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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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

Leonardo Collado-Torres^1,2, Abhinav Nellore^3,4,5, Andrew E Jaffe^1,2,6,7

¹Lieber Institute for Brain Development, Baltimore, MD, 21205, USA.

|October 19, 2017

Summary

The recount2 resource offers over 70,000 human RNA-seq samples for research. This guide details using the recount2 Bioconductor package for data analysis, including differential gene expression and genome coverage visualization.

Keywords:

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

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

The recount2 resource aggregates over 70,000 uniformly processed human RNA-sequencing (RNA-seq) samples from TCGA, SRA, and GTEx.
Access to this large-scale transcriptomic data is available through the recount2 website and a dedicated Bioconductor package.

Purpose of the Study:

To provide a detailed workflow for utilizing the recount2 Bioconductor package.
To demonstrate integration with other Bioconductor tools for various downstream analyses.
To explain the computation of coverage count matrices and metadata retrieval within recount2.

Main Methods:

Utilizing the recount2 Bioconductor package for data access and analysis.
Implementing R code for gene-level differential expression analysis.
Visualizing base-level genome coverage data.
Performing multi-feature level analyses.

Main Results:

The workflow details the computation of coverage count matrices in recount2.
It outlines methods for obtaining public metadata to aid downstream analyses.
Step-by-step instructions are provided for common bioinformatics tasks using recount2 data.

Conclusions:

The recount2 workflow enhances understanding of the resource's data.
It offers a compendium of R code for reproducible transcriptomic analyses.
Facilitates diverse analyses from differential expression to genome-wide coverage exploration.