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F1000Res. 2017 Aug 24;6:1558. doi: 10.12688/f1000research.12223.1. eCollection 2017.

recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor.

Author information

1
Lieber Institute for Brain Development, Baltimore, MD, 21205, USA.
2
Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205 , USA.
3
Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
4
Department of Surgery, Oregon Health and Science University, Portland, OR, 97239, USA.
5
Computational Biology Program, Oregon Health and Science University, Portland, OR, 97239, USA.
6
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
7
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.

Abstract

The recount2 resource is composed of over 70,000 uniformly processed human RNA-seq samples spanning TCGA and SRA, including GTEx. The processed data can be accessed via the recount2 website and the recount Bioconductor package. This workflow explains in detail how to use the recount package and how to integrate it with other Bioconductor packages for several analyses that can be carried out with the recount2 resource. In particular, we describe how the coverage count matrices were computed in recount2 as well as different ways of obtaining public metadata, which can facilitate downstream analyses. Step-by-step directions show how to do a gene-level differential expression analysis, visualize base-level genome coverage data, and perform an analyses at multiple feature levels. This workflow thus provides further information to understand the data in recount2 and a compendium of R code to use the data.

KEYWORDS:

Bioconductor; GTEx; RNA-seq; SRA; TCGA; bioinformatics; differential expression; genomics; human; visualization

Conflict of interest statement

Competing interests: No competing interests were disclosed.

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