol Syst Biol. 2007;3:104
17437029
Nat Rev Genet. 2007 Jun;8(6):437-49
17510664
BMC Bioinformatics. 2007;8:236
17605818
Nucleic Acids Res. 2008 Jan;36(Database issue):D577-81
17982175
Proc Natl Acad Sci U S A. 2008 Mar 4;105(9):3461-6
18305163
PLoS Comput Biol. 2008 Apr;4(4):e1000065
18421374
Science. 2008 Apr 18;320(5874):362-5
18420932
Genome Res. 2008 Jul;18(7):1092-9
18463300
Mol Syst Biol. 2008;4:209
18628749
Nat Methods. 2008 Aug;5(8):711-8
18622397
Bioinformatics. 2008 Oct 15;24(20):2376-83
18718945
Genome Biol. 2008;9(9):R135
18789146
Nucleic Acids Res. 2009 Jan;37(Database issue):D555-9
18948289
Nucleic Acids Res. 2009 Feb;37(3):825-31
19095691
Cell. 2009 Mar 6;136(5):952-63
19269370
PLoS One. 2009;4(4):e5364
19399174
FEBS Lett. 2009 Jun 5;583(11):1656-61
19351535
Mol Biosyst. 2009 Dec;5(12):1473-81
19763324
J Cell Biol. 2010 Jan 11;188(1):69-81
20065090
J Biol. 2007;6(3):8
17897480
Science. 2010 Jan 22;327(5964):425-31
20093466
Bioinformatics. 2010 Jun 15;26(12):i228-36
20529911
Mol Cell. 2010 Jun 25;38(6):916-28
20620961
Nat Methods. 2010 Dec;7(12):1017-24
21076421
Science. 2001 Feb 9;291(5506):1001-4
11232561
Computational Biology
Gene Regulatory Networks
Genes, Fungal
Models, Biological
Phenotype
Saccharomyces cerevisiae
genetics
metabolism
physiology
Saccharomyces cerevisiae Proteins
genetics
metabolism
physiology
Signal Transduction
PMC3044758
2010
8
1
2011
1
25
2011
2
24
2011
3
11
6
0
2011
3
11
6
0
2011
6
23
6
0
ppublish
10.1371/journal.pcbi.1001092
21390331
PMC3044758
21324131
2011
10
20
2012
02
15
2015
08
04
1474-760X
12
2
2011
Genome biology
Genome Biol.
Bringing order to protein disorder through comparative genomics and genetic interactions.
R14
10.1186/gb-2011-12-2-r14
Intrinsically disordered regions are widespread, especially in proteomes of higher eukaryotes. Recently, protein disorder has been associated with a wide variety of cellular processes and has been implicated in several human diseases. Despite its apparent functional importance, the sheer range of different roles played by protein disorder often makes its exact contribution difficult to interpret.
We attempt to better understand the different roles of disorder using a novel analysis that leverages both comparative genomics and genetic interactions. Strikingly, we find that disorder can be partitioned into three biologically distinct phenomena: regions where disorder is conserved but with quickly evolving amino acid sequences (flexible disorder); regions of conserved disorder with also highly conserved amino acid sequences (constrained disorder); and, lastly, non-conserved disorder. Flexible disorder bears many of the characteristics commonly attributed to disorder and is associated with signaling pathways and multi-functionality. Conversely, constrained disorder has markedly different functional attributes and is involved in RNA binding and protein chaperones. Finally, non-conserved disorder lacks clear functional hallmarks based on our analysis.
Our new perspective on protein disorder clarifies a variety of previous results by putting them into a systematic framework. Moreover, the clear and distinct functional association of flexible and constrained disorder will allow for new approaches and more specific algorithms for disorder detection in a functional context. Finally, in flexible disordered regions, we demonstrate clear evolutionary selection of protein disorder with little selection on primary structure, which has important implications for sequence-based studies of protein structure and evolution.
Bellay
Jeremy
J
Department of Computer Science and Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, MN 55455, USA.
Han
Sangjo
S
Michaut
Magali
M
Kim
Taehyung
T
Costanzo
Michael
M
Andrews
Brenda J
BJ
Boone
Charles
C
Bader
Gary D
GD
Myers
Chad L
CL
Kim
Philip M
PM
eng
1R01HG005084-01A1
HG
NHGRI NIH HHS
United States
P41 GM103504
GM
NIGMS NIH HHS
United States
R01 HG005084
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
2011
02
16
England
Genome Biol
100960660
1474-7596
0
Proteins
IM
J Mol Biol. 2003 Jul 25;330(5):979-92
12860121
Proc Natl Acad Sci U S A. 2002 Apr 30;99(9):5860-5
11972065
Genome Res. 2003 Nov;13(11):2498-504
14597658
Structure. 2003 Nov;11(11):1453-9
14604535
Nucleic Acids Res. 2004;32(3):1037-49
14960716
J Mol Biol. 2004 Mar 26;337(3):635-45
15019783
Nature. 2004 Jul 1;430(6995):88-93
15190252
FASEB J. 2004 Aug;18(11):1169-75
15284216
BMC Bioinformatics. 2004 Aug 19;5:113
15318951
Cell. 1998 Nov 25;95(5):717-28
9845373
Nucleic Acids Res. 2005;33(2):511-8
15661851
Nat Rev Mol Cell Biol. 2005 Mar;6(3):197-208
15738986
Mol Cell Proteomics. 2005 Mar;4(3):310-27
15665377
FEBS Lett. 2005 Jun 13;579(15):3346-54
15943980
Nature. 2006 Mar 30;440(7084):631-6
16429126
Nature. 2006 Mar 30;440(7084):637-43
16554755
J Proteome Res. 2006 Apr;5(4):879-87
16602695
J Proteome Res. 2006 Apr;5(4):888-98
16602696
Proc Natl Acad Sci U S A. 2006 May 30;103(22):8390-5
16717195
BMC Genomics. 2006;7:187
16869964
Bioinformatics. 2006 Dec 1;22(23):2890-7
17005538
Science. 2006 Dec 22;314(5807):1938-41
17185604
Biophys J. 2007 Mar 1;92(5):1439-56
17158572
Proc Natl Acad Sci U S A. 2007 Feb 13;104(7):2193-8
17287358
J Proteome Res. 2007 Mar;6(3):1190-7
17330950
Mol Biol Evol. 2007 Aug;24(8):1586-91
17483113
J Biol Chem. 2008 Mar 14;283(11):6886-96
18184659
Mol Cell. 2008 Mar 14;29(5):563-76
18342604
Mol Syst Biol. 2008;4:179
18364713
Science. 2008 Apr 18;320(5874):362-5
18420932
Mol Cell Proteomics. 2008 Jul;7(7):1389-96
18407956
BMC Genomics. 2008;9 Suppl 2:S1
18831774
Science. 2008 Oct 3;322(5898):104-10
18719252
Science. 2008 Nov 28;322(5906):1365-8
19039133
PLoS Biol. 2008 Nov 4;6(11):e264
18986213
Curr Opin Struct Biol. 2009 Feb;19(1):31-8
19157855
Nat Struct Mol Biol. 2009 Mar;16(3):287-93
19234467
Bioinformatics. 2009 May 1;25(9):1189-91
19151095
PLoS Comput Biol. 2009 Jun;5(6):e1000413
19521505
Biochem Cell Biol. 2010 Apr;88(2):167-74
20453919
Nat Biotechnol. 2002 Mar;20(3):301-5
11875433
Proteomics. 2010 Mar;10(6):1316-27
20127684
Mol Cell Biol. 2002 Oct;22(19):6750-8
12215532
Nucleic Acids Res. 2003 Jul 1;31(13):3635-41
12824383
Biochem Cell Biol. 2010 Apr;88(2):269-90
20453929
Mol Biol Evol. 2010 Sep;27(9):2027-37
20368267
PLoS Biol. 2009 Jun 16;7(6):e1000134
19547744
Cell. 2009 Jul 10;138(1):198-208
19596244
Proteins. 2009;77 Suppl 9:210-6
19774619
Science. 2010 Jan 22;327(5964):425-31
20093466
Bioinformatics. 2010 Mar 1;26(5):625-31
20081223
Proteins. 2003 Sep 1;52(4):573-84
12910457
Algorithms
Amino Acid Sequence
Conserved Sequence
Databases, Protein
Escherichia coli
Evolution, Molecular
Genomics
methods
Humans
Models, Statistical
Molecular Sequence Data
Protein Folding
Protein Structure, Tertiary
genetics
Proteins
chemistry
genetics
Transcriptome
PMC3188796
2010
9
29
2011
2
1
2011
2
16
2011
2
16
2011
2
18
6
0
2011
2
18
6
0
2012
2
16
6
0
ppublish
gb-2011-12-2-r14
10.1186/gb-2011-12-2-r14
21324131
PMC3188796
21307913
2011
02
10
2011
02
28
1476-4687
470
7333
2011
Feb
10
Nature
Nature
Too many roads not taken.
163-5
10.1038/470163a
Edwards
Aled M
AM
University of Toronto, Toronto, Ontario M5G 1L7, Canada. aled.edwards@utoronto.ca
Isserlin
Ruth
R
Bader
Gary D
GD
Frye
Stephen V
SV
Willson
Timothy M
TM
Yu
Frank H
FH
eng
Journal Article
England
Nature
0410462
0028-0836
0
Ion Channels
0
Receptors, Cytoplasmic and Nuclear
EC 2.7.-
Protein Kinases
IM
Bibliometrics
Biomedical Research
instrumentation
methods
statistics & numerical data
trends
Human Genome Project
Humans
Ion Channels
Protein Kinases
Receptors, Cytoplasmic and Nuclear
2011
2
11
6
0
2011
2
11
6
0
2011
3
1
6
0
ppublish
470163a
10.1038/470163a
21307913
21233089
2011
01
14
2011
05
03
2015
02
09
1758-0463
2011
2011
Database : the journal of biological databases and curation
Database (Oxford)
The Biomolecular Interaction Network Database in PSI-MI 2.5.
baq037
10.1093/database/baq037
The Biomolecular Interaction Network Database (BIND) is a major source of curated biomolecular interactions, which has been unmaintained for the last few years, a trend which will eventually result in the loss of a significant amount of unique biomolecular interaction information, mostly as database identifiers become out of date. To help reverse this trend, we converted BIND to a standard format, Proteomics Standard Initiative-Molecular Interaction 2.5, starting from the last curated data release (from 2005) available in a custom XML format and made the core components (interactions and complexes) plus additional valuable curated information available for download (http://download.baderlab.org/BINDTranslation/). Major work during the conversion process was required to update out of date molecule identifiers resulting in a more comprehensive conversion of BIND, by measures including number of species and interactor types covered, than what is currently accessible elsewhere. This work also highlights issues of data modeling, controlled vocabulary adoption and data cleaning that can serve as a general case study on the future compatibility of interaction databases. Database URL: http://download.baderlab.org/BINDTranslation/
Isserlin
Ruth
R
The Donnelly Centre, University of Toronto, ON, Canada.
El-Badrawi
Rashad A
RA
Bader
Gary D
GD
eng
P41 P41HG04118
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2011
01
12
England
Database (Oxford)
101517697
1758-0463
IM
Nat Genet. 2000 May;25(1):25-9
10802651
Bioinformatics. 2000 May;16(5):465-77
10871269
Nucleic Acids Res. 2001 Jan 1;29(1):242-5
11125103
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11449735
Tanpakushitsu Kakusan Koso. 2002 May;47(6):733-6
11995340
Biopolymers. 2001-2002;61(2):111-20
11987160
Nat Biotechnol. 2002 Oct;20(10):991-7
12355115
Nucleic Acids Res. 2003 Jan 1;31(1):248-50
12519993
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14597658
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14755292
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15117749
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15588483
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16381926
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16845013
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17130148
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17148479
Genome Biol. 2007;8(5):R95
17535438
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17687370
Nat Biotechnol. 2007 Sep;25(9):971
17846617
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18000002
BMC Syst Biol. 2007;1:58
18078503
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18467421
Mol Syst Biol. 2008;4:218
18766178
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18823568
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18940858
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19594875
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19834897
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19843607
Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31
19850723
Nat Biotechnol. 2010 Sep;28(9):935-42
20829833
Animals
Databases, Protein
Humans
Internet
Protein Binding
Proteomics
standards
Rats
Reproducibility of Results
Vocabulary, Controlled
PMC3021793
2011
2011
1
15
6
0
2011
1
15
6
0
2011
5
4
6
0
epublish
baq037
10.1093/database/baq037
21233089
PMC3021793
21127034
2011
02
01
2011
12
20
2014
08
21
1367-4811
27
3
2011
Feb
1
Bioinformatics (Oxford, England)
Bioinformatics
A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequence.
383-90
10.1093/bioinformatics/btq657
Predicting protein interactions involving peptide recognition domains is essential for understanding the many important biological processes they mediate. It is important to consider the binding strength of these interactions to help us construct more biologically relevant protein interaction networks that consider cellular context and competition between potential binders.
We developed a novel regression framework that considers both positive (quantitative) and negative (qualitative) interaction data available for mouse PDZ domains to quantitatively predict interactions between PDZ domains, a large peptide recognition domain family, and their peptide ligands using primary sequence information. First, we show that it is possible to learn from existing quantitative and negative interaction data to infer the relative binding strength of interactions involving previously unseen PDZ domains and/or peptides given their primary sequence. Performance was measured using cross-validated hold out testing and testing with previously unseen PDZ domain-peptide interactions. Second, we find that incorporating negative data improves quantitative interaction prediction. Third, we show that sequence similarity is an important prediction performance determinant, which suggests that experimentally collecting additional quantitative interaction data for underrepresented PDZ domain subfamilies will improve prediction.
The Matlab code for our SemiSVR predictor and all data used here are available at http://baderlab.org/Data/PDZAffinity.
Shao
Xiaojian
X
Department of Applied Mathematics, College of Science, China Agricultural University, Beijing, 100083, China.
Tan
Chris S H
CS
Voss
Courtney
C
Li
Shawn S C
SS
Deng
Naiyang
N
Bader
Gary D
GD
eng
MOP-84324
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2010
12
02
England
Bioinformatics
9808944
1367-4803
0
Ligands
0
Peptides
0
Proteins
IM
Nature. 2003 Dec 11;426(6967):676-80
14668868
Sci Signal. 2009;2(58):ra6
19224897
PLoS Biol. 2004 Jan;2(1):E14
14737190
Bioinformatics. 2000 Jan;16(1):16-23
10812473
Nat Biotechnol. 2001 Apr;19(4):348-53
11283593
Science. 2002 Jan 11;295(5553):321-4
11743162
Science. 2003 Apr 18;300(5618):445-52
12702867
Sci STKE. 2003 Apr 22;2003(179):RE7
12709532
Curr Opin Struct Biol. 2004 Apr;14(2):208-16
15093836
FEBS Lett. 2004 Jun 1;567(1):74-9
15165896
Science. 1999 Oct 8;286(5438):295-9
10514373
J Mol Biol. 2004 Nov 26;344(3):865-81
15533451
Proc Natl Acad Sci U S A. 2005 May 3;102(18):6395-400
15851683
Nature. 2006 Jan 12;439(7073):168-74
16273093
J Am Chem Soc. 2006 May 3;128(17):5913-22
16637659
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16845018
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16579851
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16870929
IEEE Trans Neural Netw. 2007 Jan;18(1):300-6
17278481
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17641200
Nature. 2007 Oct 18;449(7164):851-61
17943122
Bioinformatics. 2008 Feb 1;24(3):358-66
18083718
PLoS Comput Biol. 2008 Apr;4(4):e1000052
18389064
Mol Cell Proteomics. 2008 Apr;7(4):768-84
17956856
Protein Sci. 2009 Aug;18(8):1609-19
19569188
Nucleic Acids Res. 2009 Aug;37(14):4629-41
19502496
Cell. 2009 Aug 21;138(4):774-86
19703402
Sci Signal. 2009;2(87):ra50
19738200
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19744855
PLoS Biol. 2009 Oct;7(10):e1000218
19841731
BMC Bioinformatics. 2010;11:144
20298601
PLoS Comput Biol. 2010 May;6(5):e1000789
20502673
J Mol Biol. 2010 Sep 17;402(2):460-74
20654621
PLoS Comput Biol. 2008;4(7):e1000107
18604266
Nat Biotechnol. 2008 Sep;26(9):1041-5
18711339
Biochemistry. 2008 Sep 23;47(38):10084-98
18754678
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18828675
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18953355
Bioinformatics. 2009 Jan 1;25(1):83-9
18996943
Proteomics. 2004 Mar;4(3):643-55
14997488
Animals
Computational Biology
methods
Ligands
Mice
Models, Molecular
Mutation
PDZ Domains
Peptides
chemistry
genetics
metabolism
Protein Binding
Protein Structure, Tertiary
Proteins
chemistry
metabolism
Regression Analysis
Reproducibility of Results
PMC3031032
2010
12
2
2010
12
4
6
0
2010
12
4
6
0
2011
12
21
6
0
ppublish
btq657
10.1093/bioinformatics/btq657
21127034
PMC3031032
21085593
2010
11
18
2011
04
27
2014
08
21
1932-6203
5
11
2010
PloS one
PLoS ONE
Enrichment map: a network-based method for gene-set enrichment visualization and interpretation.
e13984
10.1371/journal.pone.0013984
Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal.
To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed "Enrichment Map", a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results.
Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).
Merico
Daniele
D
Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.
Isserlin
Ruth
R
Stueker
Oliver
O
Emili
Andrew
A
Bader
Gary D
GD
eng
P41P41HG04118
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2010
11
15
United States
PLoS One
101285081
1932-6203
0
Estrogens
IM
Cancer Epidemiol Biomarkers Prev. 2006 Dec;15(12):2422-6
17164365
BMC Bioinformatics. 2006;7:426
17018143
Bioinformatics. 2007 Feb 15;23(4):401-7
17182697
Clin Cancer Res. 2007 Feb 15;13(4):1107-14
17317818
PLoS One. 2007;2(5):e425
17487280
Neoplasia. 2007 May;9(5):443-54
17534450
PLoS Genet. 2007 Jun;3(6):e87
17542648
Int J Colorectal Dis. 2007 Oct;22(10):1185-94
17483957
Cancer Lett. 2007 Oct 28;256(2):137-65
17629396
Nat Methods. 2007 Oct;4(10):787-97
17901868
Genome Res. 2007 Oct;17(10):1537-45
17785539
Nat Protoc. 2007;2(10):2366-82
17947979
Bioinformatics. 2007 Nov 15;23(22):3024-31
17848398
Genome Biol. 2007;8(7):R131
17615082
Genome Biol. 2007;8(9):R183
17784955
Adv Exp Med Biol. 2007;623:64-84
18380341
Nat Genet. 2000 May;25(1):25-9
10802651
Nat Rev Genet. 2001 Jun;2(6):418-27
11389458
Genomics. 2002 Feb;79(2):266-70
11829497
Bioinformatics. 2002;18 Suppl 1:S233-40
12169552
Genomics. 2003 Feb;81(2):98-104
12620386
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12734009
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12790780
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12824416
BMC Bioinformatics. 2002 Nov 13;3:35
12431279
Int J Oncol. 2004 May;24(5):1279-88
15067352
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15461798
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15468760
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9847135
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10391217
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15603755
Brief Bioinform. 2008 May;9(3):189-97
18202032
Nat Genet. 2008 May;40(5):499-507
18443585
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19131956
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Proteomics. 2010 Mar;10(6):1316-27
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20531469
Nat Biotechnol. 2010 Sep;28(9):935-42
20829833
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32
15608231
Clin Colorectal Cancer. 2005 Jan;4(5):302-12
15663833
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15763557
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16369572
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16606683
Cancer Res. 2006 Sep 15;66(18):8927-30
16982728
Bioinformatics. 2007 Jan 15;23(2):257-8
17098774
Algorithms
Breast Neoplasms
genetics
Cluster Analysis
Colonic Neoplasms
genetics
Computational Biology
methods
Estrogens
pharmacology
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
drug effects
Gene Regulatory Networks
Humans
Internet
Reproducibility of Results
Software
PMC2981572
2010
2
2
2010
10
20
2010
11
15
2010
11
19
6
0
2010
11
19
6
0
2011
4
28
6
0
epublish
10.1371/journal.pone.0013984
21085593
PMC2981572
21078182
2010
12
08
2011
02
17
2014
08
21
1471-2105
11
2010
BMC bioinformatics
BMC Bioinformatics
An improved method for scoring protein-protein interactions using semantic similarity within the gene ontology.
562
10.1186/1471-2105-11-562
Semantic similarity measures are useful to assess the physiological relevance of protein-protein interactions (PPIs). They quantify similarity between proteins based on their function using annotation systems like the Gene Ontology (GO). Proteins that interact in the cell are likely to be in similar locations or involved in similar biological processes compared to proteins that do not interact. Thus the more semantically similar the gene function annotations are among the interacting proteins, more likely the interaction is physiologically relevant. However, most semantic similarity measures used for PPI confidence assessment do not consider the unequal depth of term hierarchies in different classes of cellular location, molecular function, and biological process ontologies of GO and thus may over-or under-estimate similarity.
We describe an improved algorithm, Topological Clustering Semantic Similarity (TCSS), to compute semantic similarity between GO terms annotated to proteins in interaction datasets. Our algorithm, considers unequal depth of biological knowledge representation in different branches of the GO graph. The central idea is to divide the GO graph into sub-graphs and score PPIs higher if participating proteins belong to the same sub-graph as compared to if they belong to different sub-graphs.
The TCSS algorithm performs better than other semantic similarity measurement techniques that we evaluated in terms of their performance on distinguishing true from false protein interactions, and correlation with gene expression and protein families. We show an average improvement of 4.6 times the F1 score over Resnik, the next best method, on our Saccharomyces cerevisiae PPI dataset and 2 times on our Homo sapiens PPI dataset using cellular component, biological process and molecular function GO annotations.
