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 | zenodo_link | workflow | interactive_tour | |
Clustering 3K PBMCs with Scanpy
single-cell
10x
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
De novo transcriptome reconstruction with RNA-Seq
|
tutorial Toggle Dropdown | zenodo_link | workflow | interactive_tour | |
Differential abundance testing of small RNAs
|
tutorial Toggle Dropdown | zenodo_link | workflow | interactive_tour | |
Downstream Single-cell RNA analysis with RaceID
single-cell
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
GO Enrichment Analysis
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
Network analysis with Heinz
metatranscriptomics
network analysis
|
slides | tutorial Toggle Dropdown | zenodo_link | workflow | interactive_tour |
Plates, Batches, and Barcodes
single-cell
|
slides | ||||
Pre-processing of 10X Single-Cell RNA Datasets
single-cell
10x
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
Pre-processing of Single-Cell RNA Data
single-cell
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
RNA Seq Counts to Viz in R
|
tutorial Toggle Dropdown | zenodo_link | |||
RNA-Seq reads to counts
collections
mouse
QC
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
RNA-seq counts to genes
limma-voom
mouse
QC
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
RNA-seq genes to pathways
mouse
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
Reference-based RNA-Seq data analysis
bulk
rna-seq
|
tutorial Toggle Dropdown | zenodo_link | workflow | interactive_tour | |
Single-cell quality control with scater
single-cell
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
Small Non-coding RNA Clustering using BlockClust
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
Understanding Barcodes
single-cell
|
tutorial Toggle Dropdown | zenodo_link | |||
Visualization of RNA-Seq results with CummeRbund
|
slides | tutorial Toggle Dropdown | zenodo_link | interactive_tour | |
Visualization of RNA-Seq results with Volcano Plot
|
tutorial Toggle Dropdown | zenodo_link | workflow | ||
Visualization of RNA-Seq results with heatmap2
|
tutorial Toggle Dropdown | 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.
You can also use the following Docker image for these tutorials:
docker run -p 8080:80 quay.io/galaxy/transcriptomics-training
NOTE: Use the -d flag at the end of the command if you want to automatically download all the data-libraries into the container.
It will launch a flavored Galaxy instance available on http://localhost:8080. This instance will contain all the tools and workflows to follow the tutorials in this topic. Login as admin with password admin to access everything.
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