MuSE statistics

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Associated diseases

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MuSE specifications


Unique identifier OMICS_12678
Name MuSE
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data MuSE is comprised of two steps, which requires (1) the indexed reference genome FASTA file, (2) the binary sequence alignment/map formatted (BAM) sequence data from the pair of tumor and normal DNA samples, and (3) the dbSNP variant call format (VCF) file that should be bgzip compressed, tabix indexed and based on the same reference genome as (1).
Output data The final output of MuSE is a VCF file that lists the identified somatic variants.
Operating system Unix/Linux, Mac OS, Windows
Programming languages C, C++
License GNU General Public License version 2.0
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes



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  • person_outline Wenyi Wang <>

Publication for MuSE

MuSE in publications

PMCID: 5852328
PMID: 29552334
DOI: 10.1016/j.csbj.2018.01.003

[…] assumption and model joint allele frequencies (ft,fn) instead of joint genotypes (gt,gn). the allele frequency analysis approach is taken by strelka, mutect, lofreq, ebcall, deepsnv, lolopicker, and muse , , , , , , . strelka's core algorithm consists of two steps. first, the posterior probabilities of vafs in tumor and normal, noted as p(ft,fn|dt,dn), are estimated based proportions […]

PMCID: 5797543
PMID: 29441070
DOI: 10.3389/fimmu.2018.00099

[…] phlat (). the haplotype for a sample is decided based on a consensus decision of the three input haplotypes. somatic point mutations were called using a panel of five mutation callers, mutect (), muse (), radia (), somaticsniper (), and strelka (). since most mutation callers are dna centric, we allow mutations rejected by up to two of the callers through this first filter. the vcf […]

PMCID: 5688099
PMID: 29142225
DOI: 10.1038/s41467-017-01730-x

[…] accuracy. major steps included marking duplicates, local realignment around high-confidence insertion and deletions and base quality recalibration. we then used several popular callers, including muse, mutect2, somaticsniper, radia, and varscan2, to identify somatic point mutations. only mutations reported by at least two callers were used in further analyses. low coverage and strand-biased […]

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MuSE institution(s)
Department of Bioinformatics and Computational Biology - Unit 1410, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center,Houston, TX, USA
MuSE funding source(s)
The Keck Center of the Gulf Coast Consortia for the Computational Cancer Biology Training Program; the Cancer Prevention and Research Institute of Texas (CPRIT) RP140113, PI - Rathindra Bose; the National Institutes of Health/National Cancer Institute through grant U24 CA143883 02S2 and the Integrative Pipeline for Analysis & Translational Application of TCGA Data, grant 5U24CA143883-04 , the Cancer Prevention Research Institute of Texas through grant number RP130090, NCI through grant numbers 1R01CA174206-01 and P30 CA016672, in part by the US National Cancer Institute (NCI; MD Anderson TCGA Genome Data Analysis Center) through grant numbers CA143883, CA083639 and CA183793, by the Cancer Prevention and Research Institute of Texas through grant number R1205 01, the UT Systems Stars Award (PS100149), the Welch Foundation Robert A. Welch Distinguished University Chair Award (G-0040), the MD Anderson Physician Scientist Award, and the C.G. Johnson Advanced Scholar Award.

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