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Version 3. F1000Res. 2016 Jun 17 [revised 2018 Dec 28];5. pii: ISCB Comm J-1408. doi: 10.12688/f1000research.9005.3. eCollection 2016.

RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR.

Author information

1
The Walter and Eliza Hall Institute of Medical Research, Parkville, 3052, Australia.
2
Department of Medical Biology, The University of Melbourne, Parkville, 3010, Australia.
3
CSL Limited, Parkville, Victoria, 3010, Australia.
4
School of Mathematics and Statistics, The University of Melbourne, Parkville, 3010, Australia.

Abstract

The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.

KEYWORDS:

RNA sequencing; data analysis; gene expression

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