title: MSIseq: Software for Assessing Microsatellite Instability from Catalogs of Somatic Mutations.
abstract: Microsatellite instability (MSI) is a form of hypermutation that occurs in some tumors due to defects in cellular DNA mismatch repair. MSI is characterized by frequent somatic mutations (i.e., cancer-specific mutations) that change the length of simple repeats (e.g., AAAAA…., GATAGATAGATA...). Clinical MSI tests evaluate the lengths of a handful of simple repeat sites, while next-generation sequencing can assay many more sites and offers a much more complete view of their somatic mutation frequencies. Using somatic mutation data from the exomes of a 361-tumor training set, we developed classifiers to determine MSI status based on four machine-learning frameworks. All frameworks had high accuracy, and after choosing one we determined that it had >98% concordance with clinical tests in a separate 163-tumor test set. Furthermore, this classifier retained high concordance even when classifying tumors based on subsets of whole-exome data. We have released a CRAN R package, MSIseq, based on this classifier. MSIseq is faster and simpler to use than software that requires large files of aligned sequenced reads. MSIseq will be useful for genomic studies in which clinical MSI test results are unavailable and for detecting possible misclassifications by clinical tests.
authors: Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.|Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.
authors: Mi Ni
authors: MN
authors: Huang
authors: Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.|Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.
authors: John R
authors: JR
authors: McPherson
authors: Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.|Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.
authors: Ioana
authors: I
authors: Cutcutache
authors: Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.|Laboratory of Cancer Epigenome, Division of Medical Sciences, National Cancer Centre Singapore, Singapore, 169610, Singapore.
authors: Bin Tean
authors: BT
authors: Teh
authors: Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.|Cancer Science Institute of Singapore, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119074, Singapore.|Division of Cellular and Molecular Research, National Cancer Centre Singapore, Singapore, 169610, Singapore.|Genome Institute of Singapore, A*STAR, Singapore, 138672, Singapore.
authors: Patrick
authors: P
authors: Tan
authors: Centre for Computational Biology, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.|Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, Singapore, 169857, Singapore.
authors: Steven G
authors: SG
authors: Rozen
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D004252:DNA Mutational Analysis / Q000379:methods*; D057225:Data Mining / Q000379:methods; D003628:Database Management Systems; D030541:Databases, Genetic; D006801:Humans; D000069550:Machine Learning; D053842:Microsatellite Instability; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software