CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data
Year: 2020
Type: article
Abstract: 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.
Source: Nature Communications
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,380
Funders: U.S. Department of Health and Human Services, University of Texas at Austin, National Institutes of Health, Department of Mechanical Engineering, University of Texas at Austin, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Arthritis and Musculoskeletal and Skin Diseases
Awards: Systems Modeling Guided Bone regeneration, Tissue Engineering Approaches for Improved Treatment of Early Stage Osteonecrosis of the Hip, Multiscale Modeling for Treatment Discovery in Duchenne Muscular Dystrophy, G5838913951, Integrative approach to studying LncRNA functions, Multiscale Resolution and Deep Network Approaches for Deconvolving Different Cell Types in Bulk Tumor using Single-cell Sequencing Data (scDEC)
OpenAlex ID: W2997123690