Training material for all kinds of transcriptomics analysis.
Before diving into this topic, we recommend you to have a look at:
|Lesson||Hands-on||Slides||Input dataset||Galaxy tour|
|De novo transcriptome reconstruction with RNA-seq|
|Reference-based RNA-seq data analysis|
|Differential abundance testing of small RNAs|
This material is maintained by:
For any question related to this topic and the content, you can contact them.
- Shirley Pepke et al: Computation for ChIP-seq and RNA-seq studies
Paul L. Auer & R. W. Doerge: Statistical Design and Analysis of RNA Sequencing Data
Insights into proper planning of your RNA-seq run! To read before any RNA-seq experiment!
Ian Korf: Genomics: the state of the art in RNA-seq analysis
A refreshingly honest view on the non-trivial aspects of RNA-seq analysis
Marie-Agnès Dillies et al: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
Systematic comparison of seven representative normalization methods for the differential analysis of RNA-seq data (Total Count, Upper Quartile, Median (Med), DESeq, edgeR, Quantile and Reads Per Kilobase per Million mapped reads (RPKM) normalization)
Franck Rapaport et al: Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
Evaluation of methods for differential gene expression analysis
- Charlotte Soneson & Mauro Delorenzi: A comparison of methods for differential expression analysis of RNA-seq data
- Adam Roberts et al: Improving RNA-Seq expression estimates by correcting for fragment bias
Manuel Garber et al: Computational methods for transcriptome annotation and quantification using RNA-seq
Classical paper about the computational aspects of RNA-seq data analysis