Cargando…
RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting
RNA-Seq is increasingly being used to measure human RNA expression on a genome-wide scale. Expression profiles can be interrogated to identify and functionally characterize treatment-responsive genes. Ultimately, such controlled studies promise to reveal insights into molecular mechanisms of treatme...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039931/ https://www.ncbi.nlm.nih.gov/pubmed/30026912 http://dx.doi.org/10.12688/f1000research.13049.2 |
_version_ | 1783338767705178112 |
---|---|
author | Jensen, Travis L. Frasketi, Michael Conway, Kevin Villarroel, Leigh Hill, Heather Krampis, Konstantinos Goll, Johannes B. |
author_facet | Jensen, Travis L. Frasketi, Michael Conway, Kevin Villarroel, Leigh Hill, Heather Krampis, Konstantinos Goll, Johannes B. |
author_sort | Jensen, Travis L. |
collection | PubMed |
description | RNA-Seq is increasingly being used to measure human RNA expression on a genome-wide scale. Expression profiles can be interrogated to identify and functionally characterize treatment-responsive genes. Ultimately, such controlled studies promise to reveal insights into molecular mechanisms of treatment effects, identify biomarkers, and realize personalized medicine. RNA-Seq Reports (RSEQREP) is a new open-source cloud-enabled framework that allows users to execute start-to-end gene-level RNA-Seq analysis on a preconfigured RSEQREP Amazon Virtual Machine Image (AMI) hosted by AWS or on their own Ubuntu Linux machine via a Docker container or installation script. The framework works with unstranded, stranded, and paired-end sequence FASTQ files stored locally, on Amazon Simple Storage Service (S3), or at the Sequence Read Archive (SRA). RSEQREP automatically executes a series of customizable steps including reference alignment, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially expressed genes, heatmaps, co-expressed gene clusters, enriched pathways, and a series of custom visualizations. The framework outputs a file collection that includes a dynamically generated PDF report using R, knitr, and LaTeX, as well as publication-ready table and figure files. A user-friendly configuration file handles sample metadata entry, processing, analysis, and reporting options. The configuration supports time series RNA-Seq experimental designs with at least one pre- and one post-treatment sample for each subject, as well as multiple treatment groups and specimen types. All RSEQREP analyses components are built using open-source R code and R/Bioconductor packages allowing for further customization. As a use case, we provide RSEQREP results for a trivalent influenza vaccine (TIV) RNA-Seq study that collected 1 pre-TIV and 10 post-TIV vaccination samples (days 1-10) for 5 subjects and two specimen types (peripheral blood mononuclear cells and B-cells). |
format | Online Article Text |
id | pubmed-6039931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-60399312018-07-18 RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting Jensen, Travis L. Frasketi, Michael Conway, Kevin Villarroel, Leigh Hill, Heather Krampis, Konstantinos Goll, Johannes B. F1000Res Software Tool Article RNA-Seq is increasingly being used to measure human RNA expression on a genome-wide scale. Expression profiles can be interrogated to identify and functionally characterize treatment-responsive genes. Ultimately, such controlled studies promise to reveal insights into molecular mechanisms of treatment effects, identify biomarkers, and realize personalized medicine. RNA-Seq Reports (RSEQREP) is a new open-source cloud-enabled framework that allows users to execute start-to-end gene-level RNA-Seq analysis on a preconfigured RSEQREP Amazon Virtual Machine Image (AMI) hosted by AWS or on their own Ubuntu Linux machine via a Docker container or installation script. The framework works with unstranded, stranded, and paired-end sequence FASTQ files stored locally, on Amazon Simple Storage Service (S3), or at the Sequence Read Archive (SRA). RSEQREP automatically executes a series of customizable steps including reference alignment, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially expressed genes, heatmaps, co-expressed gene clusters, enriched pathways, and a series of custom visualizations. The framework outputs a file collection that includes a dynamically generated PDF report using R, knitr, and LaTeX, as well as publication-ready table and figure files. A user-friendly configuration file handles sample metadata entry, processing, analysis, and reporting options. The configuration supports time series RNA-Seq experimental designs with at least one pre- and one post-treatment sample for each subject, as well as multiple treatment groups and specimen types. All RSEQREP analyses components are built using open-source R code and R/Bioconductor packages allowing for further customization. As a use case, we provide RSEQREP results for a trivalent influenza vaccine (TIV) RNA-Seq study that collected 1 pre-TIV and 10 post-TIV vaccination samples (days 1-10) for 5 subjects and two specimen types (peripheral blood mononuclear cells and B-cells). F1000 Research Limited 2018-04-13 /pmc/articles/PMC6039931/ /pubmed/30026912 http://dx.doi.org/10.12688/f1000research.13049.2 Text en Copyright: © 2018 Jensen TL et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Tool Article Jensen, Travis L. Frasketi, Michael Conway, Kevin Villarroel, Leigh Hill, Heather Krampis, Konstantinos Goll, Johannes B. RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting |
title | RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting |
title_full | RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting |
title_fullStr | RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting |
title_full_unstemmed | RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting |
title_short | RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting |
title_sort | rseqrep: rna-seq reports, an open-source cloud-enabled framework for reproducible rna-seq data processing, analysis, and result reporting |
topic | Software Tool Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039931/ https://www.ncbi.nlm.nih.gov/pubmed/30026912 http://dx.doi.org/10.12688/f1000research.13049.2 |
work_keys_str_mv | AT jensentravisl rseqreprnaseqreportsanopensourcecloudenabledframeworkforreproduciblernaseqdataprocessinganalysisandresultreporting AT frasketimichael rseqreprnaseqreportsanopensourcecloudenabledframeworkforreproduciblernaseqdataprocessinganalysisandresultreporting AT conwaykevin rseqreprnaseqreportsanopensourcecloudenabledframeworkforreproduciblernaseqdataprocessinganalysisandresultreporting AT villarroelleigh rseqreprnaseqreportsanopensourcecloudenabledframeworkforreproduciblernaseqdataprocessinganalysisandresultreporting AT hillheather rseqreprnaseqreportsanopensourcecloudenabledframeworkforreproduciblernaseqdataprocessinganalysisandresultreporting AT krampiskonstantinos rseqreprnaseqreportsanopensourcecloudenabledframeworkforreproduciblernaseqdataprocessinganalysisandresultreporting AT golljohannesb rseqreprnaseqreportsanopensourcecloudenabledframeworkforreproduciblernaseqdataprocessinganalysisandresultreporting |