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

• Published in: Bioinformatics Vol. 30; no. 7; pp. 1015 - 1016
• Main Authors: Niu, Beifang, Ye, Kai, Zhang, Qunyuan, Lu, Charles, Xie, Mingchao, McLellan, Michael D., Wendl, Michael C., Ding, Li
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.04.2014
• Subjects: Applications Notes; Automation, Laboratory; Diseases; Genome, Human; Humans; Instability; Microsatellite Instability; Microsatellites; Neoplasms - genetics; Polymerase Chain Reaction; Samples; Sequence Analysis, DNA - methods; Software; Stability; Statistical analysis; Statistical methods
• ISSN: 1367-4803; 1367-4811; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btt755

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 Contact:  kye@genome.wustl.edu or lding@genome.wustl.edu Supplementary information:  Supplementary data are available at Bioinformatics online.