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SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data by N. M. Prashant; Nawaf Alomran; Yu Chen; Hongyu Liu; Pavlos Bousounis; Mercedeh Movassagh; Nathan Edwards; Anelia Horvath is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data about?

## Abstract ## Background Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. ## Results To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAF~RNA~) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a

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Author

N. M. Prashant; Nawaf Alomran; Yu Chen; Hongyu Liu; Pavlos Bousounis; Mercedeh Movassagh; Nathan Edwards; Anelia Horvath

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)