Jain
Shobhit
S
Department of Computer Science, University of Toronto, 10 Kings College Road, Toronto, Ontario M5S3G4, Canada.
Bader
Gary D
GD
eng
MOP-84324
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2010
11
15
England
BMC Bioinformatics
100965194
1471-2105
0
Proteins
IM
Nucleic Acids Res. 2000 Jan 1;28(1):289-91
10592249
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W214-20
20576703
Nat Genet. 2000 May;25(1):25-9
10802651
EMBO J. 2000 Nov 1;19(21):5824-34
11060033
Nature. 2002 May 16;417(6886):304-8
11979277
Mol Cell Proteomics. 2002 May;1(5):349-56
12118076
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15468759
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9271392
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17044170
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10688190
Algorithms
Databases, Protein
Humans
Molecular Sequence Annotation
Protein Interaction Mapping
methods
Proteins
chemistry
genetics
metabolism
Semantics
PMC2998529
2010
7
9
2010
11
15
2010
11
15
2010
11
17
6
0
2010
11
17
6
0
2011
2
18
6
0
epublish
1471-2105-11-562
10.1186/1471-2105-11-562
21078182
PMC2998529
21076421
2010
11
30
2010
12
30
2014
09
17
1548-7105
7
12
2010
Dec
Nature methods
Nat. Methods
Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.
1017-24
10.1038/nmeth.1534
Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.
Baryshnikova
Anastasia
A
Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada. brenda.andrews@utoronto.ca
Costanzo
Michael
M
Kim
Yungil
Y
Ding
Huiming
H
Koh
Judice
J
Toufighi
Kiana
K
Youn
Ji-Young
JY
Ou
Jiongwen
J
San Luis
Bryan-Joseph
BJ
Bandyopadhyay
Sunayan
S
Hibbs
Matthew
M
Hess
David
D
Gingras
Anne-Claude
AC
Bader
Gary D
GD
Troyanskaya
Olga G
OG
Brown
Grant W
GW
Andrews
Brenda
B
Boone
Charles
C
Myers
Chad L
CL
eng
(MOP-79368
Canadian Institutes of Health Research
Canada
1R01HG005084-01A1
HG
NHGRI NIH HHS
United States
GSP-415-67
Canadian Institutes of Health Research
Canada
R01 HG005084
HG
NHGRI NIH HHS
United States
R01 HG005084-01A1
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
2010
11
14
United States
Nat Methods
101215604
1548-7091
IM
Trends Genet. 1992 Sep;8(9):312-6
1365397
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15821735
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15879523
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15716499
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16406524
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16429126
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16554755
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16869964
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18474868
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18467557
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18622397
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18719252
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18818364
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18931302
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18782753
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19763343
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20946810
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14764870
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7678255
Algorithms
Gene Expression Regulation, Fungal
Genetic Fitness
Genome, Fungal
Genome-Wide Association Study
methods
Mutagenesis
Mutation
Oligonucleotide Array Sequence Analysis
methods
Ultraviolet Rays
Yeasts
genetics
radiation effects
NIHMS291951
PMC3117325
2010
7
28
2010
10
14
2010
11
14
2010
11
16
6
0
2010
11
16
6
0
2010
12
31
6
0
ppublish
nmeth.1534
10.1038/nmeth.1534
21076421
PMC3117325
NIHMS291951
21071392
2010
12
23
2011
04
28
2014
08
21
1362-4962
39
Database issue
2011
Jan
Nucleic acids research
Nucleic Acids Res.
Pathway Commons, a web resource for biological pathway data.
D685-90
10.1093/nar/gkq1039
Pathway Commons (http://www.pathwaycommons.org) is a collection of publicly available pathway data from multiple organisms. Pathway Commons provides a web-based interface that enables biologists to browse and search a comprehensive collection of pathways from multiple sources represented in a common language, a download site that provides integrated bulk sets of pathway information in standard or convenient formats and a web service that software developers can use to conveniently query and access all data. Database providers can share their pathway data via a common repository. Pathways include biochemical reactions, complex assembly, transport and catalysis events and physical interactions involving proteins, DNA, RNA, small molecules and complexes. Pathway Commons aims to collect and integrate all public pathway data available in standard formats. Pathway Commons currently contains data from nine databases with over 1400 pathways and 687,000 interactions and will be continually expanded and updated.
Cerami
Ethan G
EG
Computational Biology Center, Memorial Sloan-Kettering Cancer Center 1275 York Avenue, Box 460, New York, NY 10065, USA.
Gross
Benjamin E
BE
Demir
Emek
E
Rodchenkov
Igor
I
Babur
Ozgün
O
Anwar
Nadia
N
Schultz
Nikolaus
N
Bader
Gary D
GD
Sander
Chris
C
eng
1T32 GM083937
GM
NIGMS NIH HHS
United States
2R01GM070743-06
GM
NIGMS NIH HHS
United States
P41HG004118
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2010
11
10
England
Nucleic Acids Res
0411011
0305-1048
IM
Nat Genet. 2000 May;25(1):25-9
10802651
Proc Natl Acad Sci U S A. 2003 Apr 15;100(8):4372-6
12676999
Genome Res. 2003 Nov;13(11):2498-504
14597658
Nat Biotechnol. 2004 Jan;22(1):78-85
14704708
Genome Biol. 2005;6(1):R2
15642094
Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
16199517
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D187-91
16381842
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6
16381921
BMC Bioinformatics. 2006;7:497
17101041
Mol Syst Biol. 2007;3:140
17940530
BMC Biol. 2007;5:44
17925023
Nucleic Acids Res. 2008 Jan;36(Database issue):D637-40
18000002
Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9
18832364
Nucleic Acids Res. 2009 Jan;37(Database issue):D5-15
18940862
Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22
18981052
Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72
18988627
Nucleic Acids Res. 2009 Jan;37(1):1-13
19033363
Nat Biotechnol. 2009 Aug;27(8):735-41
19668183
BMC Bioinformatics. 2009;10:376
19917102
Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31
19850723
Nucleic Acids Res. 2010 Jan;38(Database issue):D532-9
19897547
Bioinformatics. 2010 Feb 1;26(3):429-31
20007251
PLoS One. 2010;5(2):e8918
20169195
Bioinformatics. 2010 Sep 15;26(18):2347-8
20656902
Databases, Factual
Databases, Genetic
Databases, Protein
Disease
classification
Genomics
Internet
Models, Biological
Systems Integration
User-Computer Interface
PMC3013659
2010
11
10
2010
11
13
6
0
2010
11
13
6
0
2011
4
29
6
0
ppublish
gkq1039
10.1093/nar/gkq1039
21071392
PMC3013659
20939902
2010
11
02
2011
03
24
2014
08
21
1471-2105
11
2010
BMC bioinformatics
BMC Bioinformatics
Proteome scanning to predict PDZ domain interactions using support vector machines.
507
10.1186/1471-2105-11-507
PDZ domains mediate protein-protein interactions involved in important biological processes through the recognition of short linear motifs in their target proteins. Two recent independent studies have used protein microarray or phage display technology to detect PDZ domain interactions with peptide ligands on a large scale. Several computational predictors of PDZ domain interactions have been developed, however they are trained using only protein microarray data and focus on limited subsets of PDZ domains. An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require an accurate and precise predictor to avoid generating too many false positive hits given the large amount of possible interactors in a given proteome. Once validated these predictions will help to increase the coverage of current PDZ domain interaction networks and further our understanding of the roles that PDZ domains play in a variety of biological processes.
We developed a PDZ domain interaction predictor using a support vector machine (SVM) trained with both protein microarray and phage display data. In order to use the phage display data for training, which only contains positive interactions, we developed a method to generate artificial negative interactions. Using cross-validation and a series of independent tests, we showed that our SVM successfully predicts interactions in different organisms. We then used the SVM to scan the proteomes of human, worm and fly to predict binders for several PDZ domains. Predictions were validated using known genomic interactions and published protein microarray experiments. Based on our results, new protein interactions potentially associated with Usher and Bardet-Biedl syndromes were predicted. A comparison of performance measures (F1 measure and FPR) for the SVM and published predictors demonstrated our SVM's improved accuracy and precision at proteome scanning.
We built an SVM using mouse and human experimental training data to predict PDZ domain interactions. We showed that it correctly predicts known interactions from proteomes of different organisms and is more accurate and precise at proteome scanning compared with published state-of-the-art predictors.
Hui
Shirley
S
Donnelly Center for Cellular and Biomolecular Research, Banting and Best Department of Medical Research, University of Toronto, Toronto ON, Canada.
Bader
Gary D
GD
eng
MOP-84324
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2010
10
12
England
BMC Bioinformatics
100965194
1471-2105
0
Proteins
0
Proteome
IM
J Clin Invest. 1999 Nov;104(10):1353-61
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19533032
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19502496
Nat Biotechnol. 2001 Apr;19(4):348-53
11283593
Animals
Artificial Intelligence
Binding Sites
Humans
Mice
PDZ Domains
Protein Array Analysis
methods
Protein Interaction Mapping
methods
Proteins
chemistry
metabolism
Proteome
chemistry
metabolism
PMC2967561
2010
5
12
2010
10
12
2010
10
12
2010
10
14
6
0
2010
10
14
6
0
2011
3
25
6
0
epublish
1471-2105-11-507
10.1186/1471-2105-11-507
20939902
PMC2967561
20926419
2010
11
04
2011
02
16
2014
08
21
1367-4811
26
22
2010
Nov
15
Bioinformatics (Oxford, England)
Bioinformatics
GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop.
2927-8
10.1093/bioinformatics/btq562
The GeneMANIA Cytoscape plugin brings fast gene function prediction capabilities to the desktop. GeneMANIA identifies the most related genes to a query gene set using a guilt-by-association approach. The plugin uses over 800 networks from six organisms and each related gene is traceable to the source network used to make the prediction. Users may add their own interaction networks and expression profile data to complement or override the default data.
The GeneMANIA Cytoscape plugin is implemented in Java and is freely available at http://www.genemania.org/plugin/.
Montojo
J
J
Banting and Best Department of Medical Research, The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON, M5S 3E1, Canada.
Zuberi
K
K
Rodriguez
H
H
Kazi
F
F
Wright
G
G
Donaldson
S L
SL
Morris
Q
Q
Bader
G D
GD
eng
Journal Article
Research Support, Non-U.S. Gov't
2010
10
05
England
Bioinformatics
9808944
1367-4803
IM
Genome Res. 2003 Nov;13(11):2498-504
14597658
Bioinformatics. 2005 May 1;21(9):2076-82
15657099
Nucleic Acids Res. 2008 Jan;36(Database issue):D637-40
18000002
Genome Biol. 2008;9 Suppl 1:S2
18613946
Bioinformatics. 2010 Jul 15;26(14):1759-65
20507895
Nucleic Acids Res. 2009 Jan;37(Database issue):D885-90
18940857
PLoS Med. 2009 Apr 7;6(4):e1000046
19360088
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W214-20
20576703
Genome Biol. 2008;9 Suppl 1:S4
18613948
Algorithms
Computational Biology
methods
Databases, Factual
Gene Regulatory Networks
Genes
Software
PMC2971582
2010
10
5
2010
10
8
6
0
2010
10
12
6
0
2011
2
17
6
0
ppublish
btq562
10.1093/bioinformatics/btq562
20926419
PMC2971582
20924352
2010
10
06
2011
01
19
2014
08
21
1744-4292
6
2010
Oct
5
Molecular systems biology
Mol. Syst. Biol.
Dynamic interaction networks in a hierarchically organized tissue.
417
10.1038/msb.2010.71
Intercellular (between cell) communication networks maintain homeostasis and coordinate regenerative and developmental cues in multicellular organisms. Despite the importance of intercellular networks in stem cell biology, their rules, structure and molecular components are poorly understood. Herein, we describe the structure and dynamics of intercellular and intracellular networks in a stem cell derived, hierarchically organized tissue using experimental and theoretical analyses of cultured human umbilical cord blood progenitors. By integrating high-throughput molecular profiling, database and literature mining, mechanistic modeling, and cell culture experiments, we show that secreted factor-mediated intercellular communication networks regulate blood stem cell fate decisions. In particular, self-renewal is modulated by a coupled positive-negative intercellular feedback circuit composed of megakaryocyte-derived stimulatory growth factors (VEGF, PDGF, EGF, and serotonin) versus monocyte-derived inhibitory factors (CCL3, CCL4, CXCL10, TGFB2, and TNFSF9). We reconstruct a stem cell intracellular network, and identify PI3K, Raf, Akt, and PLC as functionally distinct signal integration nodes, linking extracellular, and intracellular signaling. This represents the first systematic characterization of how stem cell fate decisions are regulated non-autonomously through lineage-specific interactions with differentiated progeny.
Kirouac
Daniel C
DC
Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
Ito
Caryn
C
Csaszar
Elizabeth
E
Roch
Aline
A
Yu
Mei
M
Sykes
Edward A
EA
Bader
Gary D
GD
Zandstra
Peter W
PW
eng
Journal Article
Research Support, Non-U.S. Gov't
England
Mol Syst Biol
101235389
1744-4292
0
Intercellular Signaling Peptides and Proteins
IM
Bone Marrow Transplant. 2001 May;27(10):1075-80
11438824
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19035792
J Exp Med. 2001 Oct 1;194(7):941-52
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12028051
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12091880
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12540299
Blood. 1990 Jan 1;75(1):96-101
2403823
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8867723
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9012841
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9843981
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11535512
Analysis of Variance
Cell Communication
physiology
Cell Differentiation
physiology
Cells, Cultured
Cluster Analysis
Computational Biology
methods
Computer Simulation
Data Mining
Fetal Blood
cytology
Gene Expression Profiling
Gene Regulatory Networks
Hematopoietic Stem Cells
cytology
physiology
Humans
Intercellular Signaling Peptides and Proteins
physiology
Linear Models
Models, Biological
Signal Transduction
PMC2990637
2010
3
29
2010
7
27
2010
10
7
6
0
2010
10
7
6
0
2011
1
20
6
0
ppublish
msb201071
10.1038/msb.2010.71
20924352
PMC2990637
20868367
2010
11
26
2011
01
06
2013
04
05
1470-8728
432
3
2010
Dec
15
The Biochemical journal
Biochem. J.
Functional complexes between YAP2 and ZO-2 are PDZ domain-dependent, and regulate YAP2 nuclear localization and signalling.
461-72
10.1042/BJ20100870
The Hippo pathway regulates the size of organs by controlling two opposing processes: proliferation and apoptosis. YAP2 (Yes kinase-associated protein 2), one of the three isoforms of YAP, is a WW domain-containing transcriptional co-activator that acts as the effector of the Hippo pathway in mammalian cells. In addition to WW domains, YAP2 has a PDZ-binding motif at its C-terminus. We reported previously that this motif was necessary for YAP2 localization in the nucleus and for promoting cell detachment and apoptosis. In the present study, we show that the tight junction protein ZO (zonula occludens)-2 uses its first PDZ domain to form a complex with YAP2. The endogenous ZO-2 and YAP2 proteins co-localize in the nucleus. We also found that ZO-2 facilitates the nuclear localization and pro-apoptotic function of YAP2, and that this activity of ZO-2 is PDZ-domain-dependent. The present paper is the first report on a PDZ-based nuclear translocation mechanism. Moreover, since the Hippo pathway acts as a tumour suppressor pathway, the YAP2-ZO-2 complex could represent a target for cancer therapy.
Oka
Tsutomu
T
Weis Center for Research, 100 North Academy Avenue, Danville, PA 17822, USA.
Remue
Eline
E
Meerschaert
Kris
K
Vanloo
Berlinda
B
Boucherie
Ciska
C
Gfeller
David
D
Bader
Gary D
GD
Sidhu
Sachdev S
SS
Vandekerckhove
Joël
J
Gettemans
Jan
J
Sudol
Marius
M
eng
MOP-84324
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
England
Biochem J
2984726R
0264-6021
0
Adaptor Proteins, Signal Transducing
0
Membrane Proteins
0
Mutant Proteins
0
Phosphoproteins
0
Recombinant Fusion Proteins
0
TJP1 protein, human
0
TJP2 protein, human
0
YAP1 (Yes-associated) protein, human
0
Zonula Occludens-1 Protein
0
Zonula Occludens-2 Protein
IM
Adaptor Proteins, Signal Transducing
chemistry
genetics
metabolism
Animals
Cell Adhesion
Cell Line
Cell Nucleus
metabolism
Cell Proliferation
Dogs
Genes, Reporter
HEK293 Cells
Humans
Immunoprecipitation
Membrane Proteins
chemistry
genetics
metabolism
Mutant Proteins
chemistry
metabolism
PDZ Domains
Phosphoproteins
chemistry
genetics
metabolism
Protein Transport
RNA Interference
Recombinant Fusion Proteins
chemistry
metabolism
Signal Transduction
Transfection
Zonula Occludens-1 Protein
Zonula Occludens-2 Protein
2010
9
28
6
0
2010
9
28
6
0
2011
1
7
6
0
ppublish
BJ20100870
10.1042/BJ20100870
20868367
20829833
2010
09
10
2010
12
22
2015
04
06
1546-1696
28
9
2010
Sep
Nature biotechnology
Nat. Biotechnol.
The BioPAX community standard for pathway data sharing.
935-42
10.1038/nbt.1666
Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.
