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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.
MUFFINN / MUtations For Functional Impact on Network Neighbors
A cancer gene prioritization method based on a pathway-centric analysis of mutation data. MUFFINN integrates mutational information for individual genes and their neighbors in co-functional networks. MUFFINN is highly predictive for known cancer genes, particularly for genes with low mutation occurrence among cancer patients, with the identification of drivers amongst these genes having substantially higher sensitivity than conventional methods based on gene-centric analysis of mutation data. MUFFINN works effectively with both pan-cancer and individual cancer type samples.
Identifies driver genes. This method displays the following characteristics: (1) it assigns scores to non-silent mutations according to their expected impacts on the protein function, (2) it permits each sample to have a different background mutation rate, (3) it accounts for the variable number of possible non-silent mutations that can occur at each base pair according to the genetic code, and (4) it takes into account uncertainties in the background mutation rate. The software was evaluated using lung tumor genome sequences.
WeSME / Weighted Sampling based Mutual Exclusivity
Estimates p-values of mutual exclusivity while taking into account mutation frequencies of patients. WeSME closely approximates the results of the permutation-based method and does so without using the costly permutations of a mutation matrix. Moreover, WeSME can compute p-values for a subset of genes independently from the rest of genome unlike the permutation method that requires whole genome permutations. By dynamically adjusting sampling depth, WeSME can provide high precision p-values without a significant increase in computational cost.
/ Cancer Correlation Clustering
Leverages mutual exclusivity of mutations, patient coverage and driver network concentration principles. To test C³, we performed a detailed analysis on TCGA breast cancer and glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying biologically relevant driver genes. The proposed agnostic clustering method represents a unique tool for efficient and reliable identification of mutation patterns and driver pathways in large-scale cancer genomics studies, and it may also be used for other clustering problems on biological graphs.
DIGGIT / Driver-gene Inference by Genetical-Genomic Information Theory
A package for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. DIGGIT searches for genetic alterations associated with dysregulation of MR protein activity, reducing the number of hypothesis to test, while providing regulatory clues to help elucidate associated biological mechanisms. It evaluates candidate alterations within a set of functional disease drivers and their upstream regulators .This is accomplished by a five-step process, requiring gene expression, matched genetic-variant profiles, specifically copy number variation data (CNV), and context-specific transcriptional and postranslational regulatory models. DIGGIT boosts the statistical power by focusing on the regulators mechanistically linked to the phenotype and on their upstream post-translational modulators.
A mutation network method to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.
Identifies multiple mutated modules displaying specific mutation patterns between and within modules. BeWith is a general framework that reveals complex relations between mutual exclusivity, functional interactions, and co-occurrence. This application can be used to uncover relationships between genes, gene groups, and pathways that were not accessible by previous methods. Its formulation is very general and is appropriate to interrogate other aspects of the mutational landscape by exploring different combinations of within-between definitions and constraints with simple modifications.
CoMDP / Co-occurring Mutated Driver Pathways
A method for the de novo identification of co-occurring driver pathways in cancer without any prior information beyond mutation profiles. Two possible properties of mutations that occurred in cooperative pathways were exploited to achieve this: (1) each individual pathway has high coverage and high exclusivity; and (2) the mutations between the pair of pathways showed statistically significant co-occurrence. The efficiency of CoMDP was validated first by testing on simulated data and comparing it with a previous method. Then CoMDP was applied to several real biological data including glioblastoma, lung adenocarcinoma, and ovarian carcinoma datasets. The discovered co-occurring driver pathways were here found to be involved in several key biological processes, such as cell survival and protein synthesis. Moreover, CoMDP was modified to (1) identify an extra pathway co-occurring with a known pathway and (2) detect multiple significant co-occurring driver pathways for carcinogenesis. CoMDP can be used to identify gene sets with more biological relevance than the ones currently used for the discovery of single driver pathways.
MCSS / Minimum Cost Subset Selection
Finds multiple mutated driver pathways without the need to pre-specify a number of pathways and/or a number of genes. MCSS is an algorithm developed for de novo discovery of driver mutation pathways in cancer studies. It is based on a non-convex approximation to the original combinatorial optimization problem. MCSS is flexible for integrative analysis of multiple types of genomic data. Currently, in addition to the standard analysis of a single mutation dataset, it can integrate a mutation dataset with a gene expression data (which may or may not be drawn on the same set of the subjects for the mutation data).
SLAPenrich / Sample Level Analysis of Pathway Alteration Enrichments
A statistical method to identify pathway-level enrichments of genetic alterations. SLAPenrich does not require the genes belonging to a given pathway to be statistically enriched among those altered in the individual samples. It assumes that the functionality of a given pathway might be altered in an individual sample if at least one of its genes is genomically altered. The method accounts for the differences in the mutation rates between samples and the exonic lengths of the genes in the pathways. SLAPenrich performs in differential enrichment analysis of pathway alterations across different clinically relevant sub-populations of samples. SLAPenrich also includes function to visualise the identified enriched pathway implementing a heuristic sorting to highlight mutual exclusivity trends among the pattern of alterations of the composing genes.
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