MutSig specifications

Unique identifier:
OMICS_00155
Alternative name:
MutSigCV
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
Name:
Mutation Significance
Software type:
Package/Module
Restrictions to use:
None
Input format:
MAF, TSV
Output format:
TSV
Programming languages:
MATLAB
Version:
1.3.01
Maintained:
Yes

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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.

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