Demir
Emek
E
Computational Biology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Cary
Michael P
MP
Paley
Suzanne
S
Fukuda
Ken
K
Lemer
Christian
C
Vastrik
Imre
I
Wu
Guanming
G
D'Eustachio
Peter
P
Schaefer
Carl
C
Luciano
Joanne
J
Schacherer
Frank
F
Martinez-Flores
Irma
I
Hu
Zhenjun
Z
Jimenez-Jacinto
Veronica
V
Joshi-Tope
Geeta
G
Kandasamy
Kumaran
K
Lopez-Fuentes
Alejandra C
AC
Mi
Huaiyu
H
Pichler
Elgar
E
Rodchenkov
Igor
I
Splendiani
Andrea
A
Tkachev
Sasha
S
Zucker
Jeremy
J
Gopinath
Gopal
G
Rajasimha
Harsha
H
Ramakrishnan
Ranjani
R
Shah
Imran
I
Syed
Mustafa
M
Anwar
Nadia
N
Babur
Ozgün
O
Blinov
Michael
M
Brauner
Erik
E
Corwin
Dan
D
Donaldson
Sylva
S
Gibbons
Frank
F
Goldberg
Robert
R
Hornbeck
Peter
P
Luna
Augustin
A
Murray-Rust
Peter
P
Neumann
Eric
E
Ruebenacker
Oliver
O
Reubenacker
Oliver
O
Samwald
Matthias
M
van Iersel
Martijn
M
Wimalaratne
Sarala
S
Allen
Keith
K
Braun
Burk
B
Whirl-Carrillo
Michelle
M
Cheung
Kei-Hoi
KH
Dahlquist
Kam
K
Finney
Andrew
A
Gillespie
Marc
M
Glass
Elizabeth
E
Gong
Li
L
Haw
Robin
R
Honig
Michael
M
Hubaut
Olivier
O
Kane
David
D
Krupa
Shiva
S
Kutmon
Martina
M
Leonard
Julie
J
Marks
Debbie
D
Merberg
David
D
Petri
Victoria
V
Pico
Alex
A
Ravenscroft
Dean
D
Ren
Liya
L
Shah
Nigam
N
Sunshine
Margot
M
Tang
Rebecca
R
Whaley
Ryan
R
Letovksy
Stan
S
Buetow
Kenneth H
KH
Rzhetsky
Andrey
A
Schachter
Vincent
V
Sobral
Bruno S
BS
Dogrusoz
Ugur
U
McWeeney
Shannon
S
Aladjem
Mirit
M
Birney
Ewan
E
Collado-Vides
Julio
J
Goto
Susumu
S
Hucka
Michael
M
Le Novère
Nicolas
N
Maltsev
Natalia
N
Pandey
Akhilesh
A
Thomas
Paul
P
Wingender
Edgar
E
Karp
Peter D
PD
Sander
Chris
C
Bader
Gary D
GD
eng
1R13GM076939
GM
NIGMS NIH HHS
United States
BB/F010516/1
Biotechnology and Biological Sciences Research Council
United Kingdom
P30 CA069533
CA
NCI NIH HHS
United States
P41 HG004118
HG
NHGRI NIH HHS
United States
P41 HG004118-01A1
HG
NHGRI NIH HHS
United States
P41HG004118
HG
NHGRI NIH HHS
United States
R01 GM070923
GM
NIGMS NIH HHS
United States
R01 GM071962
GM
NIGMS NIH HHS
United States
R01 RR022971
RR
NCRR NIH HHS
United States
R01GM071962-07
GM
NIGMS NIH HHS
United States
R13 GM076939
GM
NIGMS NIH HHS
United States
R13 GM076939-01
GM
NIGMS NIH HHS
United States
U24 GM077678
GM
NIGMS NIH HHS
United States
U24 GM077678-18
GM
NIGMS NIH HHS
United States
U54 AI081680
AI
NIAID NIH HHS
United States
U54 HG004028
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
2010
09
09
United States
Nat Biotechnol
9604648
1087-0156
IM
Nucleic Acids Res. 2007 Jan;35(Database issue):D247-52
17130144
Nucleic Acids Res. 2007 Jan;35(Database issue):D572-4
17135203
Nucleic Acids Res. 2007 Jan;35(Database issue):D561-5
17145710
Nucleic Acids Res. 2007 Jan;35(Database issue):D5-12
17170002
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W625-32
17586824
Mol Syst Biol. 2007;3:140
17940530
Nat Protoc. 2007;2(10):2366-82
17947979
BMC Biol. 2007;5:44
17925023
Nucleic Acids Res. 2008 Jan;36(Database issue):D120-4
18158297
BMC Syst Biol. 2007;1:58
18078503
Bioinformatics. 2008 Mar 15;24(6):876-7
18024474
PLoS Biol. 2008 Jul 22;6(7):e184
18651794
PLoS One. 2008;3(8):e2901
18682855
Nature. 2008 Oct 23;455(7216):1061-8
18772890
IET Syst Biol. 2008 Sep;2(5):352-62
19045830
Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9
18832364
Nucleic Acids Res. 2009 Jan;37(Database issue):D464-70
18974181
Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22
18981052
Nucleic Acids Res. 2009 Jan;37(1):1-13
19033363
Nat Biotechnol. 2009 Aug;27(8):735-41
19668183
Bioinformatics. 2009 Dec 15;25(24):3327-9
19837718
Nucleic Acids Res. 2010 Jan;38(Database issue):D473-9
19850718
Science. 2010 Jan 22;327(5964):425-31
20093466
Nat Genet. 2000 May;25(1):25-9
10802651
Bioinformatics. 2000 Mar;16(3):269-85
10869020
Bioinformatics. 2000 May;16(5):465-77
10871269
Nature. 2001 Apr 26;410(6832):1023-4
11323639
IUBMB Life. 2000 Dec;50(6):341-4
11327304
Bioinformatics. 2001 Sep;17(9):829-37
11590099
Nucleic Acids Res. 2002 Jan 1;30(1):303-5
11752321
Science. 2002 Mar 1;295(5560):1669-78
11872831
FEBS Lett. 2002 Feb 20;513(1):135-40
11911893
Nat Rev Cancer. 2002 Jul;2(7):489-501
12094235
Bioinformatics. 2002 Jul;18(7):996-1003
12117798
Bioinformatics. 2003 Mar 1;19(4):524-31
12611808
Genome Biol. 2003;4(3):R23
12620108
Nucleic Acids Res. 2003 Jul 1;31(13):3784-8
12824418
Genome Res. 2003 Oct;13(10):2363-71
14525934
Genome Res. 2003 Nov;13(11):2498-504
14597658
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D277-80
14681412
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D449-51
14681454
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D452-5
14681455
OMICS. 2003 Winter;7(4):355-72
14683609
Nat Biotechnol. 2004 Feb;22(2):177-83
14755292
Bioinformatics. 2004 Feb 12;20(3):349-56
14960461
BMC Bioinformatics. 2004 Feb 19;5:17
15028117
Prog Biophys Mol Biol. 2004 Jun-Jul;85(2-3):433-50
15142756
Bioinformatics. 2004 Aug 4;20 Suppl 1:i257-64
15262807
Mol Biol Cell. 1999 Aug;10(8):2703-34
10436023
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D284-8
15608197
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32
15608231
Genome Biol. 2005;6(1):R2
15642094
Nucleic Acids Res. 2005;33(4):1399-409
15745999
FEBS Lett. 2005 Mar 21;579(8):1815-20
15763557
Bioinformatics. 2005 May 1;21(9):2076-82
15657099
Sci STKE. 2005 May 10;2005(283):pe21
15886388
Genome Biol. 2005;6(5):R44
15892872
Nat Biotechnol. 2005 Aug;23(8):961-6
16082367
Nucleic Acids Res. 2005;33(19):6083-9
16246909
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D108-10
16381825
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D436-41
16381906
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6
16381921
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9
16381927
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D546-51
16381929
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D689-91
16381960
Bioinformatics. 2006 Jan 15;22(2):233-41
16278238
Sci STKE. 2006 Jul 17;2006(344):re6
16849649
BMC Bioinformatics. 2006;7:196
16603083
Bioinformatics. 2006 Jul 15;22(14):e271-80
16873482
BMC Bioinformatics. 2006;7:497
17101041
Brief Bioinform. 2010 Jan;11(1):40-79
19955237
Bioinformatics. 2010 Feb 1;26(3):429-31
20007251
Proteomics. 2010 Mar;10(6):1316-27
20127684
Genome Biol. 2010;11(1):R3
20067622
Genome Biol. 2010;11(5):R53
20482850
Nature. 2010 Jul 15;466(7304):368-72
20531469
Bioinformatics. 2002;18 Suppl 1:S233-40
12169552
Nucleic Acids Res. 2003 Jan 1;31(1):248-50
12519993
Nat Biotechnol. 2010 Dec;28(12):1308
Nat Biotechnol. 2012 Apr;30(4):365
Reubenacker, Oliver [corrected to Ruebenacker, Oliver]
Computational Biology
methods
standards
Databases as Topic
Information Dissemination
Metabolic Networks and Pathways
Programming Languages
Signal Transduction
Software
NIHMS216985
PMC3001121
2010
9
09
2010
9
11
6
0
2010
9
11
6
0
2010
12
24
6
0
ppublish
nbt.1666
10.1038/nbt.1666
20829833
PMC3001121
NIHMS216985
20714644
2010
09
08
2011
01
13
2011
05
16
1742-2051
6
10
2010
Oct
Molecular bioSystems
Mol Biosyst
Coevolution of PDZ domain-ligand interactions analyzed by high-throughput phage display and deep sequencing.
1782-90
10.1039/c0mb00061b
The determinants of binding specificities of peptide recognition domains and their evolution remain important problems in molecular systems biology. Here, we present a new methodology to analyze the coevolution between a domain and its ligands by combining high-throughput phage display with deep sequencing. First, from a library of PDZ domains with diversity introduced at ten positions in the binding site, we evolved domains for binding to 15 distinct peptide ligands. Interestingly, for a given peptide many different functional domains emerged, which exhibited only limited sequence homology, showing that many different binding sites can recognize a given peptide. Subsequently, we used peptide-phage libraries and deep sequencing to map the specificity profiles of these evolved domains at high resolution, and we found that the domains recognize their cognate peptides with high affinity but low specificity. Our analysis reveals two aspects of evolution of new binding specificities. First, we were able to identify some common features amongst domains raised against a common peptide. Second, our analysis suggests that cooperative interactions between multiple binding site residues lead to a diversity of binding profiles with considerable plasticity. The details of intramolecular cooperativity remain to be elucidated, but nonetheless, we have established a general methodology that can be used to explore protein evolution in a systematic yet rapid manner.
Ernst
Andreas
A
Banting and Best Department of Medical Research, and the Terrence Donnelly Center for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario, Canada.
Gfeller
David
D
Kan
Zhengyan
Z
Seshagiri
Somasekar
S
Kim
Philip M
PM
Bader
Gary D
GD
Sidhu
Sachdev S
SS
eng
MOP-84324
Canadian Institutes of Health Research
Canada
MOP-93684
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2010
08
11
England
Mol Biosyst
101251620
1742-2051
0
Ligands
0
Peptide Library
0
Peptides
IM
Amino Acid Sequence
Bacteriophages
genetics
Evolution, Molecular
Ligands
PDZ Domains
Peptide Library
Peptides
metabolism
Protein Binding
2010
8
11
2010
10
1
2010
8
18
6
0
2010
8
18
6
0
2011
1
14
6
0
ppublish
10.1039/c0mb00061b
20714644
20656902
2010
09
08
2010
10
26
2014
08
24
1367-4811
26
18
2010
Sep
15
Bioinformatics (Oxford, England)
Bioinformatics
Cytoscape Web: an interactive web-based network browser.
2347-8
10.1093/bioinformatics/btq430
Cytoscape Web is a web-based network visualization tool-modeled after Cytoscape-which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF.
Cytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/.
Lopes
Christian T
CT
Banting and Best Department of Medical Research, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
Franz
Max
M
Kazi
Farzana
F
Donaldson
Sylva L
SL
Morris
Quaid
Q
Bader
Gary D
GD
eng
2R01GM070743-06
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2010
07
23
England
Bioinformatics
9808944
1367-4803
IM
Genome Res. 2003 Nov;13(11):2498-504
14597658
Bioinformatics. 2005 Dec 15;21(24):4432-3
16188923
Bioinformatics. 2008 Jun 15;24(12):1467-8
18445606
BMC Bioinformatics. 2008;9:405
18823568
Nucleic Acids Res. 2009 Jan;37(Database issue):D412-6
18940858
Nucleic Acids Res. 2010 Jul;38(Web Server issue):W214-20
20576703
Internet
Software
PMC2935447
2010
7
23
2010
7
27
6
0
2010
7
27
6
0
2010
10
27
6
0
ppublish
btq430
10.1093/bioinformatics/btq430
20656902
PMC2935447
20576703
2010
06
25
2010
09
27
2014
08
24
1362-4962
38
Web Server issue
2010
Jul
Nucleic acids research
Nucleic Acids Res.
The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function.
W214-20
10.1093/nar/gkq537
GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist.
Warde-Farley
David
D
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Donaldson
Sylva L
SL
Comes
Ovi
O
Zuberi
Khalid
K
Badrawi
Rashad
R
Chao
Pauline
P
Franz
Max
M
Grouios
Chris
C
Kazi
Farzana
F
Lopes
Christian Tannus
CT
Maitland
Anson
A
Mostafavi
Sara
S
Montojo
Jason
J
Shao
Quentin
Q
Wright
George
G
Bader
Gary D
GD
Morris
Quaid
Q
eng
Journal Article
Research Support, Non-U.S. Gov't
England
Nucleic Acids Res
0411011
0305-1048
IM
Nucleic Acids Res. 2009 Jan;37(Database issue):D674-9
18832364
PLoS Comput Biol. 2008;4(9):e1000165
18818725
Nucleic Acids Res. 2009 Jan;37(Database issue):D412-6
18940858
Nucleic Acids Res. 2009 Jan;37(Database issue):D767-72
18988627
Genome Res. 2009 Jun;19(6):1107-16
19246318
Nucleic Acids Res. 2010 Jan;38(Database issue):D525-31
19850723
Nucleic Acids Res. 2010 Jan;38(Database issue):D532-9
19897547
Bioinformatics. 2010 Jul 15;26(14):1759-65
20507895
Genome Biol. 2005;6(1):R2
15642094
Bioinformatics. 2005 May 1;21(9):2076-82
15657099
Plant J. 2005 Jul;43(1):153-63
15960624
Genome Biol. 2005;6(13):R114
16420673
Genome Biol. 2007;8(3):R39
17367534
Nucleic Acids Res. 2008 Jan;36(Database issue):D637-40
18000002
Genome Biol. 2008;9 Suppl 1:S2
18613946
Genome Biol. 2008;9 Suppl 1:S4
18613948
Curr Protoc Bioinformatics. 2008 Sep;Chapter 9:Unit 9.11
18819079
Nucleic Acids Res. 2009 Jan;37(Database issue):D885-90
18940857
Algorithms
Animals
Gene Regulatory Networks
Genes
physiology
Genomics
Humans
Internet
Mice
Software
PMC2896186
2010
6
26
6
0
2010
7
2
6
0
2010
9
29
6
0
ppublish
gkq537
10.1093/nar/gkq537
20576703
PMC2896186
20531469
2010
07
15
2010
08
30
2015
07
08
1476-4687
466
7304
2010
Jul
15
Nature
Nature
Functional impact of global rare copy number variation in autism spectrum disorders.
368-72
10.1038/nature09146
The autism spectrum disorders (ASDs) are a group of conditions characterized by impairments in reciprocal social interaction and communication, and the presence of restricted and repetitive behaviours. Individuals with an ASD vary greatly in cognitive development, which can range from above average to intellectual disability. Although ASDs are known to be highly heritable ( approximately 90%), the underlying genetic determinants are still largely unknown. Here we analysed the genome-wide characteristics of rare (<1% frequency) copy number variation in ASD using dense genotyping arrays. When comparing 996 ASD individuals of European ancestry to 1,287 matched controls, cases were found to carry a higher global burden of rare, genic copy number variants (CNVs) (1.19 fold, P = 0.012), especially so for loci previously implicated in either ASD and/or intellectual disability (1.69 fold, P = 3.4 x 10(-4)). Among the CNVs there were numerous de novo and inherited events, sometimes in combination in a given family, implicating many novel ASD genes such as SHANK2, SYNGAP1, DLGAP2 and the X-linked DDX53-PTCHD1 locus. We also discovered an enrichment of CNVs disrupting functional gene sets involved in cellular proliferation, projection and motility, and GTPase/Ras signalling. Our results reveal many new genetic and functional targets in ASD that may lead to final connected pathways.
Pinto
Dalila
D
The Centre for Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children, Toronto, Ontario M5G 1L7, Canada.
Pagnamenta
Alistair T
AT
Klei
Lambertus
L
Anney
Richard
R
Merico
Daniele
D
Regan
Regina
R
Conroy
Judith
J
Magalhaes
Tiago R
TR
Correia
Catarina
C
Abrahams
Brett S
BS
Almeida
Joana
J
Bacchelli
Elena
E
Bader
Gary D
GD
Bailey
Anthony J
AJ
Baird
Gillian
G
Battaglia
Agatino
A
Berney
Tom
T
Bolshakova
Nadia
N
Bölte
Sven
S
Bolton
Patrick F
PF
Bourgeron
Thomas
T
Brennan
Sean
S
Brian
Jessica
J
Bryson
Susan E
SE
Carson
Andrew R
AR
Casallo
Guillermo
G
Casey
Jillian
J
Chung
Brian H Y
BH
Cochrane
Lynne
L
Corsello
Christina
C
Crawford
Emily L
EL
Crossett
Andrew
A
Cytrynbaum
Cheryl
C
Dawson
Geraldine
G
de Jonge
Maretha
M
Delorme
Richard
R
Drmic
Irene
I
Duketis
Eftichia
E
Duque
Frederico
F
Estes
Annette
A
Farrar
Penny
P
Fernandez
Bridget A
BA
Folstein
Susan E
SE
Fombonne
Eric
E
Freitag
Christine M
CM
Gilbert
John
J
Gillberg
Christopher
C
Glessner
Joseph T
JT
Goldberg
Jeremy
J
Green
Andrew
A
Green
Jonathan
J
Guter
Stephen J
SJ
Hakonarson
Hakon
H
Heron
Elizabeth A
EA
Hill
Matthew
M
Holt
Richard
R
Howe
Jennifer L
JL
Hughes
Gillian
G
Hus
Vanessa
V
Igliozzi
Roberta
R
Kim
Cecilia
C
Klauck
Sabine M
SM
Kolevzon
Alexander
A
Korvatska
Olena
O
Kustanovich
Vlad
V
Lajonchere
Clara M
CM
Lamb
Janine A
JA
Laskawiec
Magdalena
M
Leboyer
Marion
M
Le Couteur
Ann
A
Leventhal
Bennett L
BL
Lionel
Anath C
AC
Liu
Xiao-Qing
XQ
Lord
Catherine
C
Lotspeich
Linda
L
Lund
Sabata C
SC
Maestrini
Elena
E
Mahoney
William
W
Mantoulan
Carine
C
Marshall
Christian R
CR
McConachie
Helen
H
McDougle
Christopher J
CJ
McGrath
Jane
J
McMahon
William M
WM
Merikangas
Alison
A
Migita
Ohsuke
O
Minshew
Nancy J
NJ
Mirza
Ghazala K
GK
Munson
Jeff
J
Nelson
Stanley F
SF
Noakes
Carolyn
C
Noor
Abdul
A
Nygren
Gudrun
G
Oliveira
Guiomar
G
Papanikolaou
Katerina
K
Parr
Jeremy R
JR
Parrini
Barbara
B
Paton
Tara
T
Pickles
Andrew
A
Pilorge
Marion
M
Piven
Joseph
J
Ponting
Chris P
CP
Posey
David J
DJ
Poustka
Annemarie
A
Poustka
Fritz
F
Prasad
Aparna
A
Ragoussis
Jiannis
J
Renshaw
Katy
K
Rickaby
Jessica
J
Roberts
Wendy
W
Roeder
Kathryn
K
Roge
Bernadette
B
Rutter
Michael L
ML
Bierut
Laura J
LJ
Rice
John P
JP
Salt
Jeff
J
Sansom
Katherine
K
Sato
Daisuke
D
Segurado
Ricardo
R
Sequeira
Ana F
AF
Senman
Lili
L
Shah
Naisha
N
Sheffield
Val C
VC
Soorya
Latha
L
Sousa
Inês
I
Stein
Olaf
O
Sykes
Nuala
N
Stoppioni
Vera
V
Strawbridge
Christina
C
Tancredi
Raffaella
R
Tansey
Katherine
K
Thiruvahindrapduram
Bhooma
B
Thompson
Ann P
AP
Thomson
Susanne
S
Tryfon
Ana
A
Tsiantis
John
J
Van Engeland
Herman
H
Vincent
John B
JB
Volkmar
Fred
F
Wallace
Simon
S
Wang
Kai
K
Wang
Zhouzhi
Z
Wassink
Thomas H
TH
Webber
Caleb
C
Weksberg
Rosanna
R
Wing
Kirsty
K
Wittemeyer
Kerstin
K
Wood
Shawn
S
Wu
Jing
J
Yaspan
Brian L
BL
Zurawiecki
Danielle
D
Zwaigenbaum
Lonnie
L
Buxbaum
Joseph D
JD
Cantor
Rita M
RM
Cook
Edwin H
EH
Coon
Hilary
H
Cuccaro
Michael L
ML
Devlin
Bernie
B
Ennis
Sean
S
Gallagher
Louise
L
Geschwind
Daniel H
DH
Gill
Michael
M
Haines
Jonathan L
JL
Hallmayer
Joachim
J
Miller
Judith
J
Monaco
Anthony P
AP
Nurnberger
John I
JI
Jr
Paterson
Andrew D
AD
Pericak-Vance
Margaret A
MA
Schellenberg
Gerard D
GD
Szatmari
Peter
P
Vicente
Astrid M
AM
Vieland
Veronica J
VJ
Wijsman
Ellen M
EM
Scherer
Stephen W
SW
Sutcliffe
James S
JS
Betancur
Catalina
C
eng
075491/Z/04
Wellcome Trust
United Kingdom
AS2077
Autism Speaks
United States
AS7462
Autism Speaks
United States
G0601030
Medical Research Council
United Kingdom
HD055751
HD
NICHD NIH HHS
United States
HD055782
HD
NICHD NIH HHS
United States
HD055784
HD
NICHD NIH HHS
United States
HD35465
HD
NICHD NIH HHS
United States
MC_U137761446
Medical Research Council
United Kingdom
MH061009
MH
NIMH NIH HHS
United States
MH06359
MH
NIMH NIH HHS
United States
MH066673
MH
NIMH NIH HHS
United States
MH080647
MH
NIMH NIH HHS
United States
MH081754
MH
NIMH NIH HHS
United States
MH52708
MH
NIMH NIH HHS
United States
MH55284
MH
NIMH NIH HHS
United States
MH57881
MH
NIMH NIH HHS
United States
MH66766
MH
NIMH NIH HHS
United States
NS026630
NS
NINDS NIH HHS
United States
NS042165
NS
NINDS NIH HHS
United States
NS049261
NS
NINDS NIH HHS
United States
P01 CA089392
CA
NCI NIH HHS
United States
P01 CA089392-08
CA
NCI NIH HHS
United States
P01 HD035465-01S1
HD
NICHD NIH HHS
United States
P01 NS026630
NS
NINDS NIH HHS
United States
P01 NS026630-15
NS
NINDS NIH HHS
United States
P50 HD055748
HD
NICHD NIH HHS
United States
P50 HD055748-01
HD
NICHD NIH HHS
United States
P50 HD055748-02
HD
NICHD NIH HHS
United States
P50 HD055748-03
HD
NICHD NIH HHS
United States
P50 HD055751
HD
NICHD NIH HHS
United States
P50 HD055751-01
HD
NICHD NIH HHS
United States
P50 HD055782
HD
NICHD NIH HHS
United States
P50 HD055782-04
HD
NICHD NIH HHS
United States
R01 DA013423
DA
NIDA NIH HHS
United States
R01 DA013423-05
DA
NIDA NIH HHS
United States
R01 DA019963
DA
NIDA NIH HHS
United States
R01 DA019963-01A2
DA
NIDA NIH HHS
United States
R01 DA019963-02
DA
NIDA NIH HHS
United States
R01 DA019963-03
DA
NIDA NIH HHS
United States
R01 MH052708-05
MH
NIMH NIH HHS
United States
R01 MH055284
MH
NIMH NIH HHS
United States
R01 MH055284-04
MH
NIMH NIH HHS
United States
R01 MH057881
MH
NIMH NIH HHS
United States
R01 MH057881-02
MH
NIMH NIH HHS
United States
R01 MH061009
MH
NIMH NIH HHS
United States
R01 MH061009-05
MH
NIMH NIH HHS
United States
R01 MH080647
MH
NIMH NIH HHS
United States
R01 MH080647-11
MH
NIMH NIH HHS
United States
R01 MH081754
MH
NIMH NIH HHS
United States
R01 MH081754-01
MH
NIMH NIH HHS
United States
R01 NS042165
NS
NINDS NIH HHS
United States
R01 NS042165-05
NS
NINDS NIH HHS
United States
R01 NS049261
NS
NINDS NIH HHS
United States
R01 NS049261-02
NS
NINDS NIH HHS
United States
U01 HG004422
HG
NHGRI NIH HHS
United States
U01 HG004422-02
HG
NHGRI NIH HHS
United States
U10 MH066766-05
MH
NIMH NIH HHS
United States
U19 HD035469
HD
NICHD NIH HHS
United States
U19 HD035469-06
HD
NICHD NIH HHS
United States
U19 HD035469-07
HD
NICHD NIH HHS
United States
U19 HD035469-08
HD
NICHD NIH HHS
United States
U19 HD035469-09
HD
NICHD NIH HHS
United States
U19 HD035469-10
HD
NICHD NIH HHS
United States
U54 MH066673
MH
NIMH NIH HHS
United States
U54 MH066673-05
MH
NIMH NIH HHS
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UL1 TR000448
TR
NCATS NIH HHS
United States
Canadian Institutes of Health Research
Canada
Medical Research Council
United Kingdom
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Research Support, Non-U.S. Gov't
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Case-Control Studies
Cell Movement
Child
Child Development Disorders, Pervasive
genetics
pathology
physiopathology
Cytoprotection
DNA Copy Number Variations
genetics
Europe
ethnology
Gene Dosage
genetics
Genetic Predisposition to Disease
genetics
Genome-Wide Association Study
Humans
Signal Transduction
Social Behavior
NIHMS237855
PMC3021798
2009
12
03
2010
5
07
2010
6
09
2010
6
10
6
0
2010
6
10
6
0
2010
8
31
6
0
ppublish
nature09146
10.1038/nature09146
20531469
PMC3021798
NIHMS237855
20512140
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2010
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Nat. Chem. Biol.
