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用于单细胞基因表达和遗传变异综合分析的MOCA

MOCA for Integrated Analysis of Gene Expression and Genetic Variation in Single Cells.

作者信息

Huzar Jared, Kim Hannah, Kumar Sudhir, Miura Sayaka

机构信息

Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States.

Department of Biology, Temple University, Philadelphia, PA, United States.

出版信息

Front Genet. 2022 Mar 31;13:831040. doi: 10.3389/fgene.2022.831040. eCollection 2022.

Abstract

In cancer, somatic mutations occur continuously, causing cell populations to evolve. These somatic mutations result in the evolution of cellular gene expression patterns that can also change due to epigenetic modifications and environmental changes. By exploring the concordance of gene expression changes with molecular evolutionary trajectories of cells, we can examine the role of somatic variation on the evolution of gene expression patterns. We present Multi-Omics Concordance Analysis (MOCA) software to jointly analyze gene expressions and genetic variations from single-cell RNA sequencing profiles. MOCA outputs cells and genes showing convergent and divergent gene expression patterns in functional genomics.

摘要

在癌症中,体细胞突变持续发生,导致细胞群体进化。这些体细胞突变导致细胞基因表达模式发生进化,而这种模式也可能因表观遗传修饰和环境变化而改变。通过探索基因表达变化与细胞分子进化轨迹的一致性,我们可以研究体细胞变异在基因表达模式进化中的作用。我们展示了多组学一致性分析(MOCA)软件,用于联合分析单细胞RNA测序图谱中的基因表达和遗传变异。MOCA输出在功能基因组学中显示趋同和趋异基因表达模式的细胞和基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a44/9009314/c9826fbf4f32/fgene-13-831040-g001.jpg

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