| Autor: |
Serin Harmanci A; Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA., Harmanci AO; Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA., Zhou X; Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. Xiaobo.Zhou@uth.tmc.edu.; Department of Integrative Biology and Pharmacology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. Xiaobo.Zhou@uth.tmc.edu.; School of Dentistry, University of Texas Health Science Center at Houston, Houston, TX, 77054, USA. Xiaobo.Zhou@uth.tmc.edu. |
| Abstrakt: |
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. |
