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Reproducible RNA-seq analysis using recount2

Nature Biotechnology volume 35, pages 319–321 (2017)Cite this article

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To the Editor:

RNA sequencing (RNA-seq) is used to measure gene expression levels across the transcriptome for a huge variety of samples. For example, RNA-seq has been applied to study gene expression in individuals with rare diseases1, in hard-to-obtain tissues2 or for rare forms of cancer3. Recently, enormous RNA-seq datasets have been produced in the GTEx (Genotype-Tissue Expression) study4, which comprises 9,662 samples from 551 individuals and 54 body sites, and in the Cancer Genome Atlas (TCGA) study, which comprises 11,350 samples from 10,340 individuals and 33 cancer types. Public data repositories, such as the Sequence Read Archive (SRA), host >50,000 human RNA-seq samples. It is estimated that these repositories are likely to double in size every 18 months5. Deposited data are provided as raw sequencing reads, which are costly for standard academic labs researchers to analyze. Efforts have been made to standardize and publish ready-to-analyze summaries of both DNA sequencing6 and exome-sequencing7 data. Adopting a similar approach for archived RNA-seq data, we have developed recount2, which comprises >4.4 trillion uniformly processed and quantified RNA-seq reads.

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Figure 1: Meta-analysis and study comparison facilitated by recount2.
Figure 2: Multi-feature-level differential expression analysis is facilitated by recount2.

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Acknowledgements

We thank C. Kingsford and D. Filippova for their assistance in adding SHARQ metadata to recount2. recount2 data are hosted on SciServer, a collaborative research environment for large-scale data-driven science. It is being developed at, and administered by, the Institute for Data Intensive Engineering and Science at Johns Hopkins University. SciServer is funded by the National Science Foundation Award ACI-1261715. For more information about SciServer, visit http://www.sciserver.org/. We thank E. Lehnert and P. Radovic at Seven Bridges for their help accessing RNA-seq data from TCGA using the Cancer Genomics Cloud API and depositing results in a shared bucket on Amazon S3. We also thank other Seven Bridges team members for facilitating our TCGA reanalysis, including B. Dusenbery, N. Tijanic, M. Kovacevic, M. Sadoff and G. Kaushik. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health. Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI/SAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through an SAIC-F subcontract to Van Andel Institute (10ST1035). Additional data repository and project management were provided by SAIC-F (HHSN261200800001E). The Brain Bank was supported by a supplement to University of Miami grants DA006227 and DA033684 and to contract N01MH000028. Statistical Methods development grants were made to the University of Geneva (MH090941 & MH101814), the University of Chicago (MH090951, MH090937, MH101820, MH101825), the University of North Carolina–Chapel Hill (MH090936 & MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University St. Louis (MH101810) and the University of Pennsylvania (MH101822). The data used for the analyses described in this manuscript were obtained from: the GTEx Portal on 11/21/15 and/or dbGaP accession number phs000424.v6.p1 on 11/30/15—12/04/15. Funding: B.L., J.T.L., L.C.T., S.E., A.N., M.T., K.H. and K.K. were supported by NIH R01 GM105705. L.C.T. was supported by Consejo Nacional de Ciencia y Tecnología México 351535. L.C.T. and A.E.J. were supported by 1R21MH109956. Amazon Web Services experiments were supported by AWS in Education research grants. Storage costs on S3 for TCGA runs were partially covered by a grant from Seven Bridges Genomics for use of the Cancer Genomics Cloud.

Author information

Author notes

  1. Leonardo Collado-Torres and Abhinav Nellore: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

    Leonardo Collado-Torres, Kai Kammers, Shannon E Ellis, Margaret A Taub, Kasper D Hansen, Andrew E Jaffe, Ben Langmead & Jeffrey T Leek

  2. Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland, USA

    Leonardo Collado-Torres, Kai Kammers, Shannon E Ellis, Margaret A Taub, Kasper D Hansen, Andrew E Jaffe, Ben Langmead & Jeffrey T Leek

  3. Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland, USA

    Leonardo Collado-Torres & Andrew E Jaffe

  4. Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA

    Abhinav Nellore

  5. Department of Surgery, Oregon Health & Science University, Portland, Oregon, USA

    Abhinav Nellore

  6. Computational Biology Program, Oregon Health & Science University, Portland, Oregon, USA

    Abhinav Nellore

  7. Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Kai Kammers

  8. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA

    Kasper D Hansen

  9. Department of Mental Health, Johns Hopkins University, Baltimore, Maryland, USA

    Andrew E Jaffe

  10. Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA

    Ben Langmead

Authors
  1. Leonardo Collado-Torres
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Corresponding authors

Correspondence to Andrew E Jaffe, Ben Langmead or Jeffrey T Leek.

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Supplementary information

Supplementary Text and Figures

Supplementary Methods (PDF 200 kb)

Supplementary Note 1

Comparison of Recount with GTEx (PDF 4173 kb)

Supplementary Note 2

Recount Meta-Analysis (PDF 247 kb)

Supplementary Note 3

Recount (gene and exon analyses) (PDF 69269 kb)

Supplementary Note 4

Recount (DER analyses) (PDF 25233 kb)

Supplementary Note 5

Recount (overly two studies) (PDF 11891 kb)

Supplementary Code 1

Recount-analyses repo code (ZIP 241174 kb)

Supplementary Code 2

Recount-website (ZIP 36097 kb)

Supplementary Code 3

Nellore/runs (ZIP 264948 kb)

Supplementary Code 4

Recount-contributions (ZIP 3 kb)

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Collado-Torres, L., Nellore, A., Kammers, K. et al. Reproducible RNA-seq analysis using recount2. Nat Biotechnol 35, 319–321 (2017). https://doi.org/10.1038/nbt.3838

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