Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data
| Autor: | Sateesh Peri, Sarah Roberts, Isabella R. Kreko, Lauren B. McHan, Alexandra Naron, Archana Ram, Rebecca L. Murphy, Eric Lyons, Brian D. Gregory, Upendra K. Devisetty, Andrew D. L. Nelson |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
FASTQ format lcsh:QH426-470 Computer science workflow Data management Distributed computing Cloud computing high throughput (-omics) techniques 03 medical and health sciences transcriptomics 0302 clinical medicine Genetics Methods High-throughput computing Genetics (clinical) business.industry bioinformatics Grid lcsh:Genetics 030104 developmental biology Workflow 030220 oncology & carcinogenesis Scalability Molecular Medicine Snapshot (computer storage) RNA-seq business |
| Zdroj: | Frontiers in Genetics Frontiers in Genetics, Vol 10 (2020) |
| ISSN: | 1664-8021 |
| Popis: | Next-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. |
| Databáze: | OpenAIRE |
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