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Canadian Institutes of Health Research
Canada
MOP-81340
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, Non-U.S. Gov't
2010
05
30
United States
Nat Chem Biol
101231976
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Pharmaceutical Preparations
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Nat Chem Biol. 2010 Jul;6(7):482-3
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Animals
Caenorhabditis elegans
metabolism
Chromatography, High Pressure Liquid
methods
Drug Evaluation, Preclinical
methods
Models, Biological
Molecular Structure
Pharmaceutical Preparations
chemistry
metabolism
Structure-Activity Relationship
2009
10
05
2010
4
26
2010
5
30
2010
6
1
6
0
2010
6
1
6
0
2010
7
17
6
0
ppublish
nchembio.380
10.1038/nchembio.380
20512140
20393554
2010
04
15
2010
06
02
2014
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08
1476-4687
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Apr
15
Nature
Nature
International network of cancer genome projects.
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10.1038/nature08987
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International Cancer Genome Consortium
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Warwick
W
Artez
Axel
A
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Anna D
AD
Bell
Cindy
C
Bernabé
Rosa R
RR
Bhan
M K
MK
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Eerola
Iiro
I
Gerhard
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Guttmacher
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A
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M
Hemsley
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JL
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D
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DP
Laplace
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F
Youyong
Lu
L
Nettekoven
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G
Ozenberger
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B
Peterson
Jane
J
Rao
T S
TS
Remacle
Jacques
J
Schafer
Alan J
AJ
Shibata
Tatsuhiro
T
Stratton
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MR
Vockley
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JG
Watanabe
Koichi
K
Yang
Huanming
H
Yuen
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MM
Knoppers
Bartha M
BM
Bobrow
Martin
M
Cambon-Thomsen
Anne
A
Dressler
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LG
Dyke
Stephanie O M
SO
Joly
Yann
Y
Kato
Kazuto
K
Kennedy
Karen L
KL
Nicolás
Pilar
P
Parker
Michael J
MJ
Rial-Sebbag
Emmanuelle
E
Romeo-Casabona
Carlos M
CM
Shaw
Kenna M
KM
Wallace
Susan
S
Wiesner
Georgia L
GL
Zeps
Nikolajs
N
Lichter
Peter
P
Biankin
Andrew V
AV
Chabannon
Christian
C
Chin
Lynda
L
Clément
Bruno
B
de Alava
Enrique
E
Degos
Françoise
F
Ferguson
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ML
Geary
Peter
P
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D Neil
DN
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TJ
Johns
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AL
Kasprzyk
Arek
A
Nakagawa
Hidewaki
H
Penny
Robert
R
Piris
Miguel A
MA
Sarin
Rajiv
R
Scarpa
Aldo
A
Shibata
Tatsuhiro
T
van de Vijver
Marc
M
Futreal
P Andrew
PA
Aburatani
Hiroyuki
H
Bayés
Mónica
M
Botwell
David D L
DD
Campbell
Peter J
PJ
Estivill
Xavier
X
Gerhard
Daniela S
DS
Grimmond
Sean M
SM
Gut
Ivo
I
Hirst
Martin
M
López-Otín
Carlos
C
Majumder
Partha
P
Marra
Marco
M
McPherson
John D
JD
Nakagawa
Hidewaki
H
Ning
Zemin
Z
Puente
Xose S
XS
Ruan
Yijun
Y
Shibata
Tatsuhiro
T
Stratton
Michael R
MR
Stunnenberg
Hendrik G
HG
Swerdlow
Harold
H
Velculescu
Victor E
VE
Wilson
Richard K
RK
Xue
Hong H
HH
Yang
Liu
L
Spellman
Paul T
PT
Bader
Gary D
GD
Boutros
Paul C
PC
Campbell
Peter J
PJ
Flicek
Paul
P
Getz
Gad
G
Guigó
Roderic
R
Guo
Guangwu
G
Haussler
David
D
Heath
Simon
S
Hubbard
Tim J
TJ
Jiang
Tao
T
Jones
Steven M
SM
Li
Qibin
Q
López-Bigas
Nuria
N
Luo
Ruibang
R
Muthuswamy
Lakshmi
L
Ouellette
B F Francis
BF
Pearson
John V
JV
Puente
Xose S
XS
Quesada
Victor
V
Raphael
Benjamin J
BJ
Sander
Chris
C
Shibata
Tatsuhiro
T
Speed
Terence P
TP
Stein
Lincoln D
LD
Stuart
Joshua M
JM
Teague
Jon W
JW
Totoki
Yasushi
Y
Tsunoda
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Valencia
Alfonso
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David A
DA
Wu
Honglong
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Zhao
Shancen
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Zhou
Guangyu
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SM
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Nuria
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Spellman
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Myles
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SO
Futreal
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PA
Gerhard
Daniela S
DS
Gunter
Chris
C
Guyer
Mark
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Hudson
Thomas J
TJ
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John D
JD
Miller
Linda J
LJ
Ozenberger
Brad
B
Shaw
Kenna M
KM
Kasprzyk
Arek
A
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Lincoln D
LD
Zhang
Junjun
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Syed A
SA
Wang
Jianxin
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CK
Cros
Anthony
A
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Anthony
A
Liang
Yong
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Jack
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M
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DR
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KW
Joly
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Kaan
Terry S H
TS
Kennedy
Karen L
KL
Knoppers
Bartha M
BM
Lowrance
William W
WW
Masui
Tohru
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Nicolás
Pilar
P
Rial-Sebbag
Emmanuelle
E
Rodriguez
Laura Lyman
LL
Vergely
Catherine
C
Yoshida
Teruhiko
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Sean M
SM
Biankin
Andrew V
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Bowtell
David D L
DD
Cloonan
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A
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JR
Etemadmoghadam
Dariush
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BB
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JG
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Aldo
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Sutherland
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RL
Tempero
Margaret A
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NJ
Wilson
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PJ
McPherson
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JD
Gallinger
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S
Tsao
Ming-Sound
MS
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GM
Mukhopadhyay
Debabrata
D
Chin
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RA
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Lakshmi
L
Shazand
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Lu
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Ji
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Xiuqing
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Hu
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Zhou
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Qi
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D
Brazma
Alvis
A
Egevard
Lars
L
Prokhortchouk
Egor
E
Banks
Rosamonde Elizabeth
RE
Uhlén
Mathias
M
Cambon-Thomsen
Anne
A
Viksna
Juris
J
Ponten
Fredrik
F
Skryabin
Konstantin
K
Stratton
Michael R
MR
Futreal
P Andrew
PA
Birney
Ewan
E
Borg
Ake
A
Børresen-Dale
Anne-Lise
AL
Caldas
Carlos
C
Foekens
John A
JA
Martin
Sancha
S
Reis-Filho
Jorge S
JS
Richardson
Andrea L
AL
Sotiriou
Christos
C
Stunnenberg
Hendrik G
HG
Thoms
Giles
G
van de Vijver
Marc
M
van't Veer
Laura
L
Calvo
Fabien
F
Birnbaum
Daniel
D
Blanche
Hélène
H
Boucher
Pascal
P
Boyault
Sandrine
S
Chabannon
Christian
C
Gut
Ivo
I
Masson-Jacquemier
Jocelyne D
JD
Lathrop
Mark
M
Pauporté
Iris
I
Pivot
Xavier
X
Vincent-Salomon
Anne
A
Tabone
Eric
E
Theillet
Charles
C
Thomas
Gilles
G
Tost
Jörg
J
Treilleux
Isabelle
I
Calvo
Fabien
F
Bioulac-Sage
Paulette
P
Clément
Bruno
B
Decaens
Thomas
T
Degos
Françoise
F
Franco
Dominique
D
Gut
Ivo
I
Gut
Marta
M
Heath
Simon
S
Lathrop
Mark
M
Samuel
Didier
D
Thomas
Gilles
G
Zucman-Rossi
Jessica
J
Lichter
Peter
P
Eils
Roland
R
Brors
Benedikt
B
Korbel
Jan O
JO
Korshunov
Andrey
A
Landgraf
Pablo
P
Lehrach
Hans
H
Pfister
Stefan
S
Radlwimmer
Bernhard
B
Reifenberger
Guido
G
Taylor
Michael D
MD
von Kalle
Christof
C
Majumder
Partha P
PP
Sarin
Rajiv
R
Rao
T S
TS
Bhan
M K
MK
Scarpa
Aldo
A
Pederzoli
Paolo
P
Lawlor
Rita A
RA
Delledonne
Massimo
M
Bardelli
Alberto
A
Biankin
Andrew V
AV
Grimmond
Sean M
SM
Gress
Thomas
T
Klimstra
David
D
Zamboni
Giuseppe
G
Shibata
Tatsuhiro
T
Nakamura
Yusuke
Y
Nakagawa
Hidewaki
H
Kusada
Jun
J
Tsunoda
Tatsuhiko
T
Miyano
Satoru
S
Aburatani
Hiroyuki
H
Kato
Kazuto
K
Fujimoto
Akihiro
A
Yoshida
Teruhiko
T
Campo
Elias
E
López-Otín
Carlos
C
Estivill
Xavier
X
Guigó
Roderic
R
de Sanjosé
Silvia
S
Piris
Miguel A
MA
Montserrat
Emili
E
González-Díaz
Marcos
M
Puente
Xose S
XS
Jares
Pedro
P
Valencia
Alfonso
A
Himmelbauer
Heinz
H
Himmelbaue
Heinz
H
Quesada
Victor
V
Bea
Silvia
S
Stratton
Michael R
MR
Futreal
P Andrew
PA
Campbell
Peter J
PJ
Vincent-Salomon
Anne
A
Richardson
Andrea L
AL
Reis-Filho
Jorge S
JS
van de Vijver
Marc
M
Thomas
Gilles
G
Masson-Jacquemier
Jocelyne D
JD
Aparicio
Samuel
S
Borg
Ake
A
Børresen-Dale
Anne-Lise
AL
Caldas
Carlos
C
Foekens
John A
JA
Stunnenberg
Hendrik G
HG
van't Veer
Laura
L
Easton
Douglas F
DF
Spellman
Paul T
PT
Martin
Sancha
S
Barker
Anna D
AD
Chin
Lynda
L
Collins
Francis S
FS
Compton
Carolyn C
CC
Ferguson
Martin L
ML
Gerhard
Daniela S
DS
Getz
Gad
G
Gunter
Chris
C
Guttmacher
Alan
A
Guyer
Mark
M
Hayes
D Neil
DN
Lander
Eric S
ES
Ozenberger
Brad
B
Penny
Robert
R
Peterson
Jane
J
Sander
Chris
C
Shaw
Kenna M
KM
Speed
Terence P
TP
Spellman
Paul T
PT
Vockley
Joseph G
JG
Wheeler
David A
DA
Wilson
Richard K
RK
Hudson
Thomas J
TJ
Chin
Lynda
L
Knoppers
Bartha M
BM
Lander
Eric S
ES
Lichter
Peter
P
Stein
Lincoln D
LD
Stratton
Michael R
MR
Anderson
Warwick
W
Barker
Anna D
AD
Bell
Cindy
C
Bobrow
Martin
M
Burke
Wylie
W
Collins
Francis S
FS
Compton
Carolyn C
CC
DePinho
Ronald A
RA
Easton
Douglas F
DF
Futreal
P Andrew
PA
Gerhard
Daniela S
DS
Green
Anthony R
AR
Guyer
Mark
M
Hamilton
Stanley R
SR
Hubbard
Tim J
TJ
Kallioniemi
Olli P
OP
Kennedy
Karen L
KL
Ley
Timothy J
TJ
Liu
Edison T
ET
Lu
Youyong
Y
Majumder
Partha
P
Marra
Marco
M
Ozenberger
Brad
B
Peterson
Jane
J
Schafer
Alan J
AJ
Spellman
Paul T
PT
Stunnenberg
Hendrik G
HG
Wainwright
Brandon J
BJ
Wilson
Richard K
RK
Yang
Huanming
H
eng
077198
Wellcome Trust
United Kingdom
088340
Wellcome Trust
United Kingdom
093867
Wellcome Trust
United Kingdom
6613
Cancer Research UK
United Kingdom
K08 DK071329
DK
NIDDK NIH HHS
United States
K08 DK071329-04
DK
NIDDK NIH HHS
United States
K08 DK071329-05
DK
NIDDK NIH HHS
United States
P01 CA117969
CA
NCI NIH HHS
United States
P01 CA117969-04S1
CA
NCI NIH HHS
United States
P01 CA117969-05
CA
NCI NIH HHS
United States
P50 CA102701
CA
NCI NIH HHS
United States
P50 CA102701-08
CA
NCI NIH HHS
United States
P50 CA127003
CA
NCI NIH HHS
United States
P50 CA127003-04
CA
NCI NIH HHS
United States
P50 CA127003-05
CA
NCI NIH HHS
United States
R01 HG001806-02
HG
NHGRI NIH HHS
United States
Journal Article
England
Nature
0410462
0028-0836
IM
Nature. 2009 Apr 9;458(7239):719-24
19360079
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19264984
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17344846
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Himmelbaue, Heinz [corrected to Himmelbauer, Heinz]; Gardiner, Brooke A [corrected to Gardiner, Brooke B]; Cross, Anthony [corrected to Cros, Anthony]
DNA Methylation
DNA Mutational Analysis
trends
Databases, Genetic
Genes, Neoplasm
genetics
Genetics, Medical
organization & administration
trends
Genome, Human
genetics
Genomics
organization & administration
trends
Humans
Intellectual Property
International Cooperation
Mutation
Neoplasms
classification
genetics
pathology
therapy
NIHMS187228
PMC2902243
2010
4
16
6
0
2010
4
16
6
0
2010
6
3
6
0
ppublish
nature08987
10.1038/nature08987
20393554
PMC2902243
NIHMS187228
20127684
2010
03
29
2010
06
18
2014
09
08
1615-9861
10
6
2010
Mar
Proteomics
Proteomics
Pathway analysis of dilated cardiomyopathy using global proteomic profiling and enrichment maps.
1316-27
10.1002/pmic.200900412
Global protein expression profiling can potentially uncover perturbations associated with common forms of heart disease. We have used shotgun MS/MS to monitor the state of biological systems in cardiac tissue correlating with disease onset, cardiac insufficiency and progression to heart failure in a time-course mouse model of dilated cardiomyopathy. However, interpreting the functional significance of the hundreds of differentially expressed proteins has been challenging. Here, we utilize improved enrichment statistical methods and an extensive collection of functionally related gene sets, gaining a more comprehensive understanding of the progressive alterations associated with functional decline in dilated cardiomyopathy. We visualize the enrichment results as an Enrichment Map, where significant gene sets are grouped based on annotation similarity. This approach vastly simplifies the interpretation of the large number of enriched gene sets found. For pathways of specific interest, such as Apoptosis and the MAPK (mitogen-activated protein kinase) cascade, we performed a more detailed analysis of the underlying signaling network, including experimental validation of expression patterns.
Isserlin
Ruth
R
Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada.
Merico
Daniele
D
Alikhani-Koupaei
Rasoul
R
Gramolini
Anthony
A
Bader
Gary D
GD
Emili
Andrew
A
eng
106538-1
Canadian Institutes of Health Research
Canada
87143-1
Canadian Institutes of Health Research
Canada
P41 HG004118
HG
NHGRI NIH HHS
United States
P41 HG004118-01A1
HG
NHGRI NIH HHS
United States
P41 HG004118-02
HG
NHGRI NIH HHS
United States
P41 P41HG04118
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Germany
Proteomics
101092707
1615-9853
0
Gelsolin
9Y8NXQ24VQ
Propranolol
EC 3.4.22.-
Caspase 3
IM
J Am Coll Cardiol. 2000 Mar 1;35(3):569-82
10716457
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19714876
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11120693
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11389458
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11593045
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12610310
Genome Biol. 2003;4(5):P3
12734009
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12750397
EMBO J. 2003 Oct 1;22(19):5079-89
14517246
BMC Bioinformatics. 2002 Nov 13;3:35
12431279
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14597658
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14734503
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14990455
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15253663
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10802651
Animals
Apoptosis
physiology
Cardiomyopathy, Dilated
drug therapy
genetics
physiopathology
Caspase 3
physiology
Databases, Protein
Gelsolin
physiology
Gene Expression Profiling
MAP Kinase Signaling System
physiology
Metabolomics
Mice
Propranolol
therapeutic use
Proteomics
methods
Systems Biology
Tandem Mass Spectrometry
NIHMS201980
PMC2879143
2010
2
4
6
0
2010
2
4
6
0
2010
6
19
6
0
ppublish
10.1002/pmic.200900412
20127684
PMC2879143
NIHMS201980
20093466
2010
01
22
2010
02
02
2014
12
05
1095-9203
327
5964
2010
Jan
22
Science (New York, N.Y.)
Science
The genetic landscape of a cell.
425-31
10.1126/science.1180823
A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
Costanzo
Michael
M
Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
Baryshnikova
Anastasia
A
Bellay
Jeremy
J
Kim
Yungil
Y
Spear
Eric D
ED
Sevier
Carolyn S
CS
Ding
Huiming
H
Koh
Judice L Y
JL
Toufighi
Kiana
K
Mostafavi
Sara
S
Prinz
Jeany
J
St Onge
Robert P
RP
VanderSluis
Benjamin
B
Makhnevych
Taras
T
Vizeacoumar
Franco J
FJ
Alizadeh
Solmaz
S
Bahr
Sondra
S
Brost
Renee L
RL
Chen
Yiqun
Y
Cokol
Murat
M
Deshpande
Raamesh
R
Li
Zhijian
Z
Lin
Zhen-Yuan
ZY
Liang
Wendy
W
Marback
Michaela
M
Paw
Jadine
J
San Luis
Bryan-Joseph
BJ
Shuteriqi
Ermira
E
Tong
Amy Hin Yan
AH
van Dyk
Nydia
N
Wallace
Iain M
IM
Whitney
Joseph A
JA
Weirauch
Matthew T
MT
Zhong
Guoqing
G
Zhu
Hongwei
H
Houry
Walid A
WA
Brudno
Michael
M
Ragibizadeh
Sasan
S
Papp
Balázs
B
Pál
Csaba
C
Roth
Frederick P
FP
Giaever
Guri
G
Nislow
Corey
C
Troyanskaya
Olga G
OG
Bussey
Howard
H
Bader
Gary D
GD
Gingras
Anne-Claude
AC
Morris
Quaid D
QD
Kim
Philip M
PM
Kaiser
Chris A
CA
Myers
Chad L
CL
Andrews
Brenda J
BJ
Boone
Charles
C
eng
084314
Wellcome Trust
United Kingdom
GSP-41567
Canadian Institutes of Health Research
Canada
R01 HG003224
HG
NHGRI NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
United States
Science
0404511
0036-8075
0
Saccharomyces cerevisiae Proteins
IM
Nat Rev Genet. 2010 Mar;11(3):172
21485431
Computational Biology
Gene Duplication
Gene Expression Regulation, Fungal
Gene Regulatory Networks
Genes, Fungal
Genetic Fitness
Genome, Fungal
Metabolic Networks and Pathways
Mutation
Protein Interaction Mapping
Saccharomyces cerevisiae
genetics
metabolism
physiology
Saccharomyces cerevisiae Proteins
genetics
metabolism
2010
1
23
6
0
2010
1
23
6
0
2010
2
3
6
0
ppublish
327/5964/425
10.1126/science.1180823
20093466
20067622
2010
03
31
2010
07
28
2015
08
04
1474-760X
11
1
2010
Genome biology
Genome Biol.
NetPath: a public resource of curated signal transduction pathways.
R3
10.1186/gb-2010-11-1-r3
We have developed NetPath as a resource of curated human signaling pathways. As an initial step, NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles. We anticipate NetPath to become a consolidated resource for human signaling pathways that should enable systems biology approaches.
