Driver mutation prioritization software tools | High-throughput sequencing data analysis
Driver mutation give a selective advantage to a clone in its microenvironment, through either increasing its survival or reproduction. Detecting these mutations after whole-genome sequencing or whole-exome sequencing is of major interest for understanding molecular mechanisms of carcinogenesis and for prognostic and diagnostic markers of cancer. Driver mutation prioritization software tools analyze genome and transcriptome data for identification of altered genes as potential cancer drivers.
Predicts functional consequences of non-synonymous single nucleotide polymorphisms (nsSNP). FATHMM is built on a sequence-based method that associates evolutionary conservation in homologous sequences with disease-specific weights. This software can characterize mutations related to a specific disease, or a group of related diseases (disease-specific), and other putative disease-causing (non-specific) mutations.
Serves for the functional analysis of gene expression and genomic data. Babelomics offers the possibility to explore the effects of alteration in gene expression levels or changes in genes sequences within a functional context. It provides user-friendly access to a full range of methods that cover: (1) primary data analysis; (2) a variety of tests for different experimental designs; and (3) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context.
Assigns molecular functional effects of non-synonymous SNPs based on structure and sequence analysis. There are three unique features of the SNPs3D resource. First, it is designed specifically for the analysis of the relationship between SNPs and disease. Second, it constructs gene networks based on conceptual relationships derived from the literature, rather than experimental data. Third, it integrates access to all available and relevant information sources, wherever possible giving the user easy access to the underlying data and literature, so that informed judgments can be made.
Analyzes lists of mutations discovered in DNA sequencing, to identify genes that were mutated more often than expected by chance given background mutation processes. MutSig was originally developed for analyzing somatic mutations, but it has also been useful in analyzing germline mutations. MutSig builds a model of the background mutation processes that were at work during formation of the tumors, and it analyzes the mutations of each gene to identify genes that were mutated more often than expected by chance, given the background model.
Deals with information involved in carcinogenesis. IntOGen supplies high-throughput data merged with transcriptomic alterations, genomic gains and losses and somatic mutation information. The platform allows users to: (i) discover altered genes and modules in a cancer type (ii) explore alteration pattern of a gene module and; (iii) browse results from one or a combination of experiments including user-customed combinations.
Performs pathology predictions, gives access to a repository of pre-calculated predictions and generates and validates new predictors. PMut is a web-based tool that offers a generally trained predictor performing with current available methods and allows user to access an automatic procedure to train new predictors with specific datasets or features. The software was trained using the manually curated variation database SwissVar.
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.