CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data

被引:85
|
作者
Harmanci, Akdes Serin [1 ]
Harmanci, Arif O. [2 ]
Zhou, Xiaobo [1 ,3 ,4 ]
机构
[1] Univ Texas Hlth Sci Ctr Houston, Ctr Computat Syst Med, Sch Biomed Informat, Houston, TX 77030 USA
[2] Univ Texas Hlth Sci Ctr Houston, Ctr Precis Hlth, Sch Biomed Informat, Houston, TX 77030 USA
[3] Univ Texas Hlth Sci Ctr Houston, Dept Integrat Biol & Pharmacol, McGovern Med Sch, Houston, TX 77030 USA
[4] Univ Texas Hlth Sci Ctr Houston, Sch Dent, Houston, TX 77054 USA
关键词
SEQ; GENOME; LANDSCAPE;
D O I
10.1038/s41467-019-13779-x
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
RNA sequencing experiments generate large amounts of information about expression levels of genes. Although they are mainly used for quantifying expression levels, they contain much more biologically important information such as copy number variants (CNVs). Here, we present CaSpER, a signal processing approach for identification, visualization, and integrative analysis of focal and large-scale CNV events in multiscale resolution using either bulk or single-cell RNA sequencing data. CaSpER integrates the multiscale smoothing of expression signal and allelic shift signals for CNV calling. The allelic shift signal measures the loss-of-heterozygosity (LOH) which is valuable for CNV identification. CaSpER employs an efficient methodology for the generation of a genome-wide B-allele frequency (BAF) signal profile from the reads and utilizes it for correction of CNVs calls. CaSpER increases the utility of RNA-sequencing datasets and complements other tools for complete characterization and visualization of the genomic and transcriptomic landscape of single cell and bulk RNA sequencing data.
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页数:16
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