Kandasamy
Kumaran
K
Institute of Bioinformatics, International Tech Park, Bangalore 560066, India. kumaran@ibioinformatics.org
Mohan
S Sujatha
SS
Raju
Rajesh
R
Keerthikumar
Shivakumar
S
Kumar
Ghantasala S Sameer
GS
Venugopal
Abhilash K
AK
Telikicherla
Deepthi
D
Navarro
J Daniel
JD
Mathivanan
Suresh
S
Pecquet
Christian
C
Gollapudi
Sashi Kanth
SK
Tattikota
Sudhir Gopal
SG
Mohan
Shyam
S
Padhukasahasram
Hariprasad
H
Subbannayya
Yashwanth
Y
Goel
Renu
R
Jacob
Harrys K C
HK
Zhong
Jun
J
Sekhar
Raja
R
Nanjappa
Vishalakshi
V
Balakrishnan
Lavanya
L
Subbaiah
Roopashree
R
Ramachandra
Y L
YL
Rahiman
B Abdul
BA
Prasad
T S Keshava
TS
Lin
Jian-Xin
JX
Houtman
Jon C D
JC
Desiderio
Stephen
S
Renauld
Jean-Christophe
JC
Constantinescu
Stefan N
SN
Ohara
Osamu
O
Hirano
Toshio
T
Kubo
Masato
M
Singh
Sujay
S
Khatri
Purvesh
P
Draghici
Sorin
S
Bader
Gary D
GD
Sander
Chris
C
Leonard
Warren J
WJ
Pandey
Akhilesh
A
eng
CA 88843
CA
NCI NIH HHS
United States
R01 DK089167
DK
NIDDK NIH HHS
United States
U54 RR020839
RR
NCRR NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
2010
01
12
England
Genome Biol
100960660
1474-7596
0
Interleukin-2
IM
Intensive Care Med. 2007 Oct;33(10):1829-39
17581740
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15763557
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18651794
BMC Bioinformatics. 2008;9:399
18817533
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18948298
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18988627
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19628504
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12117798
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12611808
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12752672
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12952881
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15763558
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16381921
Nucleic Acids Res. 2000 Jan 1;28(1):27-30
10592173
Nature. 2001 May 17;411(6835):380-4
11357146
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11590099
Cytokine Growth Factor Rev. 2002 Feb;13(1):27-40
11750878
AIDS Rev. 2002 Jan-Mar;4(1):36-40
11998783
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16381926
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16911870
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17099226
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14597658
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Access to Information
Animals
Apoptosis
Biochemistry
methods
Cell Movement
Computational Biology
methods
Databases, Factual
Humans
Immune System
Interleukin-2
metabolism
Models, Biological
Models, Genetic
Protein Interaction Mapping
Signal Transduction
Software
Transcription, Genetic
PMC2847715
2009
4
21
2009
11
2
2010
1
12
2010
1
12
2010
1
14
6
0
2010
1
14
6
0
2010
7
29
6
0
epublish
gb-2010-11-1-r3
10.1186/gb-2010-11-1-r3
20067622
PMC2847715
19880385
2009
12
22
2010
02
01
2014
08
27
1362-4962
38
Database issue
2010
Jan
Nucleic acids research
Nucleic Acids Res.
DRYGIN: a database of quantitative genetic interaction networks in yeast.
D502-7
10.1093/nar/gkp820
Genetic interactions are highly informative for deciphering the underlying functional principles that govern how genes control cell processes. Recent developments in Synthetic Genetic Array (SGA) analysis enable the mapping of quantitative genetic interactions on a genome-wide scale. To facilitate access to this resource, which will ultimately represent a complete genetic interaction network for a eukaryotic cell, we developed DRYGIN (Data Repository of Yeast Genetic Interactions)-a web database system that aims at providing a central platform for yeast genetic network analysis and visualization. In addition to providing an interface for searching the SGA genetic interactions, DRYGIN also integrates other data sources, in order to associate the genetic interactions with pathway information, protein complexes, other binary genetic and physical interactions, and Gene Ontology functional annotation. DRYGIN version 1.0 currently holds more than 5.4 million measurements of genetic interacting pairs involving approximately 4500 genes, and is available at http://drygin.ccbr.utoronto.ca.
Koh
Judice L Y
JL
Banting and Best Department of Medical Research, The Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, ON, Canada. judice.koh@utoronto.ca
Ding
Huiming
H
Costanzo
Michael
M
Baryshnikova
Anastasia
A
Toufighi
Kiana
K
Bader
Gary D
GD
Myers
Chad L
CL
Andrews
Brenda J
BJ
Boone
Charles
C
eng
Journal Article
Research Support, Non-U.S. Gov't
2009
10
30
England
Nucleic Acids Res
0411011
0305-1048
0
Fungal Proteins
IM
Genome Res. 2003 Nov;13(11):2498-504
14597658
Science. 2001 Dec 14;294(5550):2364-8
11743205
Science. 2004 Feb 6;303(5659):808-13
14764870
Bioinformatics. 2004 Nov 22;20(17):3246-8
15180930
Nat Genet. 2005 Jan;37(1):77-83
15592468
Bioinformatics. 2005 Apr 15;21(8):1741-2
15613388
Cell. 2005 Nov 4;123(3):507-19
16269340
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D442-5
16381907
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9
16381927
Nat Rev Genet. 2007 Jun;8(6):437-49
17510664
Nat Rev Genet. 2007 Sep;8(9):699-710
17703239
Nucleic Acids Res. 2008 Jan;36(Database issue):D480-4
18077471
Nucleic Acids Res. 2008 Jan;36(Database issue):D196-201
18158298
PLoS Comput Biol. 2008 Apr;4(4):e1000065
18421374
Proc Natl Acad Sci U S A. 2008 Oct 28;105(43):16653-8
18931302
Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22
18981052
Nucleic Acids Res. 2009 Feb;37(3):825-31
19095691
Nat Biotechnol. 2004 Feb;22(2):177-83
14755292
Computational Biology
methods
trends
Databases, Genetic
Databases, Nucleic Acid
Databases, Protein
Fungal Proteins
genetics
Genes, Fungal
Genome, Fungal
Information Storage and Retrieval
methods
Internet
Models, Genetic
Protein Interaction Mapping
Protein Structure, Tertiary
Software
PMC2808960
2009
10
30
2009
11
3
6
0
2009
11
3
6
0
2010
2
2
6
0
ppublish
gkp820
10.1093/nar/gkp820
19880385
PMC2808960
19841731
2009
10
20
2010
02
17
2014
08
27
1545-7885
7
10
2009
Oct
PLoS biology
PLoS Biol.
Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins.
e1000218
10.1371/journal.pbio.1000218
SH3 domains are peptide recognition modules that mediate the assembly of diverse biological complexes. We scanned billions of phage-displayed peptides to map the binding specificities of the SH3 domain family in the budding yeast, Saccharomyces cerevisiae. Although most of the SH3 domains fall into the canonical classes I and II, each domain utilizes distinct features of its cognate ligands to achieve binding selectivity. Furthermore, we uncovered several SH3 domains with specificity profiles that clearly deviate from the two canonical classes. In conjunction with phage display, we used yeast two-hybrid and peptide array screening to independently identify SH3 domain binding partners. The results from the three complementary techniques were integrated using a Bayesian algorithm to generate a high-confidence yeast SH3 domain interaction map. The interaction map was enriched for proteins involved in endocytosis, revealing a set of SH3-mediated interactions that underlie formation of protein complexes essential to this biological pathway. We used the SH3 domain interaction network to predict the dynamic localization of several previously uncharacterized endocytic proteins, and our analysis suggests a novel role for the SH3 domains of Lsb3p and Lsb4p as hubs that recruit and assemble several endocytic complexes.
Tonikian
Raffi
R
Terrence Donnelly Center for Cellular and Biomolecular Research, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada.
Xin
Xiaofeng
X
Toret
Christopher P
CP
Gfeller
David
D
Landgraf
Christiane
C
Panni
Simona
S
Paoluzi
Serena
S
Castagnoli
Luisa
L
Currell
Bridget
B
Seshagiri
Somasekar
S
Yu
Haiyuan
H
Winsor
Barbara
B
Vidal
Marc
M
Gerstein
Mark B
MB
Bader
Gary D
GD
Volkmer
Rudolf
R
Cesareni
Gianni
G
Drubin
David G
DG
Kim
Philip M
PM
Sidhu
Sachdev S
SS
Boone
Charles
C
eng
GM R01 50399
GM
NIGMS NIH HHS
United States
MOP-84324
Canadian Institutes of Health Research
Canada
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2009
10
20
United States
PLoS Biol
101183755
1544-9173
0
Carrier Proteins
0
Ligands
0
Lsb3 protein, S cerevisiae
0
Microfilament Proteins
0
Peptide Library
0
Saccharomyces cerevisiae Proteins
0
YSC84 protein, S cerevisiae
IM
Genetics. 1996 Dec;144(4):1425-36
8978031
Comput Appl Biosci. 1996 Apr;12(2):135-43
8744776
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9383403
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Mol Cell Proteomics. 2005 Aug;4(8):1155-66
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9207794
Algorithms
Bayes Theorem
Carrier Proteins
chemistry
metabolism
Endocytosis
Gene Expression Regulation, Fungal
Ligands
Microfilament Proteins
chemistry
metabolism
Models, Molecular
Peptide Library
Protein Binding
Protein Interaction Mapping
methods
Saccharomyces cerevisiae
metabolism
Saccharomyces cerevisiae Proteins
chemistry
genetics
metabolism
Two-Hybrid System Techniques
src Homology Domains
PMC2756588
2009
2
6
2009
9
4
2009
10
20
2009
10
21
6
0
2009
10
21
6
0
2010
2
18
6
0
ppublish
10.1371/journal.pbio.1000218
19841731
PMC2756588
19816451
2009
10
09
2010
02
17
2014
09
07
1546-1696
27
10
2009
Oct
Nature biotechnology
Nat. Biotechnol.
How to visually interpret biological data using networks.
921-4
10.1038/nbt.1567
Networks in biology can appear complex and difficult to decipher. We illustrate how to interpret biological networks with the help of frequently used visualization and analysis patterns.
Merico
Daniele
D
Terrence Donnelly Centre for Cellular and Biomolecular Research and Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada.
Gfeller
David
D
Bader
Gary D
GD
eng
R01 GM070743
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
United States
Nat Biotechnol
9604648
1087-0156
IM
Nat Genet. 2000 May;25(1):25-9
10802651
Nat Biotechnol. 2009 Aug;27(8):735-41
19668183
Bioinformatics. 2001 Sep;17(9):829-37
11590099
Science. 2002 Mar 1;295(5560):1669-78
11872831
Science. 2003 Oct 10;302(5643):249-55
12934013
Nat Genet. 2004 Jun;36(6):559-64
15167932
Mol Biol Cell. 1998 Dec;9(12):3273-97
9843569
Nature. 2005 Aug 11;436(7052):861-5
16094371
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9
16381927
J Biol. 2006;5(4):11
16762047
Nature. 2007 Apr 12;446(7137):806-10
17314980
Nat Biotechnol. 2007 May;25(5):547-54
17483841
Nat Rev Genet. 2007 Jun;8(6):437-49
17510664
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W625-32
17586824
Nat Protoc. 2007;2(10):2366-82
17947979
Nat Genet. 2007 Nov;39(11):1338-49
17922014
Nature. 2009 Apr 23;458(7241):987-92
19363474
Nature. 2001 Mar 8;410(6825):268-76
11258382
Data Interpretation, Statistical
Gene Regulatory Networks
Information Services
Metabolic Networks and Pathways
Models, Biological
NIHMS419541
PMC4154490
2009
10
10
6
0
2009
10
10
6
0
2010
2
18
6
0
ppublish
nbt.1567
10.1038/nbt.1567
19816451
PMC4154490
NIHMS419541
19738200
2009
09
09
2009
12
08
2010
09
21
1937-9145
2
87
2009
Science signaling
Sci Signal
Rapid evolution of functional complexity in a domain family.
ra50
10.1126/scisignal.2000416
Multicellular organisms rely on complex, fine-tuned protein networks to respond to environmental changes. We used in vitro evolution to explore the role of domain mutation and expansion in the evolution of network complexity. Using random mutagenesis to facilitate family expansion, we asked how versatile and robust the binding site must be to produce the rich functional diversity of the natural PDZ domain family. From a combinatorial protein library, we analyzed several hundred structured domain variants and found that one-quarter were functional for carboxyl-terminal ligand recognition and that our variant repertoire was as specific and diverse as the natural family. Our results show that ligand binding is hardwired in the PDZ fold and suggest that this flexibility may facilitate the rapid evolution of complex protein interaction networks.
Ernst
Andreas
A
Department of Protein Engineering, Genentech, 1 DNA Way, South San Francisco, CA 94080, USA.
Sazinsky
Stephen L
SL
Hui
Shirley
S
Currell
Bridget
B
Dharsee
Moyez
M
Seshagiri
Somasekar
S
Bader
Gary D
GD
Sidhu
Sachdev S
SS
eng
Journal Article
2009
09
08
United States
Sci Signal
101465400
0
Adaptor Proteins, Signal Transducing
0
ERBB2IP protein, human
0
Ligands
IM
Sci Signal. 2010;3(139):pe31
20841566
Adaptor Proteins, Signal Transducing
chemistry
genetics
Animals
Binding Sites
genetics
Directed Molecular Evolution
Humans
Ligands
Mutagenesis
Protein Structure, Tertiary
2009
9
10
6
0
2009
9
10
6
0
2009
12
16
6
0
epublish
2/87/ra50
10.1126/scisignal.2000416
19738200
19638616
2009
07
29
2009
10
26
2012
06
08
1937-9145
2
81
2009
Science signaling
Sci Signal
Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases.
ra39
10.1126/scisignal.2000316
Protein kinases enable cellular information processing. Although numerous human phosphorylation sites and their dynamics have been characterized, the evolutionary history and physiological importance of many signaling events remain unknown. Using target phosphoproteomes determined with a similar experimental and computational pipeline, we investigated the conservation of human phosphorylation events in distantly related model organisms (fly, worm, and yeast). With a sequence-alignment approach, we identified 479 phosphorylation events in 344 human proteins that appear to be positionally conserved over approximately 600 million years of evolution and hence are likely to be involved in fundamental cellular processes. This sequence-alignment analysis suggested that many phosphorylation sites evolve rapidly and therefore do not display strong evolutionary conservation in terms of sequence position in distantly related organisms. Thus, we devised a network-alignment approach to reconstruct conserved kinase-substrate networks, which identified 778 phosphorylation events in 698 human proteins. Both methods identified proteins tightly regulated by phosphorylation as well as signal integration hubs, and both types of phosphoproteins were enriched in proteins encoded by disease-associated genes. We analyzed the cellular functions and structural relationships for these conserved signaling events, noting the incomplete nature of current phosphoproteomes. Assessing phosphorylation conservation at both site and network levels proved useful for exploring both fast-evolving and ancient signaling events. We reveal that multiple complex diseases seem to converge within the conserved networks, suggesting that disease development might rely on common molecular networks.
Tan
Chris Soon Heng
CS
Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada M5G 1X5.
Bodenmiller
Bernd
B
Pasculescu
Adrian
A
Jovanovic
Marko
M
Hengartner
Michael O
MO
Jørgensen
Claus
C
Bader
Gary D
GD
Aebersold
Ruedi
R
Pawson
Tony
T
Linding
Rune
R
eng
Comparative Study
Journal Article
2009
07
28
United States
Sci Signal
101465400
0
Phosphoproteins
0
Proteins
EC 2.7.11.22
Cyclin-Dependent Kinases
EC 2.7.11.22
cyclin-dependent kinase-activating kinase
IM
Amino Acid Sequence
Animals
Binding Sites
Caenorhabditis elegans
genetics
metabolism
Cluster Analysis
Cyclin-Dependent Kinases
chemistry
genetics
metabolism
Disease Susceptibility
metabolism
physiopathology
Drosophila melanogaster
genetics
metabolism
Evolution, Molecular
Humans
Molecular Sequence Data
Molecular Structure
Phosphoproteins
classification
metabolism
Phosphorylation
Phylogeny
Protein Structure, Tertiary
Proteins
classification
genetics
metabolism
Proteomics
methods
Saccharomyces cerevisiae
genetics
metabolism
Sequence Homology, Amino Acid
Signal Transduction
physiology
2009
7
30
9
0
2009
7
30
9
0
2009
10
27
6
0
epublish
2/81/ra39
10.1126/scisignal.2000316
19638616
19589966
2009
09
25
2009
10
09
2015
06
12
1095-9203
325
5948
2009
Sep
25
Science (New York, N.Y.)
Science
Positive selection of tyrosine loss in metazoan evolution.
1686-8
10.1126/science.1174301
John Nash showed that within a complex system, individuals are best off if they make the best decision that they can, taking into account the decisions of the other individuals. Here, we investigate whether similar principles influence the evolution of signaling networks in multicellular animals. Specifically, by analyzing a set of metazoan species we observed a striking negative correlation of genomically encoded tyrosine content with biological complexity (as measured by the number of cell types in each organism). We discuss how this observed tyrosine loss correlates with the expansion of tyrosine kinases in the evolution of the metazoan lineage and how it may relate to the optimization of signaling systems in multicellular animals. We propose that this phenomenon illustrates genome-wide adaptive evolution to accommodate beneficial genetic perturbation.
Tan
Chris Soon Heng
CS
Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto M5G 1X5, Canada.
Pasculescu
Adrian
A
Lim
Wendell A
WA
Pawson
Tony
T
Bader
Gary D
GD
Linding
Rune
R
eng
R01 GM055040
GM
NIGMS NIH HHS
United States
R01 GM055040-11
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
2009
07
09
United States
Science
0404511
0036-8075
0
Fungal Proteins
0
Proteins
21820-51-9
Phosphotyrosine
42HK56048U
Tyrosine
EC 2.7.10.1
Protein-Tyrosine Kinases
IM
Nature. 2003 Dec 11;426(6967):676-80
14668868
Trends Biochem Sci. 1995 Nov;20(11):470-5
8578591
J Mol Evol. 2008 Dec;67(6):621-30
18937004
Sci Signal. 2008;1(35):ra2
18765831
Proc Natl Acad Sci U S A. 2008 Jul 15;105(28):9674-9
18621719
Proc Natl Acad Sci U S A. 2008 Jul 15;105(28):9680-4
18599463
Brief Funct Genomic Proteomic. 2008 Jan;7(1):17-26
18270216
Nature. 2008 Feb 14;451(7180):783-8
18273011
Cell. 2007 Jun 29;129(7):1415-26
17570479
Nat Rev Mol Cell Biol. 2006 Jul;7(7):473-83
16829979
PLoS Comput Biol. 2006 May;2(5):e48
16733546
Science. 2001 May 18;292(5520):1315-6
11360989
Science. 2009 Sep 25;325(5948):1635-6
19779182
Science. 2011 May 20;332(6032):917; author reply 917
21596977
Adaptation, Physiological
Animals
Biological Evolution
Evolution, Molecular
Fungal Proteins
chemistry
metabolism
Glycosylation
Humans
Methylation
Mutation
Phosphorylation
Phosphotyrosine
metabolism
Protein Structure, Tertiary
Protein-Tyrosine Kinases
metabolism
Proteins
chemistry
metabolism
Selection, Genetic
Signal Transduction
Substrate Specificity
Tyrosine
metabolism
NIHMS279874
PMC3066034
2009
7
9
2009
7
9
2009
7
11
9
0
2009
7
11
9
0
2009
10
10
6
0
ppublish
1174301
10.1126/science.1174301
19589966
PMC3066034
NIHMS279874
18828675
2008
10
02
2008
11
12
2014
09
03
1545-7885
6
9
2008
Sep
30
PLoS biology
PLoS Biol.
A specificity map for the PDZ domain family.
e239
10.1371/journal.pbio.0060239
PDZ domains are protein-protein interaction modules that recognize specific C-terminal sequences to assemble protein complexes in multicellular organisms. By scanning billions of random peptides, we accurately map binding specificity for approximately half of the over 330 PDZ domains in the human and Caenorhabditis elegans proteomes. The domains recognize features of the last seven ligand positions, and we find 16 distinct specificity classes conserved from worm to human, significantly extending the canonical two-class system based on position -2. Thus, most PDZ domains are not promiscuous, but rather are fine-tuned for specific interactions. Specificity profiling of 91 point mutants of a model PDZ domain reveals that the binding site is highly robust, as all mutants were able to recognize C-terminal peptides. However, many mutations altered specificity for ligand positions both close and far from the mutated position, suggesting that binding specificity can evolve rapidly under mutational pressure. Our specificity map enables the prediction and prioritization of natural protein interactions, which can be used to guide PDZ domain cell biology experiments. Using this approach, we predicted and validated several viral ligands for the PDZ domains of the SCRIB polarity protein. These findings indicate that many viruses produce PDZ ligands that disrupt host protein complexes for their own benefit, and that highly pathogenic strains target PDZ domains involved in cell polarity and growth.
Tonikian
Raffi
R
Terrence Donnelly Center for Cellular and Biomolecular Research, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada.
Zhang
Yingnan
Y
Sazinsky
Stephen L
SL
Currell
Bridget
B
Yeh
Jung-Hua
JH
Reva
Boris
B
Held
Heike A
HA
Appleton
Brent A
BA
Evangelista
Marie
M
Wu
Yan
Y
Xin
Xiaofeng
X
Chan
Andrew C
AC
Seshagiri
Somasekar
S
Lasky
Laurence A
LA
Sander
Chris
C
Boone
Charles
C
Bader
Gary D
GD
Sidhu
Sachdev S
SS
eng
Journal Article
Research Support, Non-U.S. Gov't
United States
PLoS Biol
101183755
1544-9173
0
Caenorhabditis elegans Proteins
0
Membrane Proteins
0
Peptides
0
Proteome
0
SCRIB protein, human
0
Tumor Suppressor Proteins
0
Viral Proteins
IM
J Cell Sci. 2001 Sep;114(Pt 18):3219-31
11591811
Bioinformatics. 2001;17 Suppl 1:S22-9
11472989
J Biol Chem. 2002 Apr 5;277(14):12275-9
11801603
J Biol Chem. 2002 Apr 12;277(15):12906-14
11821434
J Biol Chem. 2002 Jun 14;277(24):21666-74
11929862
Mol Cell. 2002 Jun;9(6):1215-25
12086619
Science. 2002 Oct 25;298(5594):846-50
12399596
Oncogene. 2003 Feb 6;22(5):710-21
12569363
J Biol Chem. 2003 Feb 28;278(9):7645-54
12446668
Science. 2003 Apr 18;300(5618):445-52
12702867
Sci STKE. 2003 Apr 22;2003(179):RE7
12709532
Bioessays. 2003 Jun;25(6):542-53
12766944
Cancer Res. 2003 Aug 1;63(15):4547-51
12907630
Science. 2003 Sep 26;301(5641):1904-8
14512628
Genome Res. 2003 Nov;13(11):2498-504
14597658
BMC Bioinformatics. 2004 Aug 19;5:113
15318951
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15378037
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8612272
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8974395
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10514373
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Annu Rev Biochem. 2005;74:219-45
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Immunity. 2005 Jun;22(6):737-48
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Oncogene. 2005 Sep 5;24(39):5965-75
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Nature. 2005 Sep 22;437(7058):579-83
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Cell. 2006 Jan 13;124(1):133-45
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Clin Sci (Lond). 2006 May;110(5):525-41
16597322
PLoS Comput Biol. 2006 May;2(5):e48
16733546
J Biol Chem. 2006 Aug 4;281(31):22299-311
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J Biol Chem. 2006 Aug 4;281(31):22312-20
16737969
Brain Res Rev. 2006 Sep;52(2):305-15
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ACS Chem Biol. 2006 Sep 19;1(8):525-33
17168540
Nucleic Acids Res. 2007 Jan;35(Database issue):D610-7
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Nat Protoc. 2007;2(6):1368-86
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Annu Rev Neurosci. 2001;24:1-29
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J Biol Chem. 2001 Nov 9;276(45):42122-30
11509564
Amino Acid Sequence
Animals
Binding Sites
genetics
Caenorhabditis elegans Proteins
analysis
chemistry
classification
genetics
Humans
Membrane Proteins
chemistry
genetics
metabolism
Models, Molecular
Molecular Sequence Data
Mutation
PDZ Domains
Peptides
analysis
genetics
Phylogeny
Protein Structure, Secondary
Proteome
analysis
Tumor Suppressor Proteins
chemistry
genetics
metabolism
Viral Proteins
genetics
metabolism
PMC2553845
2007
12
14
2008
8
19
2008
10
3
9
0
2008
11
13
9
0
2008
10
3
9
0
ppublish
07-PLBI-RA-4212
10.1371/journal.pbio.0060239
18828675
PMC2553845
18819078
2008
09
26
2008
11
04
1934-340X
Chapter 8
2008
Sep
Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.]
