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. 2014 Apr 1;30(7):1015-6.
doi: 10.1093/bioinformatics/btt755. Epub 2013 Dec 25.

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

Beifang Niu  1 , Kai Ye, Qunyuan Zhang, Charles Lu, Mingchao Xie, Michael D McLellan, Michael C Wendl, Li Ding
Affiliations

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

Beifang Niu et al. Bioinformatics. .

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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Figures

Fig. 1.
Fig. 1.
MSIsensor differentiates MSI (microsatellite instable) samples from MSS (microsatellite stable) ones. The x-axis is clinical classification of MSI and MSS. The y-axis is the percentage of microsatellite sites with a somatic indel
See this image and copyright information in PMC

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