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.
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.
An R package that identifies personalized driver mutations for any given patient sample. Applications to TCGA datasets demonstrated the effectiveness of our method. We believe DawnRank complements existing driver identification methods and will help us discover personalized causal mutations that would otherwise be obscured by tumor heterogeneity.
A method for stratification (clustering) of patients in a cancer cohort based on genome scale somatic mutations measurements and a gene interaction network. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology.
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.
Captures biologically and clinically relevant information. iPAS was applied to samples of lung and colon adenocarcinoma. It can recognize altered pathways in an individual by employing the nRef. This tool is able to find pathway aberrances that are associated with a patient’s clinical outcome. It provides an approach that uses the inter-gene correlation structure of the accumulated normal samples.