Papers and Online Tutorials on RNAseq Data Analysis

Papers and Online Tutorials on RNAseq Data Analysis


Over the years, I made many google searches to find high-quality online tutorials on analysis of RNAseq data. Posting my list (with comments) will likely save some time for others making the same journey.

Two Review Papers

I will make your life simple by listing only two papers.

(i) J. Costa-Silva et al.

This recent paper made a detailed comparison of all statistical analysis techniques based on actual qRT-PCR measurements. They concluded that NOIseq, limma+voom and DESeq2 were the “most balanced softwares by considering the precision, accuracy and sensitivity”. Figure 1 of the paper provides a nice review of various tools being used in different stages of RNAseq data analysis.

I have not implemented NOIseq in rnaseq.work yet and plan to do that soon.

(ii) Ana Conseca et al.

I call this 2016 paper the Walmart of RNA-seq analysis methods. It has a little bit of everything, which is also why the paper gets cited a lot.

Online Tutorials

Here I mention seven detailed online tutorials. You can download their data and then try to reproduce the analysis steps using R. The only disadvantage is that they highlight well-established methods published 6-7 years back. If you are looking for help on efficient methods published in the last 2-3 years (e.g. Kallisto, Salmon), you need to consult the “owner’s manual” or join our class.

(i) Annick Moisan, Ignacio Gonzales, Nathalie Villa-Vialaneix

This tutorial is strong on statistical analysis. You can also download the well-written pdf tutorial from 2015.

(ii) Friederike Dündar, Luce Skrabanek, Paul Zumbo

This is one of the few bioinformatics tutorials with detailed description of the experimental steps.

(iii) Thomas Girke, Rakesh Kaundal (UC Riverside)

The tutorials of Thomas Girke at UC Riverside are excellent. His old pdf-based slides on RNAseq data analysis are here.

Additionally, Rakesh Kaundal from UC Riverside has this detailed tutorial.

(iv) Mike Love

Mike Love, who was the primary author of DESeq2, has an excellent tutorial at bioconductor. Also check the vignette.

(v) Stanford Bios221

This group posted a number of detailed tutorials on various Bioconductor-related topics, including one on RNAseq analysis using edgeR.

(vi) University of Cambridge

Here is another excellent online tutorial.

(vii) List of All Analysis Steps including Biological Analysis

Weijun Luo, the author of GAGE and pathview, posted a brief summary of RNAseq workflow including biological analysis.


Written by M. //

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