---------------------------- About ---------------------------- capDSD is an extension of DSD (available at dsd.cs.tufts.edu) that takes in to account a measure of interaction confidence while also incorporating directed pathway data. If you use capDSD, please cite: M. Cao, C. M. Pietras, X. Feng, K.J. Doroschak, T. Schaffner, J. Park, H. Zhang, L. J. Cowen and B. Hescott, New directions for diffusion-based network prediction of protein function: incorporating pathways with confidence. Bioinformatics, Volume 30, ISMB 2014 Proceedings, i219-i227, 2014. DSD is licensed under the GNU public license version 2.0. If you would like to license DSD in an environment where the GNU public license is unacceptable (such as inclusion in a non-GPL software package) commercial licensing is available through Tufts offices of Technology Transfer. Contact cowen@cs.tufts.edu for more information. Contact cpietras@cs.tufts.edu for issues involving the code. Address: 161 College Ave., Medford, MA 02155, USA ---------------------------- Installation ---------------------------- capDSD requires Python 2.7 and an installation of numpy. To run, simply place the .py files in the desired directory and invoke using the python interpreter. ---------------------------- Using DSD.py ---------------------------- Run python DSD.py -h for information on arguments. capDSD takes a PPI and optionally a directory of PPIPs (PPI pathways). The format for PPIs is tab delimited: Protein1 Protein2 confidence ProteinX ProteinY confidence ... Confidence is optional, but must be specified in a flag passed to DSD.py if it is to be used. If the PPI is treated as directed, the direction is from the first column to the second. The format for PPIPs is tab delimited: Protein1 Protein2 type1 type2 ProteinX ProteinY typeX typeY typeZ ... Types are optional, and are used with -m 3 (default) to determine whether or not to treat the edge as directed. If all of the types are present in a list of "directed types," the edge is treated as directed. To run cDSD, simply pass a properly formatted PPI as infile and specify the -c flag. To run caDSD, use the -c flag, specify the directory with the ppip files with -p, and use -mult 0 To run capDSD, use the -c flag, specify the directory with the ppip files with -p Parameters m, s, and r can be specified with -mult, -s, and -r respectively, but will default to the values of 25, 7, and .7 that are used in the paper. A small example is available at bcb.cs.tufts.edu/capdsd/files/testdata.zip ---------------------------- Using KeggParse.py ---------------------------- KeggParse.py is used to parse a directory of .kgml files from http://www.genome.jp/kegg/ and output them in .ppip format. Run with KeggParse.py /target/directory/path/ ---------------------------- Generating confidence scores ---------------------------- The source code for generating confidence scores for a given PPI is not available at the moment, but may be at a future date.