GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure
Posted on 2022-05-03 - 04:14
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.
CITE THIS COLLECTION
DataCiteDataCite
3 Biotech3 Biotech
3D Printing in Medicine3D Printing in Medicine
3D Research3D Research
3D-Printed Materials and Systems3D-Printed Materials and Systems
4OR4OR
AAPG BulletinAAPG Bulletin
AAPS OpenAAPS Open
AAPS PharmSciTechAAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität HamburgAbhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)ABI Technik (German)
Academic MedicineAcademic Medicine
Academic PediatricsAcademic Pediatrics
Academic PsychiatryAcademic Psychiatry
Academic QuestionsAcademic Questions
Academy of Management DiscoveriesAcademy of Management Discoveries
Academy of Management JournalAcademy of Management Journal
Academy of Management Learning and EducationAcademy of Management Learning and Education
Academy of Management PerspectivesAcademy of Management Perspectives
Academy of Management ProceedingsAcademy of Management Proceedings
Academy of Management ReviewAcademy of Management Review
Hadish, John A.; Biggs, Tyler D.; Shealy, Benjamin T.; Bender, M. Reed; McKnight, Coleman B.; Wytko, Connor; et al. (2022). GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5976143.v1