CASCADE_SCAN: mining signal transduction network from high-throughput data based on steepest descent method
Kai Wang1, Fuyan Hu2, Kejia Xu2, Hua Cheng1, Meng Jiang1, Ruili Feng1, Jing Li1, Tieqiao Wen1¡ì. BMC Bioinformatics 2011, 12(1):164.
1 Laboratory of Molecular Neurobiology, School of Life Sciences and Institute of Systems Biology, Shanghai University, Shanghai 200444, China.
2 Department of Mathematics, College of Sciences, Shanghai University, Shanghai 200444, China.
[PubMed: 21575263]
Signal transduction
Signal transduction plays an essential role in response to environment changes. This biological process is usually characterized by phosphorylation/dephosphorylation of some key proteins (e.g. kinases) and generally involves in a signal cascade. The signal transduction process often starts from a membrane protein (usually a membrane surface receptor), comes by a series of intercellular signaling proteins and then transfers to transcription factors in nucleolus, and subsequently raises the expression of downstream genes.
Overview
CASCADE_SCAN is a new approach for mining signal transduction network from high-throughput data based on a steepest descent method, which can effectively utilize more additional proteins, and not restrict to a membrane receptor or a transcription factor. Thus, this method is more suitable for detecting the signal transduction networks where the membrane receptors or transcription factors are not known. Besides, our model can automatically search additional high related proteins, which may be involved in the same signal transduction network.
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Download
The current release of CASCADE_SCAN is 1.0, it is easy to implement and the executable binary for linux systerm (x86_64) as well as he source code are freely available from http://sourceforge.net/projects/cascadescan/files/cascade_scan.tar.gz/download, to compile it, all you need to do is to type "gcc cascade_scan.c -o cascade_scan -lm -Wall -L. -lhash".
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Usage
Usage: cascade_scan options: [-i data],[-c 0.800],[-j 0.980],[-e list],[-d 10],[-s 50],[-a 1],[-o result]
-i protein-protein interaction data [or integrated microarray data] [needed ]
-c protein-protein interaction score threshold [0.800] [optional]
-j high credible PPI score threshold [0.980] [optional]
-d path length for Depth First Search (DFS) algorithm [10] [optional]
-e a list file containing expected seed genes [needed ]
-s maximal network size (number of genes) [50 ] [optional]
-a number of networks will be extracted [<=5] [optional]
-o result file [optional]
-h show help information
protein-protein interaction data file format:
protein_1 protein_2 score[0.000-0.999]
protein_1 protein_3 score[0.000-0.999]
protein_3 protein_4 score[0.000-0.999]
seed protein list file format: (no spaces and one protein name per line)
seed_protein_name_1
seed_protein_name_2
seed_protein_name_3
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Comments and suggestions to: charles.k.w@msn.com
Nervous Molecular Systems Biology Laboratory, Shanghai University
Last updated: 05/19/2011