Curr Protoc Bioinformatics
Exploring biological networks with Cytoscape software.
Unit 8.13
10.1002/0471250953.bi0813s23
Cytoscape is a free software package for visualizing, modeling, and analyzing molecular and genetic interaction networks. As a key feature, Cytoscape enables biologists to determine and analyze the interconnectivity of a list of genes or proteins. This unit explains how to use Cytoscape to load and navigate biological network information and view mRNA expression profiles and other functional genomics and proteomics data in the context of the network obtained for genes of interest. Additional analyses that can be performed with Cytoscape are also discussed.
(c) 2008 by John Wiley & Sons, Inc.
Yeung
Natalie
N
University of Toronto, Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada.
Cline
Melissa S
MS
Kuchinsky
Allan
A
Smoot
Michael E
ME
Bader
Gary D
GD
eng
GM070743-01
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
United States
Curr Protoc Bioinformatics
101157830
1934-3396
IM
Animals
Computational Biology
Computer Simulation
Database Management Systems
utilization
Gene Expression Profiling
utilization
Gene Regulatory Networks
Genomics
methods
Humans
Proteomics
methods
Quantitative Structure-Activity Relationship
Software
2008
9
27
9
0
2008
11
5
9
0
2008
9
27
9
0
ppublish
10.1002/0471250953.bi0813s23
18819078
18445605
2008
06
16
2008
07
10
2014
09
03
1367-4811
24
12
2008
Jun
15
Bioinformatics (Oxford, England)
Bioinformatics
Cytoscape ESP: simple search of complex biological networks.
1465-6
10.1093/bioinformatics/btn208
Cytoscape enhanced search plugin (ESP) enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. Availabiity: http://chianti.ucsd.edu/cyto_web/plugins/
ashkenaz@agri.huji.ac.il.
Ashkenazi
Maital
M
Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Hebrew University of Jerusalem, Rehovot, Israel. ashkenaz@agri.huji.ac.il
Bader
Gary D
GD
Kuchinsky
Allan
A
Moshelion
Menachem
M
States
David J
DJ
eng
DA021519
DA
NIDA NIH HHS
United States
GM070743-01
GM
NIGMS NIH HHS
United States
LM008106
LM
NLM NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2008
04
28
England
Bioinformatics
9808944
1367-4803
IM
Nucleic Acids Res. 2002 Jan 1;30(1):303-5
11752321
Genome Res. 2003 Nov;13(11):2498-504
14597658
Planta. 2003 Nov;218(1):1-14
14513379
Plant J. 2007 Apr;50(2):347-63
17376166
Plant Physiol. 2005 Oct;139(2):790-805
16183846
Plant Physiol. 2005 Nov;139(3):1313-22
16244138
Nucleic Acids Res. 2007 Jan;35(Database issue):D566-71
17130145
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D452-5
14681455
Algorithms
Computer Graphics
Computer Simulation
Information Storage and Retrieval
methods
Models, Biological
Signal Transduction
physiology
Software
User-Computer Interface
PMC2427162
2008
4
28
2008
5
1
9
0
2008
7
11
9
0
2008
5
1
9
0
ppublish
btn208
10.1093/bioinformatics/btn208
18445605
PMC2427162
18095187
2007
12
20
2008
02
11
1073-6085
38
1
2008
Jan
Molecular biotechnology
Mol. Biotechnol.
Computational prediction of protein-protein interactions.
1-17
Recently a number of computational approaches have been developed for the prediction of protein-protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.
Skrabanek
Lucy
L
Department of Physiology and Biophysics and Institute for Computational Biomedicine, Weill Medical College of Cornell University, 1300 York Avenue, New York, NY 10021, USA.
Saini
Harpreet K
HK
Bader
Gary D
GD
Enright
Anton J
AJ
eng
Journal Article
Review
2007
08
14
United States
Mol Biotechnol
9423533
1073-6085
0
Multiprotein Complexes
IM
Biotechnology
Computer Simulation
Databases, Genetic
Gene Fusion
Genomics
Models, Molecular
Multiprotein Complexes
Phylogeny
Protein Array Analysis
Protein Interaction Mapping
statistics & numerical data
Proteomics
Software
Two-Hybrid System Techniques
103
2007
7
4
2007
7
16
2007
8
14
2007
12
21
9
0
2008
2
12
9
0
2007
12
21
9
0
ppublish
10.1007/s12033-007-0069-2
18095187
17947979
2007
10
19
2008
01
22
2014
09
16
1750-2799
2
10
2007
Nature protocols
Nat Protoc
Integration of biological networks and gene expression data using Cytoscape.
2366-82
Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
Cline
Melissa S
MS
Institut Pasteur, 25-28 rue du Docteur Roux, 75724 Paris cedex 15, France.
Smoot
Michael
M
Cerami
Ethan
E
Kuchinsky
Allan
A
Landys
Nerius
N
Workman
Chris
C
Christmas
Rowan
R
Avila-Campilo
Iliana
I
Creech
Michael
M
Gross
Benjamin
B
Hanspers
Kristina
K
Isserlin
Ruth
R
Kelley
Ryan
R
Killcoyne
Sarah
S
Lotia
Samad
S
Maere
Steven
S
Morris
John
J
Ono
Keiichiro
K
Pavlovic
Vuk
V
Pico
Alexander R
AR
Vailaya
Aditya
A
Wang
Peng-Liang
PL
Adler
Annette
A
Conklin
Bruce R
BR
Hood
Leroy
L
Kuiper
Martin
M
Sander
Chris
C
Schmulevich
Ilya
I
Schwikowski
Benno
B
Warner
Guy J
GJ
Ideker
Trey
T
Bader
Gary D
GD
eng
GM070743-01
GM
NIGMS NIH HHS
United States
R01 GM070743
GM
NIGMS NIH HHS
United States
R01 GM070743-01
GM
NIGMS NIH HHS
United States
R01 GM070743-02
GM
NIGMS NIH HHS
United States
R01 GM070743-03
GM
NIGMS NIH HHS
United States
R01 GM070743-03S1
GM
NIGMS NIH HHS
United States
R01 GM070743-04
GM
NIGMS NIH HHS
United States
R01 GM070743-05
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
England
Nat Protoc
101284307
1750-2799
0
RNA, Messenger
IM
Science. 2005 Feb 4;307(5710):724-7
15692050
Proc Natl Acad Sci U S A. 2005 Feb 8;102(6):1974-9
15687504
Bioinformatics. 2005 Feb 15;21(4):430-8
15608051
Genome Biol. 2005;6(4):R38
15833125
Nat Biotechnol. 2005 May;23(5):561-6
15877074
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W352-7
15980487
Genome Biol. 2005;6(7):R62
15998451
Genome Biol. 2005;6(7):224
15998455
Appl Bioinformatics. 2005;4(1):71-4
16000016
Bioinformatics. 2005 Aug 15;21(16):3448-9
15972284
Nat Biotechnol. 2005 Aug;23(8):951-9
16082366
Nature. 2005 Aug 11;436(7052):861-5
16094371
Physiol Genomics. 2005 Sep 21;23(1):103-18
15942018
Bioinformatics. 2005 Sep 1;21 Suppl 2:ii220-1
16204107
Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
16199517
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D319-21
16381876
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D411-4
16381900
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D504-6
16381921
Proc IEEE Comput Syst Bioinform Conf. 2004;:216-23
16448015
BMC Evol Biol. 2006;6:8
16441898
Cell. 2006 Mar 10;124(5):1069-81
16487579
Nature. 2006 Mar 30;440(7084):637-43
16554755
Bioinformatics. 2006 Apr 15;22(8):1015-7
16510498
Cell. 2006 Apr 7;125(1):173-86
16615898
Annu Rev Plant Biol. 2006;57:451-75
16669770
Nature. 2006 May 11;441(7090):173-8
16688168
Bioinformatics. 2006 Aug 15;22(16):2044-6
16777906
Bioinformatics. 2006 Sep 1;22(17):2178-9
16921162
Methods Enzymol. 2006;411:352-69
16939800
Mol Syst Biol. 2006;2:63
17102808
Bioinformatics. 2006 Dec 1;22(23):2968-70
17021160
BMC Bioinformatics. 2006;7:497
17101041
Brief Bioinform. 2006 Dec;7(4):331-8
17132622
Genome Biol. 2006;7(9):R83
16973001
Nucleic Acids Res. 2007 Jan;35(Database issue):D566-71
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Nucleic Acids Res. 2007 Jan;35(Database issue):D747-50
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Bioinformatics. 2007 Jan 15;23(2):207-14
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Bioinformatics. 2007 Feb 1;23(3):394-6
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Bioinformatics. 2007 Feb 1;23(3):392-3
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Genome Biol. 2007;8(1):R7
17217541
Nat Protoc. 2006;1(2):662-71
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Nat Protoc. 2007;2(3):685-94
17406631
Bioinformatics. 2007 Apr 1;23(7):910-2
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Bioinformatics. 2007 Apr 15;23(8):1040-2
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PLoS Comput Biol. 2007 Apr 20;3(4):e59
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Comput Biol Chem. 2007 Jun;31(3):222-5
17500038
Science. 2007 Jun 8;316(5830):1497-502
17540862
Bioinformatics. 2007 Aug 15;23(16):2193-5
17553858
Nat Genet. 2000 May;25(1):25-9
10802651
Yeast. 2000 Apr;17(1):48-55
10928937
Science. 2001 May 4;292(5518):929-34
11340206
FEBS Lett. 2002 Feb 20;513(1):135-40
11911893
Nat Genet. 2002 May;31(1):19-20
11984561
Bioinformatics. 2002 Jul;18(7):996-1003
12117798
Bioinformatics. 2002;18 Suppl 1:S233-40
12169552
Genome Biol. 2003;4(1):R7
12540299
Nucleic Acids Res. 2003 Feb 15;31(4):e15
12582260
Genome Biol. 2003;4(3):R22
12620107
J Mol Biol. 2003 Apr 11;327(5):919-23
12662919
Genome Biol. 2003;4(4):R28
12702209
Trends Cell Biol. 2003 Jul;13(7):344-56
12837605
Nature. 2003 Oct 16;425(6959):737-41
14562106
BMC Bioinformatics. 2003 Jan 13;4:2
12525261
Genome Res. 2003 Nov;13(11):2498-504
14597658
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D452-5
14681455
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D497-501
14681466
Nat Biotechnol. 2004 Feb;22(2):177-83
14755292
Science. 2004 Feb 6;303(5659):808-13
14764870
Nat Biotechnol. 2004 Oct;22(10):1253-9
15470465
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D428-32
15608231
Bioinformatics. 2005 Jan 15;21(2):272-4
15347570
Computational Biology
methods
Gene Expression Profiling
methods
Gene Regulatory Networks
Genomics
methods
Proteomics
methods
RNA, Messenger
metabolism
Software
NIHMS170904
PMC3685583
2007
10
20
9
0
2008
1
23
9
0
2007
10
20
9
0
ppublish
nprot.2007.324
10.1038/nprot.2007.324
17947979
PMC3685583
NIHMS170904
17925023
2008
01
08
2008
01
18
2014
09
04
1741-7007
5
2007
BMC biology
BMC Biol.
Broadening the horizon--level 2.5 of the HUPO-PSI format for molecular interactions.
44
Molecular interaction Information is a key resource in modern biomedical research. Publicly available data have previously been provided in a broad array of diverse formats, making access to this very difficult. The publication and wide implementation of the Human Proteome Organisation Proteomics Standards Initiative Molecular Interactions (HUPO PSI-MI) format in 2004 was a major step towards the establishment of a single, unified format by which molecular interactions should be presented, but focused purely on protein-protein interactions.
The HUPO-PSI has further developed the PSI-MI XML schema to enable the description of interactions between a wider range of molecular types, for example nucleic acids, chemical entities, and molecular complexes. Extensive details about each supported molecular interaction can now be captured, including the biological role of each molecule within that interaction, detailed description of interacting domains, and the kinetic parameters of the interaction. The format is supported by data management and analysis tools and has been adopted by major interaction data providers. Additionally, a simpler, tab-delimited format MITAB2.5 has been developed for the benefit of users who require only minimal information in an easy to access configuration.
The PSI-MI XML2.5 and MITAB2.5 formats have been jointly developed by interaction data producers and providers from both the academic and commercial sector, and are already widely implemented and well supported by an active development community. PSI-MI XML2.5 enables the description of highly detailed molecular interaction data and facilitates data exchange between databases and users without loss of information. MITAB2.5 is a simpler format appropriate for fast Perl parsing or loading into Microsoft Excel.
Kerrien
Samuel
S
European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. skerrien@ebi.ac.uk
Orchard
Sandra
S
Montecchi-Palazzi
Luisa
L
Aranda
Bruno
B
Quinn
Antony F
AF
Vinod
Nisha
N
Bader
Gary D
GD
Xenarios
Ioannis
I
Wojcik
Jérôme
J
Sherman
David
D
Tyers
Mike
M
Salama
John J
JJ
Moore
Susan
S
Ceol
Arnaud
A
Chatr-Aryamontri
Andrew
A
Oesterheld
Matthias
M
Stümpflen
Volker
V
Salwinski
Lukasz
L
Nerothin
Jason
J
Cerami
Ethan
E
Cusick
Michael E
ME
Vidal
Marc
M
Gilson
Michael
M
Armstrong
John
J
Woollard
Peter
P
Hogue
Christopher
C
Eisenberg
David
D
Cesareni
Gianni
G
Apweiler
Rolf
R
Hermjakob
Henning
H
eng
1 R01 GM071909
GM
NIGMS NIH HHS
United States
GM070064
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
2007
10
09
England
BMC Biol
101190720
1741-7007
IM
EMBO J. 2001 Mar 1;20(5):1153-63
11230138
Nat Biotechnol. 2007 Aug;25(8):894-8
17687370
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D449-51
14681454
Nat Biotechnol. 2004 Feb;22(2):177-83
14755292
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D418-24
15608229
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D436-41
16381906
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9
16381927
Proteomics. 2006 Jan;6(1):4-8
16400714
BMC Bioinformatics. 2006;7:97
16507094
Mol Cell Proteomics. 2006 May;5(5):787-8
16670253
OMICS. 2006 Summer;10(2):179-84
16901224
Nucleic Acids Res. 2007 Jan;35(Database issue):D572-4
17135203
Nucleic Acids Res. 2007 Jan;35(Database issue):D193-7
17142230
Nucleic Acids Res. 2007 Jan;35(Database issue):D198-201
17145705
Nucleic Acids Res. 2007 Jan;35(Database issue):D561-5
17145710
Nucleic Acids Res. 2007 Jan;35(Database issue):D5-12
17170002
Genome Res. 2003 Nov;13(11):2498-504
14597658
Computational Biology
Computer Graphics
Database Management Systems
Databases, Protein
standards
Natural Language Processing
Protein Interaction Mapping
methods
Proteomics
methods
standards
User-Computer Interface
PMC2189715
2007
2
19
2007
10
09
2007
10
09
2007
10
11
9
0
2008
1
19
9
0
2007
10
11
9
0
epublish
1741-7007-5-44
10.1186/1741-7007-5-44
17925023
PMC2189715
17687370
2007
08
09
2007
11
13
2007
11
15
1087-0156
25
8
2007
Aug
Nature biotechnology
Nat. Biotechnol.
The minimum information required for reporting a molecular interaction experiment (MIMIx).
894-8
A wealth of molecular interaction data is available in the literature, ranging from large-scale datasets to a single interaction confirmed by several different techniques. These data are all too often reported either as free text or in tables of variable format, and are often missing key pieces of information essential for a full understanding of the experiment. Here we propose MIMIx, the minimum information required for reporting a molecular interaction experiment. Adherence to these reporting guidelines will result in publications of increased clarity and usefulness to the scientific community and will support the rapid, systematic capture of molecular interaction data in public databases, thereby improving access to valuable interaction data.
Orchard
Sandra
S
European Molecular Biology Laboratory (EMBL) - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK. orchard@ebi.ac.uk
Salwinski
Lukasz
L
Kerrien
Samuel
S
Montecchi-Palazzi
Luisa
L
Oesterheld
Matthias
M
Stümpflen
Volker
V
Ceol
Arnaud
A
Chatr-aryamontri
Andrew
A
Armstrong
John
J
Woollard
Peter
P
Salama
John J
JJ
Moore
Susan
S
Wojcik
Jérôme
J
Bader
Gary D
GD
Vidal
Marc
M
Cusick
Michael E
ME
Gerstein
Mark
M
Gavin
Anne-Claude
AC
Superti-Furga
Giulio
G
Greenblatt
Jack
J
Bader
Joel
J
Uetz
Peter
P
Tyers
Mike
M
Legrain
Pierre
P
Fields
Stan
S
Mulder
Nicola
N
Gilson
Michael
M
Niepmann
Michael
M
Burgoon
Lyle
L
De Las Rivas
Javier
J
Prieto
Carlos
C
Perreau
Victoria M
VM
Hogue
Chris
C
Mewes
Hans-Werner
HW
Apweiler
Rolf
R
Xenarios
Ioannis
I
Eisenberg
David
D
Cesareni
Gianni
G
Hermjakob
Henning
H
eng
Journal Article
Review
United States
Nat Biotechnol
9604648
1087-0156
IM
Databases, Protein
standards
Guidelines as Topic
Humans
Information Storage and Retrieval
standards
Internationality
Protein Interaction Mapping
standards
Proteomics
standards
Research
standards
21
2007
8
10
9
0
2007
11
14
9
0
2007
8
10
9
0
ppublish
nbt1324
10.1038/nbt1324
17687370
17638019
2007
10
18
2008
04
02
2008
11
21
0340-6717
122
3-4
2007
Nov
Human genetics
Hum. Genet.
Germ-line DNA copy number variation frequencies in a large North American population.
345-53
Genomic copy number variation (CNV) is a recently identified form of global genetic variation in the human genome. The Affymetrix GeneChip 100 and 500 K SNP genotyping platforms were used to perform a large-scale population-based study of CNV frequency. We constructed a genomic map of 578 CNV regions, covering approximately 220 Mb (7.3%) of the human genome, identifying 183 previously unknown intervals. Copy number changes were observed to occur infrequently (<1%) in the majority (>93%) of these genomic regions, but encompass hundreds of genes and disease loci. This North American population-based map will be a useful resource for future genetic studies.
Zogopoulos
George
G
Sam Minuk Cancer Genetics and Biomarker Laboratories, Fred Litwin Centre for Cancer Genetics, Samuel Lunenfeld Research Institute, Toronto, Canada.
Ha
Kevin C H
KC
Naqib
Faisal
F
Moore
Sara
S
Kim
Hyeja
H
Montpetit
Alexandre
A
Robidoux
Frederick
F
Laflamme
Philippe
P
Cotterchio
Michelle
M
Greenwood
Celia
C
Scherer
Stephen W
SW
Zanke
Brent
B
Hudson
Thomas J
TJ
Bader
Gary D
GD
Gallinger
Steven
S
eng
CA-96-011
CA
NCI NIH HHS
United States
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2007
07
19
Germany
Hum Genet
7613873
0340-6717
9007-49-2
DNA
IM
Aged
Chromosome Mapping
DNA
genetics
Female
Gene Dosage
Gene Frequency
Genetic Variation
Genetics, Population
Genome, Human
Germ Cells
metabolism
Humans
Male
Middle Aged
Oligonucleotide Array Sequence Analysis
Ontario
Polymerase Chain Reaction
Polymorphism, Single Nucleotide
Registries
2007
5
25
2007
7
9
2007
7
19
2007
7
20
9
0
2008
4
3
9
0
2007
7
20
9
0
ppublish
10.1007/s00439-007-0404-5
17638019
17319736
2007
02
26
2007
03
19
2014
09
07
1553-7358
3
2
2007
Feb
23
PLoS computational biology
PLoS Comput. Biol.
