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Mutation Significance MutSig

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.

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MutSig classification

MutSig specifications

Software type:
Package/Module
Restrictions to use:
None
Input format:
MAF, TSV
Output format:
TSV
Programming languages:
MATLAB
Version:
1.3.01
Maintained:
Yes
Interface:
Command line interface
Input data:
A list of mutations (and indels) from a set of samples (patients) that were subjected to DNA sequencing, a coverage file that contains information about the sequencing coverage achieved for each gene and patient/tumor, a covariate file that contains the genomic covariate data for each gene.
Output data:
A tab-delimited report of significant mutations, listed in descending order from most significant to least significant.
Operating system:
Unix/Linux
Computer skills:
Advanced
Stability:
Stable

Subtools

  • MutSigCV

MutSig distribution

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Credits

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Publications

Institution(s)

The Broad Institute of MIT and Harvard, Cambridge, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Instituto Nacional de Medicina Genómica, Mexico City, Mexico; Yale Cancer Center, Department of Hematology, New Haven, CT, USA; Massachusetts General Hospital, Boston, MA, USA; Brigham and Women’s Hospital, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA; Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel; Boston Children’s Hospital, Boston, MA, USA; Children’s Hospital, Philadelphia, PA, USA; Laboratory of Molecular Genetics, National Institute of Environmental Health Sciences, NIH, DHHS, Durham, NC, USA; Department of Pediatric Oncology, Hospital Sant Joan de Déu, Barcelona, Spain; Genome Sciences, University of Washington, Seattle, WA, USA

Funding source(s)

This work was supported as part of The Cancer Genome Atlas (TCGA), a project of the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI) and as part of the Slim Initiative for Genomic Medicine (SIGMA), a joint U.S.-Mexico project founded by the Carlos Slim Health Institute, and the Intramural Research Program of the NIEHS (NIH, DHHS) project ES065073.

Link to literature

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