Literature DB >> 30026912

RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis, and result reporting.

Travis L Jensen1, Michael Frasketi2, Kevin Conway2, Leigh Villarroel2, Heather Hill1, Konstantinos Krampis3, Johannes B Goll1.   

Abstract

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).

Entities:  

Keywords:  RNA-Seq; RSEQREP; cloud computing; differential gene expression; pathway enrichment; reproducible research; transcriptomics; trivalent influenza vaccine

Year:  2017        PMID: 30026912      PMCID: PMC6039931          DOI: 10.12688/f1000research.13049.2

Source DB:  PubMed          Journal:  F1000Res        ISSN: 2046-1402


  41 in total

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Journal:  BMC Bioinformatics       Date:  2011-08-04       Impact factor: 3.307

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Journal:  Nat Immunol       Date:  2011-07-10       Impact factor: 25.606

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Journal:  Nat Immunol       Date:  2013-12-15       Impact factor: 25.606

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Journal:  Nat Methods       Date:  2012-11-18       Impact factor: 28.547

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Journal:  Bioinformatics       Date:  2014-09-25       Impact factor: 6.937

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Journal:  Bioinformatics       Date:  2009-11-11       Impact factor: 6.937

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  5 in total

1.  RNAdetector: a free user-friendly stand-alone and cloud-based system for RNA-Seq data analysis.

Authors:  Alessandro La Ferlita; Salvatore Alaimo; Sebastiano Di Bella; Emanuele Martorana; Georgios I Laliotis; Francesco Bertoni; Luciano Cascione; Philip N Tsichlis; Alfredo Ferro; Roberta Bosotti; Alfredo Pulvirenti
Journal:  BMC Bioinformatics       Date:  2021-06-03       Impact factor: 3.169

2.  Reproducible big data science: A case study in continuous FAIRness.

Authors:  Ravi Madduri; Kyle Chard; Mike D'Arcy; Segun C Jung; Alexis Rodriguez; Dinanath Sulakhe; Eric Deutsch; Cory Funk; Ben Heavner; Matthew Richards; Paul Shannon; Gustavo Glusman; Nathan Price; Carl Kesselman; Ian Foster
Journal:  PLoS One       Date:  2019-04-11       Impact factor: 3.240

3.  Next-Generation Sequencing Profiles of the Methylome and Transcriptome in Peripheral Blood Mononuclear Cells of Rheumatoid Arthritis.

Authors:  Chia-Chun Tseng; Yuan-Zhao Lin; Chia-Hui Lin; Ruei-Nian Li; Chang-Yi Yen; Hua-Chen Chan; Wen-Chan Tsai; Tsan-Teng Ou; Cheng-Chin Wu; Wan-Yu Sung; Jeng-Hsien Yen
Journal:  J Clin Med       Date:  2019-08-22       Impact factor: 4.241

Review 4.  RNA-sequencing in ophthalmology research: considerations for experimental design and analysis.

Authors:  Nicholas Owen; Mariya Moosajee
Journal:  Ther Adv Ophthalmol       Date:  2019-03-15

5.  Read Mapping and Transcript Assembly: A Scalable and High-Throughput Workflow for the Processing and Analysis of Ribonucleic Acid Sequencing Data.

Authors:  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
Journal:  Front Genet       Date:  2020-01-24       Impact factor: 4.599

  5 in total

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