Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data
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 pro...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-01-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.01361/full |
| _version_ | 1819107516931375104 |
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| author | Sateesh Peri Sarah Roberts Isabella R. Kreko Lauren B. McHan Alexandra Naron Archana Ram Rebecca L. Murphy Eric Lyons Eric Lyons Brian D. Gregory Upendra K. Devisetty Andrew D. L. Nelson |
| author_facet | Sateesh Peri Sarah Roberts Isabella R. Kreko Lauren B. McHan Alexandra Naron Archana Ram Rebecca L. Murphy Eric Lyons Eric Lyons Brian D. Gregory Upendra K. Devisetty Andrew D. L. Nelson |
| author_sort | Sateesh Peri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T02:55:17Z |
| format | Article |
| id | doaj.art-6711094ea7604af6beaf8dbe55e6218c |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-22T02:55:17Z |
| publishDate | 2020-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-6711094ea7604af6beaf8dbe55e6218c2022-12-21T18:41:16ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-01-011010.3389/fgene.2019.01361485433Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing DataSateesh Peri0Sarah Roberts1Isabella R. Kreko2Lauren B. McHan3Alexandra Naron4Archana Ram5Rebecca L. Murphy6Eric Lyons7Eric Lyons8Brian D. Gregory9Upendra K. Devisetty10Andrew D. L. Nelson11Genetics Graduate Interdisciplinary Group, University of Arizona, Tucson, AZ, United StatesCyVerse, University of Arizona, Tucson, AZ, United StatesLIVE-for-Plants Summer Research Program, School of Plant Sciences, University of Arizona, Tucson, AZ, United StatesLIVE-for-Plants Summer Research Program, School of Plant Sciences, University of Arizona, Tucson, AZ, United StatesLIVE-for-Plants Summer Research Program, School of Plant Sciences, University of Arizona, Tucson, AZ, United StatesLIVE-for-Plants Summer Research Program, School of Plant Sciences, University of Arizona, Tucson, AZ, United StatesBiology Department, Centenary College of Louisiana, Shreveport, LA, United StatesGenetics Graduate Interdisciplinary Group, University of Arizona, Tucson, AZ, United StatesCyVerse, University of Arizona, Tucson, AZ, United StatesDepartment of Biology, University of Pennsylvania, Philadelphia, PA, United StatesCyVerse, University of Arizona, Tucson, AZ, United StatesBoyce Thompson Institute, Cornell University, Ithaca, NY, United StatesNext-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.https://www.frontiersin.org/article/10.3389/fgene.2019.01361/fullRNA-seqtranscriptomicshigh throughput (-omics) techniquesbioinformaticsworkflow |
| spellingShingle | Sateesh Peri Sarah Roberts Isabella R. Kreko Lauren B. McHan Alexandra Naron Archana Ram Rebecca L. Murphy Eric Lyons Eric Lyons Brian D. Gregory Upendra K. Devisetty Andrew D. L. Nelson Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data Frontiers in Genetics RNA-seq transcriptomics high throughput (-omics) techniques bioinformatics workflow |
| title | Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data |
| title_full | Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data |
| title_fullStr | Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data |
| title_full_unstemmed | Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data |
| title_short | Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data |
| title_sort | read mapping and transcript assembly a scalable and high throughput workflow for the processing and analysis of ribonucleic acid sequencing data |
| topic | RNA-seq transcriptomics high throughput (-omics) techniques bioinformatics workflow |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.01361/full |
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