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	interactive_tour
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
Downstream Single-cell RNA analysis with RaceID single-cell		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
GO Enrichment Analysis		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
Network analysis with Heinz metatranscriptomics network analysis	slides	tutorial Toggle Dropdown FR JA ES PT AR	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 FR JA ES PT AR	zenodo_link	workflow
Pre-processing of Single-Cell RNA Data single-cell		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
RNA-Seq reads to counts collections mouse QC		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
RNA-seq counts to genes limma-voom mouse QC		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
RNA-seq genes to pathways mouse		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
Reference-based RNA-Seq data analysis bulk rna-seq		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow	interactive_tour
Single-cell quality control with scater single-cell		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
Small Non-coding RNA Clustering using BlockClust		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
Understanding Barcodes single-cell		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link
Visualization of RNA-Seq results with CummeRbund	slides	tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link		interactive_tour
Visualization of RNA-Seq results with Volcano Plot		tutorial Toggle Dropdown FR JA ES PT AR	zenodo_link	workflow
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:

Bérénice Batut,

Maria Doyle

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:

Bérénice Batut,

Anika Erxleben,

Markus Wolfien,

Daniel Maticzka

Daniel Maticzka,

Mallory Freeberg

Mallory Freeberg,

Alex Ostrovsky,

IGC Bioinformatics Unit

IGC Bioinformatics Unit,

Chao (Cico) Zhang

Chao (Cico) Zhang,

Hans-Rudolf Hotz

Hans-Rudolf Hotz,

Daniel Blankenberg

Daniel Blankenberg,

Wolfgang Maier,

Belinda Phipson

Belinda Phipson,

Harriet Dashnow

Harriet Dashnow,

Jovana Maksimovic

Jovana Maksimovic,

Pavankumar Videm

Pavankumar Videm,

Nicola Soranzo,

Peter van Heusden

Peter van Heusden,

Graham Etherington

Graham Etherington,

Andrea Bagnacani

Andrea Bagnacani

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