Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud

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Abstract

Massively parallel RNA sequencing (RNA-seq) has rapidly become the assay of choice for interrogating RNA transcript abundance and diversity. This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at www.rnaseq.wiki.

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Most cited references 55

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The transcriptional landscape of the yeast genome defined by RNA sequencing.

U Nagalakshmi, Z. Wang, K. Waern … (2008)

The identification of untranslated regions, introns, and coding regions within an organism remains challenging. We developed a quantitative sequencing-based method called RNA-Seq for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome. We applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome and demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed. We confirmed many known and predicted introns and demonstrated that others are not actively used. Alternative initiation codons and upstream open reading frames also were identified for many yeast genes. We also found unexpected 3'-end heterogeneity and the presence of many overlapping genes. These results indicate that the yeast transcriptome is more complex than previously appreciated.

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Improved tools for biological sequence comparison.

W R Pearson, D J Lipman (1988)

We have developed three computer programs for comparisons of protein and DNA sequences. They can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity. The FASTA program is a more sensitive derivative of the FASTP program, which can be used to search protein or DNA sequence data bases and can compare a protein sequence to a DNA sequence data base by translating the DNA data base as it is searched. FASTA includes an additional step in the calculation of the initial pairwise similarity score that allows multiple regions of similarity to be joined to increase the score of related sequences. The RDF2 program can be used to evaluate the significance of similarity scores using a shuffling method that preserves local sequence composition. The LFASTA program can display all the regions of local similarity between two sequences with scores greater than a threshold, using the same scoring parameters and a similar alignment algorithm; these local similarities can be displayed as a "graphic matrix" plot or as individual alignments. In addition, these programs have been generalized to allow comparison of DNA or protein sequences based on a variety of alternative scoring matrices.

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Is Open Access

Pathview: an R/Bioconductor package for pathway-based data integration and visualization

Weijun Luo, Cory Brouwer (2013)

Summary: Pathview is a novel tool set for pathway-based data integration and visualization. It maps and renders user data on relevant pathway graphs. Users only need to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps and integrates user data onto the pathway and renders pathway graphs with the mapped data. Although built as a stand-alone program, Pathview may seamlessly integrate with pathway and functional analysis tools for large-scale and fully automated analysis pipelines. Availability: The package is freely available under the GPLv3 license through Bioconductor and R-Forge. It is available at http://bioconductor.org/packages/release/bioc/html/pathview.html and at http://Pathview.r-forge.r-project.org/. Contact: luo_weijun@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.

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Author and article information

Contributors

Francis Ouellette: Role: Editor

Journal

Journal ID (nlm-ta): PLoS Comput Biol

Journal ID (iso-abbrev): PLoS Comput. Biol

Journal ID (publisher-id): plos

Journal ID (pmc): ploscomp

Title: PLoS Computational Biology

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Print): 1553-734X

ISSN (Electronic): 1553-7358

Publication date (Electronic): 6 August 2015

Publication date Collection: August 2015

Volume: 11

Issue: 8

Electronic Location Identifier: e1004393

Affiliations

[1 ]McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, United States of America

[2 ]Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri, United States of America

[3 ]Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

[4 ]Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America

Ontario Institute for Cancer Research, CANADA

Author notes

The authors have declared that no competing interests exist.

* E-mail: mgriffit@ 123456genome.wustl.edu (MG); ogriffit@ 123456genome.wustl.edu (OLG)

Article

Publisher ID: PCOMPBIOL-D-15-00260

DOI: 10.1371/journal.pcbi.1004393

PMC ID: 4527835

PubMed ID: 26248053

SO-VID: 8a251775-df59-4f5e-b5b6-2eb7ba425506

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

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Figures: 6, Tables: 0, Pages: 20

Funding

Malachi Griffith was supported by NIH award K99HG007940. Obi L. Griffith was supported by NIH award K22CA188163. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud

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Journal of Systems Thinking Preprints

Most cited references 55

The transcriptional landscape of the yeast genome defined by RNA sequencing.

Improved tools for biological sequence comparison.

Pathview: an R/Bioconductor package for pathway-based data integration and visualization

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