Ulianova, E. | bioRxiv Science

bioRxiv2022

A wealth of novel cell-specific expressed SNVs from tumor and normal scRNA-seq datasets

We demonstrate a novel variant calling strategy using barcode-stratified alignments on 25 tumor and normal 10XGenomics scRNA-seq datasets (>200,000 cells). Our approach identified 24,528 exonic non-dbSNP single cell expressed (sce)SNVs, a third of which are shared across multiple samples. The novel sceSNVs include unreported somatic and germline variants, as well as RNA-originating variants; some are expressed in up to 17% of the cells, and many are found in known cancer genes. Our findings suggest that there is an unacknowledged repertoire of expressed genetic variants, possibly recurrent and common across samples, in the normal and cancer transcriptome.

genomics↗

bioRxiv2022

SCExecute: cell barcode-stratified analyses of scRNA-seq data

MotivationIn single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not supported by available tools that are designed for bulk RNA-Seq data. ResultsWe introduce a tool - SCExecute - which executes a user-provided command on barcode-stratified, extracted on-the-fly, single cell binary alignment map (scBAM) files. SCExecute extracts the cell barcode from aligned, pooled single-cell sequencing data. The user-specified command option executes all the commands defined in the session from monolithic programs and multi-command shell-scripts to complex shell-based pipelines. The execution can be further restricted to barcodes or/and genomic regions of interest. We demonstrate SCExecute with two popular variant callers - GATK and Strelka2 - combined with modules for bam file manipulation and variant filtering, to detect single cell-specific expressed Single Nucleotide Variants (sceSNVs) from droplet scRNA-seq data (10X Genomics Chromium System). ConclusionSCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. AvailabilitySCExecute is implemented in Python3 using the PySAM package and distributed for Linux and Python environments from https://github.com/HorvathLab/NGS/tree/master/SCExecute.

genomics↗