MSIsensor-RNA: Microsatellite Instability Detection for Bulk and Single-cell Gene Expression Data

IF 11.5 2区 生物学 Q1 GENETICS & HEREDITY
Peng Jia, Xuanhao Yang, Xiaofei Yang, Tingjie Wang, Yu Xu, Kai Ye
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引用次数: 0
Abstract Microsatellite instability (MSI) is an indispensable biomarker in cancer immunotherapy. Currently, MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI detection. However, the MSI detection method on expression data, especially single-cell expression data, is still lacking, limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell level. Herein, we developed MSIsensor-RNA, an accurate, robust, adaptable, and standalone software to detect MSI status based on expression values of MSI-associated genes. We demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing (RNA-seq), microarray, and single-cell RNA-seq. MSIsensor-RNA is a versatile, efficient, and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and applications. MSIsensor-RNA is available at https://github.com/xjtu-omics/msisensor-rna.
MSIsensor-RNA:批量和单细胞基因表达数据的微卫星不稳定性检测
摘要 微卫星不稳定性(MSI)是癌症免疫疗法中不可或缺的生物标志物。目前,通过高通量组学方法进行MSI评分的方法已得到普及,并显示出优于MSI检测金标准方法的性能。然而,表达数据尤其是单细胞表达数据的MSI检测方法仍然缺乏,限制了临床应用的范围,也阻碍了单细胞水平的MSI研究。在此,我们开发了MSIsensor-RNA,这是一种基于MSI相关基因表达值检测MSI状态的准确、稳健、适应性强的独立软件。我们展示了MSIsensor-RNA在RNA测序(RNA-seq)、微阵列和单细胞RNA-seq等多平台技术中批量和单细胞基因表达数据方面的良好性能和前景。MSIsensor-RNA 是一种多功能、高效、稳健的方法,可在临床研究和应用中从大量和单细胞基因表达数据中检测 MSI 状态。MSIsensor-RNA可在https://github.com/xjtu-omics/msisensor-rna。
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来源期刊
Genomics, Proteomics & Bioinformatics
Genomics, Proteomics & Bioinformatics Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
14.30
自引率
4.20%
发文量
844
审稿时长
61 days
期刊介绍: Genomics, Proteomics and Bioinformatics (GPB) is the official journal of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. It aims to disseminate new developments in the field of omics and bioinformatics, publish high-quality discoveries quickly, and promote open access and online publication. GPB welcomes submissions in all areas of life science, biology, and biomedicine, with a focus on large data acquisition, analysis, and curation. Manuscripts covering omics and related bioinformatics topics are particularly encouraged. GPB is indexed/abstracted by PubMed/MEDLINE, PubMed Central, Scopus, BIOSIS Previews, Chemical Abstracts, CSCD, among others.
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