RNA-Seq data analysis
RNA-sequencing is a method used to reveal the presence and quantity of RNA in a biological sample at a given moment in time. Here, we propose some materials to learn how to analyze RNA-seq data.
Slides
A deck of slides is available for this topic:
Tutorials
A tutorial with hands-on is available for this topic:
Input datasets
For de novo tutorial, data is available at Zenodo.
For ref-based tutorial, the original data is available at NCBI Gene Expression Omnibus (GEO) under accession number GSE18508. We will look at the 7 first samples (3 treated samples with Pasilla (PS) gene depletion: GSM461179, GSM461180, GSM461181 and 4 untreated samples: GSM461176, GSM461177, GSM461178, GSM461182).
Galaxy instance
For these tutorials, you can use the dedicated Docker image:
docker run -d -p 8080:80 bgruening/galaxy-rna-seq-training
It will launch a flavored Galaxy instance available on http://localhost:8080 .
References
Papers
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 DOI: 10.1534/genetics.110.114983
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
Websites
Stephen Turner: RNA-seq Workflows and Tools
Nice graphical overview of the RNA-seq processing and analysis step
Contributors
This material is maintained by:
- Maintainer 1
- Maintainer 2
For any question related to this topic and the content, you can contact them.
The following individuals have contributed to this training material:
- Name 1
- Name 2