An end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages

Here the authors walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. They start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. They perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.

Bioconductor has many packages which support analysis of high-throughput sequence data, including RNA sequencing (RNA-seq). The packages which we will use in this workflow include core packages maintained by the Bioconductor core team for importing and processing raw sequencing data and loading gene annotations. We will also use contributed packages for statistical analysis and visualization of sequencing data. Through scheduled releases every 6 months, the Bioconductor project ensures that all the packages within a release will work together in harmony (hence the “conductor” metaphor). The packages used in this workflow are loaded with the library function and can be installed by following the Bioconductor package installation instructions.

If you have questions about this workflow or any Bioconductor software, please post these to the Bioconductor support site. If the questions concern a specific package, you can tag the post with the name of the package, or for general questions about the workflow, tag the post with rnaseqgene. Note the posting guide for crafting an optimal question for the support site.

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Love MI, Anders S, Kim V, Huber W. (2015) RNA-Seq workflow: gene-level exploratory analysis and differential expression. F1000Res 4:1070. [article]

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