From bytes to bedside: data integration and computational biology for translational cancer research.
e12
Mathew
Jomol P
JP
Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America. Jomol_Mathew@dfci.harvard.edu
Taylor
Barry S
BS
Bader
Gary D
GD
Pyarajan
Saiju
S
Antoniotti
Marco
M
Chinnaiyan
Arul M
AM
Sander
Chris
C
Burakoff
Steven J
SJ
Mishra
Bud
B
eng
Journal Article
Review
United States
PLoS Comput Biol
101238922
1553-734X
0
Neoplasm Proteins
IM
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15502875
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15591277
Biomedical Research
methods
trends
Computational Biology
methods
trends
Databases, Factual
Medical Oncology
methods
trends
Neoplasm Proteins
metabolism
Neoplasms
physiopathology
Research
trends
Research Design
Systems Integration
102
PMC1808026
2007
2
27
9
0
2007
3
21
9
0
2007
2
27
9
0
ppublish
06-PLCB-PV-0255R2
10.1371/journal.pcbi.0030012
17319736
PMC1808026
17277332
2007
04
25
2007
05
15
2013
05
20
1367-4811
23
7
2007
Apr
1
Bioinformatics (Oxford, England)
Bioinformatics
NetMatch: a Cytoscape plugin for searching biological networks.
910-2
NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks.
The full package, a tutorial and associated examples are available at the following web sites: http://alpha.dmi.unict.it/~ctnyu/netmatch.html, http://baderlab.org/Software/NetMatch.
Ferro
A
A
Dipartimento di Matematica e Informatica, Università di Catania, Viale A. Doria 6, I-95125 Catania, Italy. ferro@dmi.unict.it
Giugno
R
R
Pigola
G
G
Pulvirenti
A
A
Skripin
D
D
Bader
G D
GD
Shasha
D
D
eng
Journal Article
2007
02
03
England
Bioinformatics
9808944
1367-4803
IM
Algorithms
Computer Graphics
Computer Simulation
Database Management Systems
Information Storage and Retrieval
methods
Models, Biological
Signal Transduction
physiology
Software
User-Computer Interface
2007
2
3
2007
2
6
9
0
2007
5
16
9
0
2007
2
6
9
0
ppublish
btm032
10.1093/bioinformatics/btm032
17277332
17101041
2006
11
23
2007
01
03
2014
09
07
1471-2105
7
2006
BMC bioinformatics
BMC Bioinformatics
cPath: open source software for collecting, storing, and querying biological pathways.
497
Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required.
We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use.
cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling.
Cerami
Ethan G
EG
Computational Biology Center, Memorial Sloan-Kettering Cancer Center 1275 York Avenue, Box 460, New York, NY 10021, USA. cpath-bmc@cbio.mskcc.org
Bader
Gary D
GD
Gross
Benjamin E
BE
Sander
Chris
C
eng
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
2006
11
13
England
BMC Bioinformatics
100965194
1471-2105
IM
Bioinformatics. 2006 Apr 15;22(8):1015-7
16510498
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D689-91
16381960
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16533819
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10592249
Cell. 2000 Jan 7;100(1):57-70
10647931
Science. 2000 Mar 17;287(5460):1977-8
10755952
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11340206
Annu Rev Genomics Hum Genet. 2001;2:343-72
11701654
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11847095
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11872829
FEBS Lett. 2002 Feb 20;513(1):135-40
11911893
Nature. 2002 May 9;417(6885):119-20
12000935
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12611808
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12728276
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16480510
Computational Biology
methods
Computer Graphics
Databases, Factual
Humans
Information Storage and Retrieval
Internet
Programming Languages
Signal Transduction
Software
Software Design
Systems Integration
User-Computer Interface
PMC1660554
2006
6
16
2006
11
13
2006
11
13
2006
11
15
9
0
2007
1
4
9
0
2006
11
15
9
0
epublish
1471-2105-7-497
10.1186/1471-2105-7-497
17101041
PMC1660554
16381921
2005
12
29
2006
02
28
2014
09
09
1362-4962
34
Database issue
2006
Jan
1
Nucleic acids research
Nucleic Acids Res.
Pathguide: a pathway resource list.
D504-6
Pathguide: the Pathway Resource List (http://pathguide.org) is a meta-database that provides an overview of more than 190 web-accessible biological pathway and network databases. These include databases on metabolic pathways, signaling pathways, transcription factor targets, gene regulatory networks, genetic interactions, protein-compound interactions, and protein-protein interactions. The listed databases are maintained by diverse groups in different locations and the information in them is derived either from the scientific literature or from systematic experiments. Pathguide is useful as a starting point for biological pathway analysis and for content aggregation in integrated biological information systems.
Bader
Gary D
GD
Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10021, USA.
Cary
Michael P
MP
Sander
Chris
C
eng
Journal Article
Research Support, Non-U.S. Gov't
England
Nucleic Acids Res
0411011
0305-1048
0
Proteins
0
Transcription Factors
IM
Bioinformatics. 2003 Mar 1;19(4):524-31
12611808
Nat Biotechnol. 2004 Feb;22(2):177-83
14755292
Prog Biophys Mol Biol. 2004 Jun-Jul;85(2-3):433-50
15142756
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D5-24
15608247
FEBS Lett. 2005 Mar 21;579(8):1815-20
15763557
Databases, Genetic
standards
Gene Expression Regulation
Internet
Metabolism
Proteins
chemistry
metabolism
Signal Transduction
Systems Integration
Transcription Factors
metabolism
User-Computer Interface
PMC1347488
2005
12
31
9
0
2006
3
1
9
0
2005
12
31
9
0
ppublish
34/suppl_1/D504
10.1093/nar/gkj126
16381921
PMC1347488
15763557
2005
03
14
2005
04
05
2005
11
16
0014-5793
579
8
2005
Mar
21
FEBS letters
FEBS Lett.
Pathway information for systems biology.
1815-20
Pathway information is vital for successful quantitative modeling of biological systems. The almost 170 online pathway databases vary widely in coverage and representation of biological processes, making their use extremely difficult. Future pathway information systems for querying, visualization and analysis must support standard exchange formats to successfully integrate data on a large scale. Such integrated systems will greatly facilitate the constructive cycle of computational model building and experimental verification that lies at the heart of systems biology.
Cary
Michael P
MP
Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10021, USA.
Bader
Gary D
GD
Sander
Chris
C
eng
Journal Article
Review
Netherlands
FEBS Lett
0155157
0014-5793
IM
Animals
Computational Biology
Databases, Factual
Humans
Models, Biological
Protein Binding
Signal Transduction
51
2005
1
31
2005
2
1
2005
2
1
2005
3
15
9
0
2005
4
6
9
0
2005
3
15
9
0
ppublish
S0014-5793(05)00170-5
10.1016/j.febslet.2005.02.005
15763557
14764870
2004
02
06
2004
03
04
2007
11
14
1095-9203
303
5659
2004
Feb
6
Science (New York, N.Y.)
Science
Global mapping of the yeast genetic interaction network.
808-13
A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.
Tong
Amy Hin Yan
AH
Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada M5G 1L6.
Lesage
Guillaume
G
Bader
Gary D
GD
Ding
Huiming
H
Xu
Hong
H
Xin
Xiaofeng
X
Young
James
J
Berriz
Gabriel F
GF
Brost
Renee L
RL
Chang
Michael
M
Chen
YiQun
Y
Cheng
Xin
X
Chua
Gordon
G
Friesen
Helena
H
Goldberg
Debra S
DS
Haynes
Jennifer
J
Humphries
Christine
C
He
Grace
G
Hussein
Shamiza
S
Ke
Lizhu
L
Krogan
Nevan
N
Li
Zhijian
Z
Levinson
Joshua N
JN
Lu
Hong
H
Ménard
Patrice
P
Munyana
Christella
C
Parsons
Ainslie B
AB
Ryan
Owen
O
Tonikian
Raffi
R
Roberts
Tania
T
Sdicu
Anne-Marie
AM
Shapiro
Jesse
J
Sheikh
Bilal
B
Suter
Bernhard
B
Wong
Sharyl L
SL
Zhang
Lan V
LV
Zhu
Hongwei
H
Burd
Christopher G
CG
Munro
Sean
S
Sander
Chris
C
Rine
Jasper
J
Greenblatt
Jack
J
Peter
Matthias
M
Bretscher
Anthony
A
Bell
Graham
G
Roth
Frederick P
FP
Brown
Grant W
GW
Andrews
Brenda
B
Bussey
Howard
H
Boone
Charles
C
eng
GM39066
GM
NIGMS NIH HHS
United States
GM61221
GM
NIGMS NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.
United States
Science
0404511
0036-8075
0
Saccharomyces cerevisiae Proteins
IM
Science. 2004 Feb 6;303(5659):774-5
14764857
Amino Acid Sequence
Computational Biology
Cystic Fibrosis
genetics
Gene Deletion
Genes, Essential
Genes, Fungal
Genetic Diseases, Inborn
genetics
Genotype
Humans
Molecular Sequence Data
Multifactorial Inheritance
Mutation
Phenotype
Polymorphism, Genetic
Retinitis Pigmentosa
genetics
Saccharomyces cerevisiae
genetics
metabolism
Saccharomyces cerevisiae Proteins
chemistry
genetics
metabolism
2004
2
7
5
0
2004
3
5
5
0
2004
2
7
5
0
ppublish
14764870
10.1126/science.1091317
303/5659/808
14595360
2003
11
03
2004
06
24
1087-0156
21
11
2003
Nov
Nature biotechnology
Nat. Biotechnol.
Playing tag with the yeast proteome.
1297-9
Andrews
Brenda J
BJ
Bader
Gary D
GD
Boone
Charles
C
eng
News
United States
Nat Biotechnol
9604648
1087-0156
0
Epitopes
0
Proteome
0
RNA, Fungal
0
Recombinant Fusion Proteins
0
Saccharomyces cerevisiae Proteins
IM
Epitopes
analysis
genetics
Expressed Sequence Tags
Gene Expression Profiling
methods
Genome, Fungal
Open Reading Frames
genetics
Proteome
genetics
metabolism
Proteomics
methods
RNA, Fungal
genetics
metabolism
Recombinant Fusion Proteins
chemistry
genetics
metabolism
Saccharomyces cerevisiae
genetics
growth & development
metabolism
Saccharomyces cerevisiae Proteins
genetics
metabolism
2003
11
5
5
0
2004
6
25
5
0
2003
11
5
5
0
ppublish
14595360
10.1038/nbt1103-1297
nbt1103-1297
12837605
2003
07
02
2003
09
05
2004
11
17
0962-8924
13
7
2003
Jul
Trends in cell biology
Trends Cell Biol.
Functional genomics and proteomics: charting a multidimensional map of the yeast cell.
344-56
The challenge of large-scale functional genomics projects is to build a comprehensive map of the cell including genome sequence and gene expression data, information on protein localization, structure, function and expression, post-translational modifications, molecular and genetic interactions and phenotypic descriptions. Some of this broad set of functional genomics data has been already assembled for the budding yeast. Even though molecular cartography of the yeast cell is still far from comprehensive, functional genomics has begun to forge connections between disparate cellular events and to foster numerous hypotheses. Here we review several different genomics and proteomics technologies and describe bioinformatics methods for exploring these data to make new discoveries.
Bader
Gary D
GD
Computational Biology Center, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, Box 460, 10021, New York, NY, USA.
Heilbut
Adrian
A
Andrews
Brenda
B
Tyers
Mike
M
Hughes
Timothy
T
Boone
Charles
C
eng
Journal Article
Review
England
Trends Cell Biol
9200566
0962-8924
0
Nuclear Proteins
0
Saccharomyces cerevisiae Proteins
IM
Chromosome Mapping
Computational Biology
Gene Expression Regulation, Fungal
genetics
Genomics
Nuclear Proteins
genetics
metabolism
Proteomics
Saccharomyces cerevisiae
genetics
metabolism
ultrastructure
Saccharomyces cerevisiae Proteins
genetics
metabolism
Signal Transduction
genetics
138
2003
7
3
5
0
2003
9
6
5
0
2003
7
3
5
0
ppublish
12837605
S0962892403001272
12689350
2003
10
29
2003
12
04
2014
06
11
1471-2105
4
2003
Mar
27
BMC bioinformatics
BMC Bioinformatics
PreBIND and Textomy--mining the biomedical literature for protein-protein interactions using a support vector machine.
11
The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND.
Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days.
Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.
Donaldson
Ian
I
Samuel Lunenfeld Research Institute, Toronto, M5G 1X5, Canada. ian.donaldson@utoronto.ca
Martin
Joel
J
de Bruijn
Berry
B
Wolting
Cheryl
C
Lay
Vicki
V
Tuekam
Brigitte
B
Zhang
Shudong
S
Baskin
Berivan
B
Bader
Gary D
GD
Michalickova
Katerina
K
Pawson
Tony
T
Hogue
Christopher W V
CW
eng
Comparative Study
Evaluation Studies
Journal Article
Validation Studies
2003
03
27
England
BMC Bioinformatics
100965194
1471-2105
0
Saccharomyces cerevisiae Proteins
IM
Bioinformatics. 2000 May;16(5):465-77
10871269
Nat Biotechnol. 2002 Oct;20(10):991-7
12355115
Pac Symp Biocomput. 2000;:517-28
10902199
Pac Symp Biocomput. 2000;:529-40
10902200
Pac Symp Biocomput. 2000;:541-52
10902201
Proc Int Conf Intell Syst Mol Biol. 2000;8:279-85
10977089
Nucleic Acids Res. 2001 Jan 1;29(1):137-40
11125071
Nucleic Acids Res. 2003 Jan 1;31(1):365-70
12520024
BMC Bioinformatics. 2002 Oct 25;3:32
12401134
Pac Symp Biocomput. 1998;:707-18
9697224
Nucleic Acids Res. 2001 Jan 1;29(1):242-5
11125103
Bioinformatics. 2001 Feb;17(2):155-61
11238071
Pac Symp Biocomput. 2001;:520-31
11262970
Bioinformatics. 2001 Apr;17(4):359-63
11301305
Nat Genet. 2001 May;28(1):21-8
11326270
Methods Biochem Anal. 2001;43:19-43
11449725
Bioinformatics. 2001;17 Suppl 1:S74-82
11472995
Nucleic Acids Res. 2002 Jan 1;30(1):13-6
11752242
Nucleic Acids Res. 2002 Jan 1;30(1):31-4
11752246
Nucleic Acids Res. 2002 Jan 1;30(1):69-72
11752257
Genome Inform. 2001;12:123-34
11791231
Nature. 2002 Jan 10;415(6868):180-3
11805837
Pac Symp Biocomput. 2000;:505-16
10902198
Algorithms
Artificial Intelligence
Computational Biology
methods
statistics & numerical data
Databases, Factual
trends
Databases, Protein
trends
Genome, Fungal
Information Storage and Retrieval
trends
Protein Interaction Mapping
classification
methods
statistics & numerical data
PubMed
classification
Saccharomyces cerevisiae
genetics
Saccharomyces cerevisiae Proteins
chemistry
PMC153503
2003
4
12
5
0
2003
12
5
5
0
2002
Dec
28
2003
Mar
27
2003
Mar
27
2003
4
12
5
0
ppublish
12689350
PMC153503
12547433
2003
01
27
2003
11
06
2009
08
25
1367-5931
7
1
2003
Feb
Current opinion in chemical biology
Curr Opin Chem Biol
Functional genomics of intracellular peptide recognition domains with combinatorial biology methods.
97-102
Phage-displayed peptide libraries have been used to identify specific ligands for peptide-binding domains that mediate intracellular protein-protein interactions. These studies have provided significant insights into the specificities of particular domains. For PDZ domains that recognize C-terminal sequences, the information has proven useful in identifying natural binding partners from genomic databases. For SH3 domains that recognize internal proline-rich motifs, the results of database searches with phage-derived ligands have been compared with the results of yeast-two-hybrid experiments to produce overlap networks that reliably predict natural protein-protein interactions. In addition, libraries of phage-displayed PDZ and SH3 domains have been used to identify the residues responsible for ligand recognition, and also to engineer domains with altered specificities.
Sidhu
Sachdev S
SS
Department of Protein Engineering, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA. sidhu@gene.com
Bader
Gary D
GD
Boone
Charles
C
eng
Journal Article
Review
England
Curr Opin Chem Biol
9811312
1367-5931
0
Peptide Library
0
Peptides
IM
Databases, Protein
Genomics
Peptide Library
Peptides
chemistry
metabolism
Protein Binding
Protein Structure, Tertiary
Structural Homology, Protein
Two-Hybrid System Techniques
38
2003
1
28
4
0
2003
11
7
5
0
2003
1
28
4
0
ppublish
12547433
S136759310200011X
12525261
2003
10
29
2003
12
04
2014
06
11
1471-2105
4
2003
Jan
13
BMC bioinformatics
BMC Bioinformatics
An automated method for finding molecular complexes in large protein interaction networks.
2
Recent advances in proteomics technologies such as two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of biomolecular interaction networks. Initial mapping efforts have already produced a wealth of data. As the size of the interaction set increases, databases and computational methods will be required to store, visualize and analyze the information in order to effectively aid in knowledge discovery.
This paper describes a novel graph theoretic clustering algorithm, "Molecular Complex Detection" (MCODE), that detects densely connected regions in large protein-protein interaction networks that may represent molecular complexes. The method is based on vertex weighting by local neighborhood density and outward traversal from a locally dense seed protein to isolate the dense regions according to given parameters. The algorithm has the advantage over other graph clustering methods of having a directed mode that allows fine-tuning of clusters of interest without considering the rest of the network and allows examination of cluster interconnectivity, which is relevant for protein networks. Protein interaction and complex information from the yeast Saccharomyces cerevisiae was used for evaluation.
Dense regions of protein interaction networks can be found, based solely on connectivity data, many of which correspond to known protein complexes. The algorithm is not affected by a known high rate of false positives in data from high-throughput interaction techniques. The program is available from ftp://ftp.mshri.on.ca/pub/BIND/Tools/MCODE.
Bader
Gary D
GD
Samuel Lunenfeld Research Institute, Mt, Sinai Hospital, Toronto ON Canada M5G 1X5, Dept, of Biochemistry, University of Toronto, Toronto ON Canada M5S 1A8. gary.bader@utoronto.ca
Hogue
Christopher W V
CW
eng
Evaluation Studies
Journal Article
Research Support, Non-U.S. Gov't
2003
01
13
England
BMC Bioinformatics
100965194
1471-2105
0
Macromolecular Substances
0
Saccharomyces cerevisiae Proteins
IM
Bioinformatics. 2000 May;16(5):412-24
10871264
Nat Genet. 2000 May;25(1):25-9
10802651
Nat Struct Biol. 2000 Oct;7(10):903-9
11017201
Nature. 2000 Oct 5;407(6804):651-4
11034217
Nat Biotechnol. 2000 Nov;18(11):1121-2
11062388
Nucleic Acids Res. 2001 Jan 1;29(1):75-9
11125054
Science. 2001 Feb 16;291(5507):1221-4
11233445
Proc Natl Acad Sci U S A. 2001 Apr 10;98(8):4569-74
11283351
Nature. 2001 May 3;411(6833):41-2
11333967
J Cell Biol. 2001 Aug 6;154(3):549-71
11489916
Proc Biol Sci. 2001 Sep 7;268(1478):1803-10
11522199
Science. 2001 Sep 14;293(5537):2087-92
11557892
Science. 2001 Nov 23;294(5547):1679-84
11721045
Nucleic Acids Res. 2002 Jan 1;30(1):303-5
11752321
Science. 2002 Jan 11;295(5553):321-4
11743162
Nature. 2002 Jan 10;415(6868):141-7
11805826
Nature. 2002 Jan 10;415(6868):180-3
11805837
Science. 2002 Apr 19;296(5567):548-50
11964484
Science. 2002 May 3;296(5569):910-3
11988575
Nature. 2002 May 23;417(6887):399-403
12000970
Nat Biotechnol. 2002 Oct;20(10):991-7
12355115
Nature. 1998 Jun 4;393(6684):440-2
9623998
Nucleic Acids Res. 1999 Jan 1;27(1):74-8
9847146
J Comput Biol. 1998 Winter;5(4):747-54
10072089
Annu Rev Biophys Biomol Struct. 1999;28:295-317
10410804
EMBO J. 1999 Aug 2;18(15):4321-31
10428970
Science. 1999 Aug 6;285(5429):901-6
10436161
Nucleic Acids Res. 2001 Jan 1;29(1):137-40
11125071
Nucleic Acids Res. 2001 Jan 1;29(1):242-5
11125103
Nucleic Acids Res. 2000 Jan 1;28(1):37-40
10592176
Nucleic Acids Res. 2000 Jan 1;28(1):56-9
10592180
Nucleic Acids Res. 2000 Jan 1;28(1):123-5
10592199
Nucleic Acids Res. 2000 Jan 1;28(1):316-9
10592259
Nature. 2000 Feb 10;403(6770):623-7
10688190
Yeast. 2000 Jun 30;17(2):95-110
10900456
Algorithms
Cluster Analysis
Computational Biology
methods
Computer Graphics
Macromolecular Substances
Predictive Value of Tests
Protein Interaction Mapping
methods
Proteomics
methods
Saccharomyces cerevisiae Proteins
chemistry
metabolism
Software Validation
PMC149346
2003
1
15
4
0
2003
12
5
5
0
2002
Sep
4
2003
Jan
13
2003
Jan
13
2003
1
15
4
0
ppublish
12525261
PMC149346
12519993
2003
01
09
2003
03
14
2014
06
11
1362-4962
31
1
2003
Jan
1
Nucleic acids research
Nucleic Acids Res.
BIND: the Biomolecular Interaction Network Database.
248-50
The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD.
Bader
Gary D
GD
Department of Biochemistry, Samuel Lunenfeld Research Institute, University of Toronto, Toronto M5G 1X5, Canada.
