GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure.
Differential gene expression
Gene co-expression network
Gene expression matrix
Nextflow
RNA-seq
Workflows
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
02 May 2022
02 May 2022
Historique:
received:
03
09
2021
accepted:
07
03
2022
entrez:
3
5
2022
pubmed:
4
5
2022
medline:
6
5
2022
Statut:
epublish
Résumé
Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility. Processing of larger and deeper RNA-seq experiments will become more common as sequencing technology matures. GEMmaker, is a nf-core compliant, Nextflow workflow, that quantifies gene expression from small to massive RNA-seq datasets. GEMmaker ensures results are highly reproducible through the use of versioned containerized software that can be executed on a single workstation, institutional compute cluster, Kubernetes platform or the cloud. GEMmaker supports popular alignment and quantification tools providing results in raw and normalized formats. GEMmaker is unique in that it can scale to process thousands of local or remote stored samples without exceeding available data storage. Workflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of access to CPUs. GEMmaker provides these benefits and adds the ability to scale despite low data storage infrastructure. This allows users to process hundreds to thousands of RNA-seq samples even when data storage resources are limited. GEMmaker is freely available and fully documented with step-by-step setup and execution instructions.
Sections du résumé
BACKGROUND
BACKGROUND
Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility. Processing of larger and deeper RNA-seq experiments will become more common as sequencing technology matures.
RESULTS
RESULTS
GEMmaker, is a nf-core compliant, Nextflow workflow, that quantifies gene expression from small to massive RNA-seq datasets. GEMmaker ensures results are highly reproducible through the use of versioned containerized software that can be executed on a single workstation, institutional compute cluster, Kubernetes platform or the cloud. GEMmaker supports popular alignment and quantification tools providing results in raw and normalized formats. GEMmaker is unique in that it can scale to process thousands of local or remote stored samples without exceeding available data storage.
CONCLUSIONS
CONCLUSIONS
Workflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of access to CPUs. GEMmaker provides these benefits and adds the ability to scale despite low data storage infrastructure. This allows users to process hundreds to thousands of RNA-seq samples even when data storage resources are limited. GEMmaker is freely available and fully documented with step-by-step setup and execution instructions.
Identifiants
pubmed: 35501696
doi: 10.1186/s12859-022-04629-7
pii: 10.1186/s12859-022-04629-7
pmc: PMC9063052
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
156Subventions
Organisme : National Science Foundation
ID : 1659300
Organisme : Washington Tree Fruit Research Commission
ID : AP-19-103
Organisme : Washington State University
ID : Emerging Research Initiatives
Organisme : Washington State University
ID : Livestock Health
Organisme : Washington State University
ID : Food Security program award
Organisme : U.S. Department of Agriculture
ID : 1014919
Organisme : McIntyre Stennis
ID : WNP00009
Informations de copyright
© 2022. The Author(s).
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