GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure

• Main Authors: Bender, M.R (Author), Biggs, T.D (Author), Feltus, F.A (Author), Ficklin, S.P (Author), Hadish, J.A (Author), Honaas, L. (Author), McKnight, C.B (Author), Shealy, B.T (Author), Smith, M.C (Author), Wytko, C. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2022
• Subjects: article; Co-expression networks; differential gene expression; Differential gene expression; Differential gene expressions; Digital storage; Gene co-expression network; gene expression; Gene expression; Gene expression matrix; Genes expression; Information management; information storage; matrix; Nextflow; Program processors; protein expression; Reusability; RNA; RNA sequencing; RNA-seq; RNA-Seq datum; software; workflow; Workflows; Work-flows
• Online Access: View Fulltext in Publisher

 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. © 2022, The Author(s).