School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China; MOE Key Laboratory for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
MOE Key Laboratory for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China; School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Genomics Proteomics Bioinformatics. 2020 Feb;18(1):65-71. doi: 10.1016/j.gpb.2020.02.001. Epub 2020 Mar 12.
Microsatellite instability (MSI) is a key biomarker for cancer therapy and prognosis. Traditional experimental assays are laborious and time-consuming, and next-generation sequencing-based computational methods do not work on leukemia samples, paraffin-embedded samples, or patient-derived xenografts/organoids, due to the requirement of matched normal samples. Herein, we developed MSIsensor-pro, an open-source single sample MSI scoring method for research and clinical applications. MSIsensor-pro introduces a multinomial distribution model to quantify polymerase slippages for each tumor sample and a discriminative site selection method to enable MSI detection without matched normal samples. We demonstrate that MSIsensor-pro is an ultrafast, accurate, and robust MSI calling method. Using samples with various sequencing depths and tumor purities, MSIsensor-pro significantly outperformed the current leading methods in both accuracy and computational cost. MSIsensor-pro is available at https://github.com/xjtu-omics/msisensor-pro and free for non-commercial use, while a commercial license is provided upon request.
微卫星不稳定性 (MSI) 是癌症治疗和预后的关键生物标志物。传统的实验检测方法既繁琐又耗时，基于下一代测序的计算方法由于需要匹配的正常样本，因此不适用于白血病样本、石蜡包埋样本或患者来源的异种移植/类器官。在此，我们开发了 MSIsensor-pro，这是一种用于研究和临床应用的开源单样本 MSI 评分方法。MSIsensor-pro 引入了一种多项分布模型来量化每个肿瘤样本的聚合酶滑动，并采用一种有区别的位点选择方法，从而能够在没有匹配正常样本的情况下进行 MSI 检测。我们证明了 MSIsensor-pro 是一种超快速、准确和稳健的 MSI 调用方法。使用具有不同测序深度和肿瘤纯度的样本，MSIsensor-pro 在准确性和计算成本方面均显著优于当前领先的方法。MSIsensor-pro 可在 https://github.com/xjtu-omics/msisensor-pro 上获得，并且免费供非商业使用，同时也可根据要求提供商业许可证。
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