MIAmS: microsatellite instability detection on NGS amplicons data

Abstract Summary Microsatellite instability (MSI) is a molecular marker of DNA mismatch repair deficiency frequently at play in oncogenesis. MSI testing is used for diagnostic, prognostic and therapeutic purposes in several cancers. The current gold standard analysis for microsatellite status is bas...

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Bibliographic Details
Published in:Bioinformatics Vol. 36; no. 6; pp. 1915 - 1916
Main Authors: Escudié, Frédéric, Van Goethem, Charles, Grand, David, Vendrell, Julie, Vigier, Anna, Brousset, Pierre, Evrard, Solène M, Solassol, Jérôme, Selves, Janick
Format: Journal Article
Language:English
Published: England Oxford University Press 24-10-2019
Oxford University Press (OUP)
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Summary:Abstract Summary Microsatellite instability (MSI) is a molecular marker of DNA mismatch repair deficiency frequently at play in oncogenesis. MSI testing is used for diagnostic, prognostic and therapeutic purposes in several cancers. The current gold standard analysis for microsatellite status is based on length distribution analysis of multiplex-PCR generated DNA fragments from tumor samples which is a laborious and time consuming method. Next generation sequencing (NGS) using amplicon panels is an easy, accurate and scalable technique to determine both the microsatellite status and tumor genome mutations at the same time. Here, we describe MIAmS, an application designed to tag microsatellite status from amplicon NGS of tumor samples. Interestingly, this tool does not require paired normal tissue for comparison. In addition, this scalable application provides a user-friendly report for the interpretation of the results by biologists and exhibits a strong accuracy and robustness for determination of the MSI status. Availability and implementation https://github.com/bialimed/miams. Evaluation data are available at http://www.ebi.ac.uk/ena/data/view/PRJEB31725. Supplementary information Supplementary data are available at Bioinformatics online.
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ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btz797