Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

recount: A large-scale resource of analysis-ready RNA-seq expression data

Leonardo Collado-Torres, Abhinav Nellore, Kai Kammers, Shannon E. Ellis, Margaret A. Taub, Kasper D. Hansen, Andrew E. Jaffe, Ben Langmead, Jeffrey T. Leek
doi: https://doi.org/10.1101/068478
Leonardo Collado-Torres
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Center for Computational Biology, Johns Hopkins University
3Lieber Institute for Brain Development, Johns Hopkins Medical Campus
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Abhinav Nellore
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Center for Computational Biology, Johns Hopkins University
4Department of Computer Science, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kai Kammers
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Center for Computational Biology, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Shannon E. Ellis
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Center for Computational Biology, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Margaret A. Taub
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Center for Computational Biology, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kasper D. Hansen
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Center for Computational Biology, Johns Hopkins University
6McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew E. Jaffe
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Center for Computational Biology, Johns Hopkins University
3Lieber Institute for Brain Development, Johns Hopkins Medical Campus
5Department of Mental Health, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: andrew.jaffe@libd.org
Ben Langmead
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Center for Computational Biology, Johns Hopkins University
4Department of Computer Science, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: langmea@cs.jhu.edu
Jeffrey T. Leek
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
2Center for Computational Biology, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: jtleek@gmail.com
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

recount is a resource of processed and summarized expression data spanning nearly 60,000 human RNA-seq samples from the Sequence Read Archive (SRA). The associated recount Bio-conductor package provides a convenient API for querying, downloading, and analyzing the data. Each processed study consists of meta/phenotype data, the expression levels of genes and their underlying exons and splice junctions, and corresponding genomic annotation. We also provide data summarization types for quantifying novel transcribed sequence including base-resolution coverage and potentially unannotated splice junctions. We present workflows illustrating how to use recount to perform differential expression analysis including meta-analysis, annotation-free base-level analysis, and replication of smaller studies using data from larger studies. recount provides a valuable and user-friendly resource of processed RNA-seq datasets to draw additional biological insights from existing public data. The resource is available at https://jhubiostatistics.shinyapps.io/recount/.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted August 08, 2016.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
recount: A large-scale resource of analysis-ready RNA-seq expression data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
recount: A large-scale resource of analysis-ready RNA-seq expression data
Leonardo Collado-Torres, Abhinav Nellore, Kai Kammers, Shannon E. Ellis, Margaret A. Taub, Kasper D. Hansen, Andrew E. Jaffe, Ben Langmead, Jeffrey T. Leek
bioRxiv 068478; doi: https://doi.org/10.1101/068478
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
recount: A large-scale resource of analysis-ready RNA-seq expression data
Leonardo Collado-Torres, Abhinav Nellore, Kai Kammers, Shannon E. Ellis, Margaret A. Taub, Kasper D. Hansen, Andrew E. Jaffe, Ben Langmead, Jeffrey T. Leek
bioRxiv 068478; doi: https://doi.org/10.1101/068478

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (2653)
  • Biochemistry (5286)
  • Bioengineering (3696)
  • Bioinformatics (15824)
  • Biophysics (7279)
  • Cancer Biology (5633)
  • Cell Biology (8118)
  • Clinical Trials (138)
  • Developmental Biology (4782)
  • Ecology (7548)
  • Epidemiology (2059)
  • Evolutionary Biology (10604)
  • Genetics (7746)
  • Genomics (10163)
  • Immunology (5223)
  • Microbiology (13962)
  • Molecular Biology (5399)
  • Neuroscience (30878)
  • Paleontology (217)
  • Pathology (883)
  • Pharmacology and Toxicology (1527)
  • Physiology (2262)
  • Plant Biology (5035)
  • Scientific Communication and Education (1045)
  • Synthetic Biology (1399)
  • Systems Biology (4156)
  • Zoology (814)