This is the implementation of Topological Clustering Semantic Similarity (TCSS)
algorithm. For any queries please conact Gary D. Bader <gary.bader@utoronto.ca>
or Shobhit Jain <shobhit@cs.toronto.edu>.

Requirements:
		1) Python 2.6

Files provided:

		1) gene_ontology.obo.txt : Gene Ontology obo file

		2) gene_association.sgd : SGD annotation file
		   
		3) *.py : Python files of TCSS implementation
	



Usage of TCSS implementation in Unix/Linux systems:

Use the following comand to run TCSS.

 tcss.py [-options] geneA geneB    
 or 
 tcss.py [-options] -i input_file

 Example: python tcss.py -i datasets/sgd_data/iea-/positives.sgd.c -o out.txt -c C:2.4 --drop="IEA" --gene=datasets/sgd_data/gene_association.sgd


 -options     
    -i [file name] or --input [=file name]       Input file (two genes separted by comma per line)    
    -o [file name] or --output [=file name]      Output file    
    -c [domain:cutoff] or                        Domain [C/P/F], cutoff [int/float] in any combination    
         --topology-cutoff [=domain:cutoff]      (default: C:2.4,P:3.5,F:3.3)    
    --detail                                     Detailed output (default: False)    
    --gene [=file name]                          Gene annotation file (default: SGD file provided)    
    --go [=file name]                            Gene Ontology (GO) obo file (default: GO file provided)    
    --drop [=evidence code]                      GO evidence code not to be used
    -h or --help                                 Usage

Note: The program currently works with only SGD gene ids or UniProtKB human ids.
