Contributed by Shawn Higdon
RNA sequencing experiments are extremely powerful in terms of the amount of information that is capable of being revealed for a given experimental system. While performing these experiments demands passing through several hurdles in the lab that require a high degree of technical skill, careful planning, and seemingly flawless execution, analyzing the data flying off the DNA sequencer is equally complex. The course of my internship in the Weimer Lab revolves heavily around interpreting large files of RNAseq data, and one tool that has been making this analysis just a little bit sweeter is DESeq2 (http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html).
DESeq2 is an open source software suite written in R, distributed through Bioconductor (https://bioconductor.org/packages/release/bioc/html/DESeq2.html). As perhaps the most active primary developer, Michael Love has put forth extensive care in generating high-quality documentation for the use of DESeq2 for RNAseq data analysis. One of my favorite features of this package is the development of the companion package tximport, which is a package that provides versatility for Scientists who have taken different bioinformatic approaches to transcript quantification. Tximport allows R users to read in their transcript count information in a relatively straight forward manner that provides streamlined entry to DESeq2 for differential expression analysis. An excellent, in-depth vignette on how to use Tximportwas composed (https://bioconductor.org/packages/devel/bioc/vignettes/tximport/inst/doc/tximport.html), and it is also covered briefly in the DESeq2 vignette.
My last post to this blog described ultrafast transcript quantification using Salmon, and DESeq2 is the next logical step in moving forward with RNAseq data analysis in a timely fashion. Tximport provides an efficient bridge for getting Salmon output into R for DESeq2 analysis. In addition, determining differentially expressed genes with DESeq2 has been simplified to the point where only a few commands need to be performed in R. I must admit, when I knew nothing about how to use DESeq2, I had this idea in my mind that it was going to be extremely complicated and require extensive R coding. After having gone through the Vignette, my anxiety was laid to rest…at least on this front. Another great thing about DESeq2 is that the developers have provided integration with ggplot2, which makes plotting visualizations of the data a breeze! I plan to learn more and more about the many facets of RNAseq data analysis, and something tells me that using DESeq2 along the way is going to be extremely helpful…