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Faculty, Staff and Student Publications
Publication Date
5-15-2024
Journal
Genome Research
Abstract
Accurate detection of somatic mutations in DNA sequencing data is a fundamental prerequisite for cancer research. Previous analytical challenges were overcome by consensus mutation calling from four to five popular callers. This, however, increases the already nontrivial computing time from individual callers. Here, we launch MuSE 2, powered by multistep parallelization and efficient memory allocation, to resolve the computing time bottleneck. MuSE 2 speeds up 50 times more than MuSE 1 and eight to 80 times more than other popular callers. Our benchmark study suggests combining MuSE 2 and the recently accelerated Strelka2 achieves high efficiency and accuracy in analyzing large cancer genomic data sets.
Keywords
Humans, Neoplasms, Mutation, Exome Sequencing, Whole Genome Sequencing, Software, Genome, Human, Genomics, Algorithms, DNA Mutational Analysis
DOI
10.1101/gr.278456.123
PMID
38589250
PMCID
PMC11146589
PubMedCentral® Posted Date
April 2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Genetic Processes Commons, Genetic Structures Commons, Medical Genetics Commons, Oncology Commons
Comments
Associated Data
PMID: 38589250