Title
GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure
Description
Abstract 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 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 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.
Publication Date
1-1-2022
Publisher
figshare Academic Research System
DOI
10.6084/m9.figshare.c.5976143.v1
Document Type
Data Set
Recommended Citation
McKnight, Coleman B.; Wytko, Connor; Smith, Melissa C.; Shealy, Benjamin T.; Biggs, Tyler D.; Bender, M. Reed; Hadish, John A.; Feltus, F. Alex; Honaas, Loren; Ficklin, Stephen P. (2022), "GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure", figshare Academic Research System, doi: 10.6084/m9.figshare.c.5976143.v1
https://doi.org/10.6084/m9.figshare.c.5976143.v1
Identifier
10.6084/m9.figshare.c.5976143.v1
Embargo Date
1-1-2022