Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data
Kreko, Isabella R
McHan, Lauren B
Murphy, Rebecca L
Gregory, Brian D
Devisetty, Upendra K
Nelson, Andrew D L
AffiliationUniv Arizona, Genet Grad Interdisciplinary Grp
Univ Arizona, CyVerse
Univ Arizona, Sch Plant Sci, LIVE For Plants Summer Res Program
MetadataShow full item record
PublisherFRONTIERS MEDIA SA
CitationPeri S, Roberts S, Kreko IR, McHan LB, Naron A, Ram A, Murphy RL, Lyons E, Gregory BD, Devisetty UK and Nelson ADL (2020) Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data. Front. Genet. 10:1361. doi: 10.3389/fgene.2019.01361
JournalFRONTIERS IN GENETICS
RightsCopyright © 2020 Peri, Roberts, Kreko, McHan, Naron, Ram, Murphy, Lyons, Gregory, Devisetty and Nelson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractNext-generation RNA-sequencing is an incredibly powerful means of generating a snapshot of the transcriptomic state within a cell, tissue, or whole organism. As the questions addressed by RNA-sequencing (RNA-seq) become both more complex and greater in number, there is a need to simplify RNA-seq processing workflows, make them more efficient and interoperable, and capable of handling both large and small datasets. This is especially important for researchers who need to process hundreds to tens of thousands of RNA-seq datasets. To address these needs, we have developed a scalable, user-friendly, and easily deployable analysis suite called RMTA (Read Mapping, Transcript Assembly). RMTA can easily process thousands of RNA-seq datasets with features that include automated read quality analysis, filters for lowly expressed transcripts, and read counting for differential expression analysis. RMTA is containerized using Docker for easy deployment within any compute environment [cloud, local, or high-performance computing (HPC)] and is available as two apps in CyVerse's Discovery Environment, one for normal use and one specifically designed for introducing undergraduates and high school to RNA-seq analysis. For extremely large datasets (tens of thousands of FASTq files) we developed a high-throughput, scalable, and parallelized version of RMTA optimized for launching on the Open Science Grid (OSG) from within the Discovery Environment. OSG-RMTA allows users to utilize the Discovery Environment for data management, parallelization, and submitting jobs to OSG, and finally, employ the OSG for distributed, high throughput computing. Alternatively, OSG-RMTA can be run directly on the OSG through the command line. RMTA is designed to be useful for data scientists, of any skill level, interested in rapidly and reproducibly analyzing their large RNA-seq data sets.
NoteOpen access journal
VersionFinal published version
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