Cargando…

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

MOTIVATION: In 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 sufficiently supported by available tools designed for...

Descripción completa

Detalles Bibliográficos
Autores principales: Edwards, Nathan, Dillard, Christian, Prashant, N M, Hongyu, Liu, Yang, Mia, Ulianova, Evgenia, Horvath, Anelia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825775/
https://www.ncbi.nlm.nih.gov/pubmed/36448703
http://dx.doi.org/10.1093/bioinformatics/btac768
_version_ 1784866695940145152
author Edwards, Nathan
Dillard, Christian
Prashant, N M
Hongyu, Liu
Yang, Mia
Ulianova, Evgenia
Horvath, Anelia
author_facet Edwards, Nathan
Dillard, Christian
Prashant, N M
Hongyu, Liu
Yang, Mia
Ulianova, Evgenia
Horvath, Anelia
author_sort Edwards, Nathan
collection PubMed
description MOTIVATION: In 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 sufficiently supported by available tools designed for high-throughput sequencing data. RESULTS: We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers—GATK and Strelka2—executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System). In conclusion, SCExecute 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. AVAILABILITY AND IMPLEMENTATION: SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
format Online
Article
Text
id pubmed-9825775
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-98257752023-01-10 SCExecute: custom cell barcode-stratified analyses of scRNA-seq data Edwards, Nathan Dillard, Christian Prashant, N M Hongyu, Liu Yang, Mia Ulianova, Evgenia Horvath, Anelia Bioinformatics Applications Note MOTIVATION: In 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 sufficiently supported by available tools designed for high-throughput sequencing data. RESULTS: We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers—GATK and Strelka2—executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System). In conclusion, SCExecute 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. AVAILABILITY AND IMPLEMENTATION: SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-11-30 /pmc/articles/PMC9825775/ /pubmed/36448703 http://dx.doi.org/10.1093/bioinformatics/btac768 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Edwards, Nathan
Dillard, Christian
Prashant, N M
Hongyu, Liu
Yang, Mia
Ulianova, Evgenia
Horvath, Anelia
SCExecute: custom cell barcode-stratified analyses of scRNA-seq data
title SCExecute: custom cell barcode-stratified analyses of scRNA-seq data
title_full SCExecute: custom cell barcode-stratified analyses of scRNA-seq data
title_fullStr SCExecute: custom cell barcode-stratified analyses of scRNA-seq data
title_full_unstemmed SCExecute: custom cell barcode-stratified analyses of scRNA-seq data
title_short SCExecute: custom cell barcode-stratified analyses of scRNA-seq data
title_sort scexecute: custom cell barcode-stratified analyses of scrna-seq data
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825775/
https://www.ncbi.nlm.nih.gov/pubmed/36448703
http://dx.doi.org/10.1093/bioinformatics/btac768
work_keys_str_mv AT edwardsnathan scexecutecustomcellbarcodestratifiedanalysesofscrnaseqdata
AT dillardchristian scexecutecustomcellbarcodestratifiedanalysesofscrnaseqdata
AT prashantnm scexecutecustomcellbarcodestratifiedanalysesofscrnaseqdata
AT hongyuliu scexecutecustomcellbarcodestratifiedanalysesofscrnaseqdata
AT yangmia scexecutecustomcellbarcodestratifiedanalysesofscrnaseqdata
AT ulianovaevgenia scexecutecustomcellbarcodestratifiedanalysesofscrnaseqdata
AT horvathanelia scexecutecustomcellbarcodestratifiedanalysesofscrnaseqdata