What is it about?
In this paper we explain in detail the data from the recount2 project and how to use this free data with R. By reading this workflow you will learn how to access uniformly processed RNA sequencing data from over 70,000 human samples, thus saving you valuable time.
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Why is it important?
Processing RNA sequencing data takes time and computing resources. We processed all the publicly available human RNA-seq data so that others don't have to repeat this process. With recount2 and the companion recount Bioconductor/R package, everyone can freely download this data and immediately start working on downstream analyses. This is a huge time saver, removes an important bottleneck, and ultimately improves the usability of public human RNA-seq data.
Perspectives
While recount2 and the recount Bioconductor/R package are described elsewhere, this workflow takes the next step in detailing the R code you might use when analyzing this type of data. This involves using popular Bioconductor packages with non-default options that can take time to find. It also explains each step for quantifying gene expression with images that can be readily used in a class or tutorial setting.
Leonardo Collado Torres
Lieber Institute for Brain Development
While recount2 and the recount Bioconductor/R package are described elsewhere, this workflow takes the next step in detailing the R code you might use when analyzing this type of data. This involves using popular Bioconductor packages with non-default options that can take time to find. It also explains each step for quantifying gene expression with images that can be readily used in a class or tutorial setting.
Leonardo Collado Torres
Lieber Institute for Brain Development
Read the Original
This page is a summary of: recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor, F1000Research, August 2017, Faculty of 1000, Ltd.,
DOI: 10.12688/f1000research.12223.1.
You can read the full text:
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