Driver pathway identification software tools | Protein interaction data analysis
It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. Therefore, discovering driver pathways is becoming an important step to understand the molecular mechanisms underlying cancer and design efficient treatments for cancer patients. Previous studies have focused mainly on observation of the alterations in cancer genomes at the individual gene or single pathway level. However, a great deal of evidence has indicated that multiple pathways often function cooperatively in carcinogenesis and other key biological processes.
A pathway analysis tool using population data. The PathScan tool performs a more sophisticated statistical analysis that considers these variables using Fisher-Lancaster theory, thus furnishing more accurate P-values. It is readily used for any biologically-relevant grouping of genes, i.e. not only established pathways, but also putative gene networks from de novo methods. PathScan is available through MuSiC, or can be invoked as a stand-alone tool.
A powerful and flexible statistical framework for identifying driver genes and driver signaling pathways in cancer genome-sequencing studies. DrGaP is immediately applicable to cancer genome-sequencing studies and will lead to a more complete identification of altered driver genes and driver signaling pathways in cancer.
An algorithm for discovery of mutated driver pathways in cancer using only mutation data. Dendrix finds sets of genes, domains, or nucleotides whose mutations exhibit both high coverage and high exclusivity in the analyzed samples.
A method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern.
Predicts functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). DriverNet combines different kinds of data thanks to an influence graph, that is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which have mutated in a larger number of patients. These mutations will push the gene expression values of the connected genes to some extreme values.
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