Betel
Doron
D
Hogue
Christopher W V
CW
eng
Journal Article
Research Support, Non-U.S. Gov't
England
Nucleic Acids Res
0411011
0305-1048
0
Macromolecular Substances
0
Proteins
IM
Science. 2002 Jan 11;295(5553):321-4
11743162
Nucleic Acids Res. 2002 Jan 1;30(1):281-3
11752315
Nature. 2002 Jan 10;415(6868):180-3
11805837
FEBS Lett. 2002 Feb 20;513(1):135-40
11911893
Biopolymers. 2001-2002;61(2):111-20
11987160
Genome Res. 2002 Oct;12(10):1619-23
12368255
BMC Bioinformatics. 2002 Oct 25;3:32
12401134
Nature. 1995 Feb 16;373(6515):573-80
7531822
Methods Enzymol. 1996;266:141-62
8743683
Nucleic Acids Res. 2002 Jan 1;30(1):303-5
11752321
Bioinformatics. 2000 May;16(5):465-77
10871269
Nucleic Acids Res. 2001 Jan 1;29(1):242-5
11125103
Science. 2001 Feb 16;291(5507):1221-4
11233445
Science. 2001 Dec 14;294(5550):2364-8
11743205
Nucleic Acids Res. 2002 Jan 1;30(1):245-8
11752306
Nucleic Acids Res. 2002 Jan 1;30(1):249-52
11752307
Nature. 2002 Jan 10;415(6868):141-7
11805826
Amino Acid Sequence
Animals
Computer Graphics
Databases, Protein
Macromolecular Substances
Protein Interaction Mapping
Protein Structure, Tertiary
Proteins
chemistry
metabolism
physiology
Sequence Alignment
methods
PMC165503
2003
1
10
4
0
2003
3
15
4
0
2003
1
10
4
0
ppublish
12519993
PMC165503
12401134
2003
10
29
2003
11
18
2014
06
11
1471-2105
3
2002
Oct
25
BMC bioinformatics
BMC Bioinformatics
SeqHound: biological sequence and structure database as a platform for bioinformatics research.
32
SeqHound has been developed as an integrated biological sequence, taxonomy, annotation and 3-D structure database system. It provides a high-performance server platform for bioinformatics research in a locally-hosted environment.
SeqHound is based on the National Center for Biotechnology Information data model and programming tools. It offers daily updated contents of all Entrez sequence databases in addition to 3-D structural data and information about sequence redundancies, sequence neighbours, taxonomy, complete genomes, functional annotation including Gene Ontology terms and literature links to PubMed. SeqHound is accessible via a web server through a Perl, C or C++ remote API or an optimized local API. It provides functionality necessary to retrieve specialized subsets of sequences, structures and structural domains. Sequences may be retrieved in FASTA, GenBank, ASN.1 and XML formats. Structures are available in ASN.1, XML and PDB formats. Emphasis has been placed on complete genomes, taxonomy, domain and functional annotation as well as 3-D structural functionality in the API, while fielded text indexing functionality remains under development. SeqHound also offers a streamlined WWW interface for simple web-user queries.
The system has proven useful in several published bioinformatics projects such as the BIND database and offers a cost-effective infrastructure for research. SeqHound will continue to develop and be provided as a service of the Blueprint Initiative at the Samuel Lunenfeld Research Institute. The source code and examples are available under the terms of the GNU public license at the Sourceforge site http://sourceforge.net/projects/slritools/ in the SLRI Toolkit.
Michalickova
Katerina
K
Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada M5S 1A8. katerina@mshri.on.ca
Bader
Gary D
GD
Dumontier
Michel
M
Lieu
Hao
H
Betel
Doron
D
Isserlin
Ruth
R
Hogue
Christopher W V
CW
eng
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.
2002
10
25
England
BMC Bioinformatics
100965194
1471-2105
IM
Nucleic Acids Res. 2000 Jan 1;28(1):45-8
10592178
Trends Biotechnol. 1999 Sep;17(9):351-5
10461180
Bioinformatics. 2000 May;16(5):465-77
10871269
Nucleic Acids Res. 2001 Jan 1;29(1):137-40
11125071
Nucleic Acids Res. 2001 Jan 1;29(1):242-5
11125103
Genome Res. 2001 Aug;11(8):1425-33
11483584
Nucleic Acids Res. 2002 Jan 1;30(1):17-20
11752243
Nucleic Acids Res. 2002 Jan 1;30(1):21-6
11752244
Nucleic Acids Res. 2002 Jan 1;30(1):35-7
11752247
Nucleic Acids Res. 2002 Jan 1;30(1):242-4
11752305
Nucleic Acids Res. 2002 Jan 1;30(1):249-52
11752307
Nucleic Acids Res. 2002 Jan 1;30(1):276-80
11752314
Nucleic Acids Res. 2002 Jan 1;30(1):281-3
11752315
BMC Bioinformatics. 2002 May 8;3:13
12019022
BMC Bioinformatics. 2002 Jul 31;3:20
12150718
Gene. 1988 Dec 15;73(1):237-44
3243435
J Mol Biol. 1990 Oct 5;215(3):403-10
2231712
Nat Genet. 1993 Aug;4(4):332-3
8401577
Nucleic Acids Res. 1994 Nov 11;22(22):4673-80
7984417
Methods Enzymol. 1996;266:141-62
8743683
Methods Biochem Anal. 1998;39:121-44
9707929
Nucleic Acids Res. 2000 Jan 1;28(1):235-42
10592235
Amino Acid Sequence
Base Sequence
Computational Biology
methods
Databases, Genetic
classification
Information Storage and Retrieval
methods
Internet
Models, Genetic
Models, Molecular
Molecular Sequence Data
Software
Structure-Activity Relationship
PMC138791
2002
10
29
4
0
2003
12
3
5
0
2002
Aug
1
2002
Oct
25
2002
Oct
25
2002
10
29
4
0
ppublish
12401134
PMC138791
12355115
2002
09
30
2003
03
12
2006
11
15
1087-0156
20
10
2002
Oct
Nature biotechnology
Nat. Biotechnol.
Analyzing yeast protein-protein interaction data obtained from different sources.
991-7
High-throughput methods for detecting protein interactions, such as mass spectrometry and yeast two-hybrid assays, continue to produce vast amounts of data that may be exploited to infer protein function and regulation. As this article went to press, the pool of all published interaction information on Saccharomyces cerevisiae was 15,143 interactions among 4,825 proteins, and power-law scaling supports an estimate of 20,000 specific protein interactions. To investigate the biases, overlaps, and complementarities among these data, we have carried out an analysis of two high-throughput mass spectrometry (HMS)-based protein interaction data sets from budding yeast, comparing them to each other and to other interaction data sets. Our analysis reveals 198 interactions among 222 proteins common to both data sets, many of which reflect large multiprotein complexes. It also indicates that a "spoke" model that directly pairs bait proteins with associated proteins is roughly threefold more accurate than a "matrix" model that connects all proteins. In addition, we identify a large, previously unsuspected nucleolar complex of 148 proteins, including 39 proteins of unknown function. Our results indicate that existing large-scale protein interaction data sets are nonsaturating and that integrating many different experimental data sets yields a clearer biological view than any single method alone.
Bader
Gary D
GD
Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada M5G 1X5.
Hogue
Christopher W V
CW
eng
Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
United States
Nat Biotechnol
9604648
1087-0156
0
Macromolecular Substances
0
Multiprotein Complexes
0
Proteome
0
Saccharomyces cerevisiae Proteins
IM
Chromatography, Liquid
methods
Database Management Systems
Databases, Protein
Genome, Fungal
Macromolecular Substances
Mass Spectrometry
methods
Multiprotein Complexes
Protein Interaction Mapping
methods
Proteome
Reproducibility of Results
Saccharomyces cerevisiae
metabolism
Saccharomyces cerevisiae Proteins
chemistry
metabolism
Sensitivity and Specificity
Sequence Alignment
methods
Sequence Analysis, Protein
Species Specificity
2002
10
2
4
0
2003
3
13
4
0
2002
Feb
20
2002
Aug
18
2002
10
2
4
0
ppublish
12355115
10.1038/nbt1002-991
nbt1002-991
11805837
2002
01
23
2002
02
14
2009
11
19
0028-0836
415
6868
2002
Jan
10
Nature
Nature
Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.
180-3
The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
Ho
Yuen
Y
MDS Proteomics, 251 Attwell Drive, Toronto, Canada M9W 7H4, and Staermosegaardsvej 6, DK-5230 Odense M, Denmark.
Gruhler
Albrecht
A
Heilbut
Adrian
A
Bader
Gary D
GD
Moore
Lynda
L
Adams
Sally-Lin
SL
Millar
Anna
A
Taylor
Paul
P
Bennett
Keiryn
K
Boutilier
Kelly
K
Yang
Lingyun
L
Wolting
Cheryl
C
Donaldson
Ian
I
Schandorff
Søren
S
Shewnarane
Juanita
J
Vo
Mai
M
Taggart
Joanne
J
Goudreault
Marilyn
M
Muskat
Brenda
B
Alfarano
Cris
C
Dewar
Danielle
D
Lin
Zhen
Z
Michalickova
Katerina
K
Willems
Andrew R
AR
Sassi
Holly
H
Nielsen
Peter A
PA
Rasmussen
Karina J
KJ
Andersen
Jens R
JR
Johansen
Lene E
LE
Hansen
Lykke H
LH
Jespersen
Hans
H
Podtelejnikov
Alexandre
A
Nielsen
Eva
E
Crawford
Janne
J
Poulsen
Vibeke
V
Sørensen
Birgitte D
BD
Matthiesen
Jesper
J
Hendrickson
Ronald C
RC
Gleeson
Frank
F
Pawson
Tony
T
Moran
Michael F
MF
Durocher
Daniel
D
Mann
Matthias
M
Hogue
Christopher W V
CW
Figeys
Daniel
D
Tyers
Mike
M
eng
Journal Article
Research Support, Non-U.S. Gov't
England
Nature
0410462
0028-0836
0
Cell Cycle Proteins
0
DNA, Fungal
0
Macromolecular Substances
0
Proteome
0
Saccharomyces cerevisiae Proteins
EC 2.7.-
Protein Kinases
EC 2.7.1.-
DUN1 protein, S cerevisiae
EC 2.7.11.1
Protein-Serine-Threonine Kinases
EC 3.1.3.-
Phosphoric Monoester Hydrolases
IM
Nature. 2002 Jan 10;415(6868):123-4
11805813
Amino Acid Sequence
Cell Cycle Proteins
Cloning, Molecular
DNA Damage
DNA Repair
DNA, Fungal
Humans
Macromolecular Substances
Mass Spectrometry
Molecular Sequence Data
Phosphoric Monoester Hydrolases
metabolism
Protein Binding
Protein Kinases
chemistry
metabolism
Protein-Serine-Threonine Kinases
Proteome
Saccharomyces cerevisiae
chemistry
Saccharomyces cerevisiae Proteins
chemistry
isolation & purification
Sequence Alignment
Signal Transduction
2002
1
24
10
0
2002
2
15
10
1
2002
1
24
10
0
ppublish
11805837
10.1038/415180a
415180a
11743205
2001
12
14
2002
01
14
2013
11
21
0036-8075
294
5550
2001
Dec
14
Science (New York, N.Y.)
Science
Systematic genetic analysis with ordered arrays of yeast deletion mutants.
2364-8
In Saccharomyces cerevisiae, more than 80% of the approximately 6200 predicted genes are nonessential, implying that the genome is buffered from the phenotypic consequences of genetic perturbation. To evaluate function, we developed a method for systematic construction of double mutants, termed synthetic genetic array (SGA) analysis, in which a query mutation is crossed to an array of approximately 4700 deletion mutants. Inviable double-mutant meiotic progeny identify functional relationships between genes. SGA analysis of genes with roles in cytoskeletal organization (BNI1, ARP2, ARC40, BIM1), DNA synthesis and repair (SGS1, RAD27), or uncharacterized functions (BBC1, NBP2) generated a network of 291 interactions among 204 genes. Systematic application of this approach should produce a global map of gene function.
Tong
A H
AH
Banting and Best Department of Medical Research, University of Toronto, Toronto ON, Canada M5G 1L6.
Evangelista
M
M
Parsons
A B
AB
Xu
H
H
Bader
G D
GD
Pagé
N
N
Robinson
M
M
Raghibizadeh
S
S
Hogue
C W
CW
Bussey
H
H
Andrews
B
B
Tyers
M
M
Boone
C
C
eng
Journal Article
Research Support, Non-U.S. Gov't
United States
Science
0404511
0036-8075
0
BIM1 protein, S cerevisiae
0
BNR1 protein, S cerevisiae
0
Bni1 protein, S cerevisiae
0
Carrier Proteins
0
Cell Cycle Proteins
0
Cytoskeletal Proteins
0
DNA, Fungal
0
Fungal Proteins
0
Genetic Markers
0
Microfilament Proteins
0
Microtubule Proteins
0
Saccharomyces cerevisiae Proteins
EC 3.1.-
Endodeoxyribonucleases
EC 3.1.-
Flap Endonucleases
EC 3.1.11.5
RAD27 protein, S cerevisiae
EC 3.6.1.-
SGS1 protein, S cerevisiae
EC 3.6.4.-
DNA Helicases
EC 3.6.4.12
RecQ Helicases
IM
Carrier Proteins
genetics
physiology
Cell Cycle Proteins
genetics
physiology
Cell Polarity
Computational Biology
Crosses, Genetic
Cytoskeletal Proteins
Cytoskeleton
physiology
DNA Helicases
genetics
physiology
DNA Repair
DNA, Fungal
biosynthesis
Databases, Genetic
Endodeoxyribonucleases
genetics
physiology
Flap Endonucleases
Fungal Proteins
genetics
physiology
Gene Deletion
Genes, Essential
Genes, Fungal
physiology
Genetic Markers
Genetic Techniques
Genome, Fungal
Microfilament Proteins
Microtubule Proteins
genetics
physiology
Mitosis
RecQ Helicases
Recombination, Genetic
Robotics
Saccharomyces cerevisiae
genetics
growth & development
physiology
Saccharomyces cerevisiae Proteins
genetics
physiology
2001
12
18
10
0
2002
1
15
10
1
2001
12
18
10
0
ppublish
11743205
10.1126/science.1065810
294/5550/2364
11743162
2002
01
11
2002
02
04
2007
11
14
1095-9203
295
5553
2002
Jan
11
Science (New York, N.Y.)
Science
A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules.
321-4
Peptide recognition modules mediate many protein-protein interactions critical for the assembly of macromolecular complexes. Complete genome sequences have revealed thousands of these domains, requiring improved methods for identifying their physiologically relevant binding partners. We have developed a strategy combining computational prediction of interactions from phage-display ligand consensus sequences with large-scale two-hybrid physical interaction tests. Application to yeast SH3 domains generated a phage-display network containing 394 interactions among 206 proteins and a two-hybrid network containing 233 interactions among 145 proteins. Graph theoretic analysis identified 59 highly likely interactions common to both networks. Las17 (Bee1), a member of the Wiskott-Aldrich Syndrome protein (WASP) family of actin-assembly proteins, showed multiple SH3 interactions, many of which were confirmed in vivo by coimmunoprecipitation.
Tong
Amy Hin Yan
AH
Banting and Best Department of Medical Research and Department of Molecular and Medical Genetics, University of Toronto, Toronto, Ontario, Canada M5G 1L6.
Drees
Becky
B
Nardelli
Giuliano
G
Bader
Gary D
GD
Brannetti
Barbara
B
Castagnoli
Luisa
L
Evangelista
Marie
M
Ferracuti
Silvia
S
Nelson
Bryce
B
Paoluzi
Serena
S
Quondam
Michele
M
Zucconi
Adriana
A
Hogue
Christopher W V
CW
Fields
Stanley
S
Boone
Charles
C
Cesareni
Gianni
G
eng
P41 RR11823
RR
NCRR NIH HHS
United States
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.
2001
12
13
United States
Science
0404511
0036-8075
0
Cytoskeletal Proteins
0
Fungal Proteins
0
LAS17 protein, S cerevisiae
0
Ligands
0
Peptide Library
0
Peptides
0
Proteins
0
Proteome
0
Saccharomyces cerevisiae Proteins
0
Wiskott-Aldrich Syndrome Protein
IM
Science. 2002 Jan 11;295(5553):284-7
11786630
Algorithms
Amino Acid Motifs
Amino Acid Sequence
Binding Sites
Computational Biology
Consensus Sequence
Cytoskeletal Proteins
Databases, Genetic
Databases, Protein
Fungal Proteins
chemistry
metabolism
Ligands
Molecular Sequence Data
Peptide Library
Peptides
chemistry
metabolism
Protein Binding
Protein Structure, Tertiary
Proteins
chemistry
metabolism
Proteome
Saccharomyces cerevisiae
chemistry
genetics
Saccharomyces cerevisiae Proteins
chemistry
genetics
metabolism
Software
Two-Hybrid System Techniques
Wiskott-Aldrich Syndrome Protein
src Homology Domains
2001
12
18
10
0
2002
2
5
10
1
2001
Dec
13
2001
12
18
10
0
ppublish
11743162
10.1126/science.1064987
1064987
10871269
2000
09
29
2000
09
29
2004
11
17
1367-4803
16
5
2000
May
Bioinformatics (Oxford, England)
Bioinformatics
BIND--a data specification for storing and describing biomolecular interactions, molecular complexes and pathways.
465-77
Proteomics is gearing up towards high-throughput methods for identifying and characterizing all of the proteins, protein domains and protein interactions in a cell and will eventually create more recorded biological information than the Human Genome Project. Each protein expressed in a cell can interact with various other proteins and molecules in the course of its function. A standard data specification is required that can describe and store this information in all its detail and allow efficient cross-platform transfer of data. A complete specification must be the basis for any database or tool for managing and analysing this information.
We have defined a complete data specification in ASN.1 that can describe information about biomolecular interactions, complexes and pathways. Our group is using this data specification in our database, the Biomolecular Interaction Network Database (BIND). An interaction record is based on the interaction between two objects. An object can be a protein, DNA, RNA, ligand, molecular complex or an interaction. Interaction description encompasses cellular location, experimental conditions used to observe the interaction, conserved sequence, molecular location, chemical action, kinetics, thermodynamics, and chemical state. Molecular complexes are defined as collections of more than two interactions that form a complex, with extra descriptive information such as complex topology. Pathways are defined as collections of more than two interactions that form a pathway, with additional descriptive information such as cell cycle stage. A request for proposal of a human readable flat-file format that mirrors the BIND data specification is also tendered for interested parties.
The ASN.1 data specification for biomolecular interaction, molecular complex and pathway data is available at ftp://bioinfo.mshri.on.ca/pub/BIND/Spec/bind.asn. An interactive browser for this document is available through our homepage at http://bioinfo.mshri.on.ca/BIND/asn-browser/.
Bader
G D
GD
Department of Biochemistry, University of Toronto/Samuel Lunenfeld Research Institute, Toronto, M5G 1X5, Canada Samuel Lunenfeld Research Institute, Toronto, M5G 1X5, Canada.
Hogue
C W
CW
eng
Journal Article
ENGLAND
Bioinformatics
9808944
1367-4803
0
Macromolecular Substances
0
Proteins
63231-63-0
RNA
9007-49-2
DNA
IM
DNA
genetics
metabolism
Databases, Factual
Humans
Macromolecular Substances
Models, Statistical
Molecular Biology
Proteins
genetics
metabolism
RNA
genetics
metabolism
2000
6
28
11
0
2000
10
7
11
1
2000
6
28
11
0
ppublish
10871269
11125103
2001
01
04
2001
02
08
2014
06
15
1362-4962
29
1
2001
Jan
1
Nucleic acids research
Nucleic Acids Res.
BIND--The Biomolecular Interaction Network Database.
242-5
The Biomolecular Interaction Network Database (BIND; http://binddb. org) is a database designed to store full descriptions of interactions, molecular complexes and pathways. Development of the BIND 2.0 data model has led to the incorporation of virtually all components of molecular mechanisms including interactions between any two molecules composed of proteins, nucleic acids and small molecules. Chemical reactions, photochemical activation and conformational changes can also be described. Everything from small molecule biochemistry to signal transduction is abstracted in such a way that graph theory methods may be applied for data mining. The database can be used to study networks of interactions, to map pathways across taxonomic branches and to generate information for kinetic simulations. BIND anticipates the coming large influx of interaction information from high-throughput proteomics efforts including detailed information about post-translational modifications from mass spectrometry. Version 2.0 of the BIND data model is discussed as well as implementation, content and the open nature of the BIND project. The BIND data specification is available as ASN.1 and XML DTD.
Bader
G D
GD
Department of Biochemistry, University of Toronto, Canada, Samuel Lunenfeld Research Institute, 600 University Avenue, Toronto M5G 1X5, Canada.
Donaldson
I
I
Wolting
C
C
Ouellette
B F
BF
Pawson
T
T
Hogue
C W
CW
eng
Journal Article
Research Support, Non-U.S. Gov't
ENGLAND
Nucleic Acids Res
0411011
0305-1048
0
Proteins
9007-49-2
DNA
IM
Proc Natl Acad Sci U S A. 2000 Feb 1;97(3):1143-7
10655498
Nucleic Acids Res. 2000 Jan 1;28(1):289-91
10592249
Nature. 2000 Feb 10;403(6770):623-7
10688190
Nat Genet. 2000 May;25(1):25-9
10802651
Bioinformatics. 2000 Mar;16(3):269-85
10869020
Bioinformatics. 2000 May;16(5):465-77
10871269
Oncogene. 1994 Oct;9(10):2827-36
8084588
Nature. 1995 Feb 16;373(6515):573-80
7531822
Trends Biochem Sci. 1997 Aug;22(8):314-6
9270306
Bioinformatics. 1999 Apr;15(4):339-40
10320402
Pac Symp Biocomput. 1999;:228-39
10380200
Science. 1999 Jun 18;284(5422):1948-50
10400537
Science. 1999 Jul 30;285(5428):751-3
10427000
Nucleic Acids Res. 2000 Jan 1;28(1):10-4
10592169
Nature. 2000 Feb 10;403(6770):591-2
10688172
Binding, Competitive
DNA
chemistry
metabolism
Databases, Factual
Information Services
Internet
Kinetics
Models, Molecular
Protein Binding
Proteins
chemistry
metabolism
PMC29820
2000
1
11
19
15
2001
3
3
10
1
2000
1
11
19
15
ppublish
11125103
PMC29820