MSIsensor: microsatellite instability detection using paired tumor-normal sequence data

Beifang Niu(Washington University in St. Louis), Kai Ye(Washington University in St. Louis), Qunyuan Zhang(Washington University in St. Louis), Charles Lu(Washington University in St. Louis), Mingchao Xie(Washington University in St. Louis), Michael D. McLellan(Washington University in St. Louis), Michael C. Wendl(Washington University in St. Louis), Li Ding(Washington University in St. Louis)
Bioinformatics
December 25, 2013
Cited by 868Open Access
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Abstract

MOTIVATION: Microsatellite instability (MSI) is an important indicator of larger genome instability and has been linked to many genetic diseases, including Lynch syndrome. MSI status is also an independent prognostic factor for favorable survival in multiple cancer types, such as colorectal and endometrial. It also informs the choice of chemotherapeutic agents. However, the current PCR-electrophoresis-based detection procedure is laborious and time-consuming, often requiring visual inspection to categorize samples. We developed MSIsensor, a C++ program for automatically detecting somatic microsatellite changes. It computes length distributions of microsatellites per site in paired tumor and normal sequence data, subsequently using these to statistically compare observed distributions in both samples. Comprehensive testing indicates MSIsensor is an efficient and effective tool for deriving MSI status from standard tumor-normal paired sequence data. AVAILABILITY AND IMPLEMENTATION: https://github.com/ding-lab/msisensor


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