Please use this identifier to cite or link to this item: https://doi.org/10.1038/srep13321
Title: MSIseq: Software for assessing microsatellite instability from catalogs of somatic mutations
Authors: Ni Huang, M 
McPherson, J.R 
Cutcutache, I 
Teh, B.T 
Tan, P 
Rozen, S.G 
Keywords: algorithm
automated pattern recognition
data mining
database management system
dna mutational analysis
DNA sequence
genetic database
human
machine learning
microsatellite instability
molecular genetics
nucleotide sequence
procedures
reproducibility
sensitivity and specificity
software
Algorithms
Base Sequence
Data Mining
Database Management Systems
Databases, Genetic
DNA Mutational Analysis
Humans
Machine Learning
Microsatellite Instability
Molecular Sequence Data
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity
Sequence Analysis, DNA
Software
Issue Date: 2015
Citation: Ni Huang, M, McPherson, J.R, Cutcutache, I, Teh, B.T, Tan, P, Rozen, S.G (2015). MSIseq: Software for assessing microsatellite instability from catalogs of somatic mutations. Scientific Reports 5 : 13321. ScholarBank@NUS Repository. https://doi.org/10.1038/srep13321
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.
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/175987
ISSN: 2045-2322
DOI: 10.1038/srep13321
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