Transcriptomics

Training material for all kinds of transcriptomics analysis.

Requirements

Before diving into this topic, we recommend you to have a look at:

Introduction to Galaxy Analyses
Sequence analysis
- Quality Control: slides slides - tutorial hands-on
- Mapping: slides slides - tutorial hands-on

Material

Lesson	Slides	Hands-on	Input dataset	Workflows	Galaxy tour
Introduction to Transcriptomics	slides
CLIP-Seq data analysis from pre-processing to motif detection		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
De novo transcriptome reconstruction with RNA-Seq		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow	interactive_tour
Differential abundance testing of small RNAs		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow	interactive_tour
GO Enrichment Analysis		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link
Network analysis with Heinz metatranscriptomics network analysis	slides	tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow	interactive_tour
Reference-based RNA-Seq data analysis		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow	interactive_tour
Small Non-coding RNA Clustering using BlockClust		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
Visualization of RNA-Seq results with CummeRbund	slides	tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow	interactive_tour
Visualization of RNA-Seq results with Volcano Plot		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link
Visualization of RNA-Seq results with heatmap2		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow

Galaxy instances

You can use a public Galaxy instance which has been tested for the availability of the used tools. They are listed along with the tutorials above.

Maintainers

This material is maintained by:

For any question related to this topic and the content, you can contact them or visit our Gitter channel.

Contributors

This material was contributed to by:

References

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
Cole Trapnell et al: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
Zhong Wang et al: RNA-Seq: a revolutionary tool for transcriptomics
Dittrich, M. T. and Klau, G. W. and Rosenwald, A. and Dandekar, T. and Muller, T.: Identifying functional modules in protein-protein interaction networks: an integrated exact approach
May, Ali; Brandt, Bernd W; El-Kebir, Mohammed; Klau, Gunnar W; Zaura, Egija; Crielaard, Wim; Heringa, Jaap; Abeln, Sanne: metaModules identifies key functional subnetworks in microbiome-related disease
Pavankumar, Videm; Dominic, Rose; Fabrizio, Costa; Rolf, Backofen: BlockClust: efficient clustering and classification of non-coding RNAs from short read RNA-seq profiles