Next Article in Journal
A Review of Probabilistic Genotyping Systems: EuroForMix, DNAStatistX and STRmix™
Next Article in Special Issue
A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies
Previous Article in Journal
Tandem Mass Tag-Based Quantitative Proteomic Analysis of ISG15 Knockout PK15 Cells in Pseudorabies Virus Infection
Article

Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments

1
McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
2
Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA
3
Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Alessandro Barbon
Received: 5 July 2021 / Revised: 25 September 2021 / Accepted: 28 September 2021 / Published: 30 September 2021
(This article belongs to the Special Issue Transcriptional and Genetic Tumor Heterogeneity through ScRNA-seq)
Currently, the detection of single nucleotide variants (SNVs) from 10 x Genomics single-cell RNA sequencing data (scRNA-seq) is typically performed on the pooled sequencing reads across all cells in a sample. Here, we assess the gaining of information regarding SNV assessments from individual cell scRNA-seq data, wherein the alignments are split by cellular barcode prior to the variant call. We also reanalyze publicly available data on the MCF7 cell line during anticancer treatment. We assessed SNV calls by three variant callers—GATK, Strelka2, and Mutect2, in combination with a method for the cell-level tabulation of the sequencing read counts bearing variant alleles–SCReadCounts (single-cell read counts). Our analysis shows that variant calls on individual cell alignments identify at least a two-fold higher number of SNVs as compared to the pooled scRNA-seq; these SNVs are enriched in novel variants and in stop-codon and missense substitutions. Our study indicates an immense potential of SNV calls from individual cell scRNA-seq data and emphasizes the need for cell-level variant detection approaches and tools, which can contribute to the understanding of the cellular heterogeneity and the relationships to phenotypes, and help elucidate somatic mutation evolution and functionality. View Full-Text
Keywords: scRNA-seq; SNV; mutation; somatic mutation; SNP; expressed SNVs; SNV expression scRNA-seq; SNV; mutation; somatic mutation; SNP; expressed SNVs; SNV expression
Show Figures

Figure 1

MDPI and ACS Style

N. M., P.; Liu, H.; Dillard, C.; Ibeawuchi, H.; Alsaeedy, T.; Chan, H.; Horvath, A.D. Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments. Genes 2021, 12, 1558. https://0-doi-org.brum.beds.ac.uk/10.3390/genes12101558

AMA Style

N. M. P, Liu H, Dillard C, Ibeawuchi H, Alsaeedy T, Chan H, Horvath AD. Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments. Genes. 2021; 12(10):1558. https://0-doi-org.brum.beds.ac.uk/10.3390/genes12101558

Chicago/Turabian Style

N. M., Prashant, Hongyu Liu, Christian Dillard, Helen Ibeawuchi, Turkey Alsaeedy, Hang Chan, and Anelia D. Horvath 2021. "Improved SNV Discovery in Barcode-Stratified scRNA-seq Alignments" Genes 12, no. 10: 1558. https://0-doi-org.brum.beds.ac.uk/10.3390/genes12101558

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop