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| Coming soon! | #acl All:read #format noCamelCase2 = ActiveDriver = '''ActiveDriver''' is a statistical method for interpreting variations in protein sequence (e.g. coding SNPs in the population, SNVs in cancer genomes) in the context of protein post-translational signaling modifications (phosphorylation sites, kinase domains, active sites, etc.). The method is based on a logistic regression strategy and identifies signaling sites in proteins that involve unexpectedly many (or few) sequence variants considering the general variability of the protein, disordered and ordered regions, density of signaling-related residues (such as phosphosites), and proximity of variants/mutations to signaling residues. We provide an implementation of the method as an R package. Further details of the method, as well as a case study of cancer mutations in phosphorylation signaling is available in the following publication: '''Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers'''<<BR>>Jüri Reimand, Gary D. Bader<<BR>>[[http://www.nature.com/msb/journal/v9/n1/full/msb201268.html|Molecular Systems Biology (2013) doi:10.1038/msb.2012.68]] <<BR>>[[http://www.ncbi.nlm.nih.gov/pubmed/23340843|PubMed Abstract]] [[attachment:Reimand_MSB_phosphomutations.pdf|PDF]]<<BR>> [[http://genomemedicine.com/content/5/2/19/abstract|Research Highlight in Genome Medicine (2013)]] <<BR>> Please refer to the ActiveDriver website for source code, examples, and additional data: http://individual.utoronto.ca/reimand/ActiveDriver/ |
ActiveDriver
ActiveDriver is a statistical method for interpreting variations in protein sequence (e.g. coding SNPs in the population, SNVs in cancer genomes) in the context of protein post-translational signaling modifications (phosphorylation sites, kinase domains, active sites, etc.).
The method is based on a logistic regression strategy and identifies signaling sites in proteins that involve unexpectedly many (or few) sequence variants considering the general variability of the protein, disordered and ordered regions, density of signaling-related residues (such as phosphosites), and proximity of variants/mutations to signaling residues.
We provide an implementation of the method as an R package. Further details of the method, as well as a case study of cancer mutations in phosphorylation signaling is available in the following publication:
Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers
Jüri Reimand, Gary D. Bader
Molecular Systems Biology (2013) doi:10.1038/msb.2012.68
PubMed Abstract PDF
Research Highlight in Genome Medicine (2013)
Please refer to the ActiveDriver website for source code, examples, and additional data: http://individual.utoronto.ca/reimand/ActiveDriver/