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
- slides Slides: Quality Control
- tutorial Hands-on: Quality Control
- slides Slides: Mapping
- tutorial Hands-on: Mapping
Material
You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides (slides) and tutorial (tutorial) buttons below.Introduction
Start here if you are new to RNA-Seq analysis in Galaxy
| Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
|---|---|---|---|---|---|
|
Introduction to Transcriptomics
|
|||||
| Reference-based RNA-Seq data analysis | |||||
|
De novo transcriptome reconstruction with RNA-Seq
|
End-to-End Analysis
These tutorials take you from raw sequencing reads to pathway analysis
| Lesson | Slides | Hands-on | Recordings | Input dataset | Workflows |
|---|---|---|---|---|---|
| 1: RNA-Seq reads to counts | |||||
| 2: RNA-seq counts to genes | |||||
| 3: RNA-seq genes to pathways |
Visualisation
Tutorials covering data visualisation
Other
Assorted Tutorials
Frequently Asked Questions
Common questions regarding this topic have been collected on a dedicated FAQ page . Common questions related to specific tutorials can be accessed from the tutorials themselves.
Follow topic updates rss-feed with our RSS Feed
Community Resources
Community Home Maintainer HomeEditorial Board
This material is reviewed by our Editorial Board:
Bérénice Batut
Maria Doyle
Florian Heyl
Pavankumar Videm
Are you on the editorial board or want to help out with this topic? View the topic maintainer page for ways to improve this topic!
Contributors
This material was contributed to by:
Mateo Boudet
Sanjay Kumar Srikakulam
Maria Doyle
Mehmet Tekman
Nicola Soranzo
Erwan Corre
Anton Nekrutenko
Mo Heydarian
Hans-Rudolf Hotz
Shian Su
Mateusz Kuzak
Harriet Dashnow
Peter-Bram 't Hoen
Anthony Bretaudeau
Björn Grüning
Sofoklis Keisaris
Martin Čech
Casper de Visser
Gildas Le Corguillé
José Manuel Domínguez
Ekaterina Polkh
Simon Bray
Anne Fouilloux
Markus Wolfien
Marek Ostaszewski
Anika Erxleben
Amirhossein Naghsh Nilchi
Mira Kuntz
Xi Liu
Armin Dadras
Chao Zhang
Saskia Hiltemann
Clea Siguret
Bérénice Batut
Clemens Blank
Myrthe van Baardwijk
Charity Law
Fotis E. Psomopoulos
Graeme Tyson
Andrea Bagnacani
Matti Hoch
Peter van Heusden
Olivier Dameron
IGC Bioinformatics Unit
Jovana Maksimovic
Anna Trigos
Daniel Maticzka
Anne Siegel
Niall Beard
Helena Rasche
Matt Ritchie
Mallory Freeberg
Toby Hodges
William Durand
Xavier Garnier
Tristan Reynolds
Wolfgang Maier
Florian Heyl
James Taylor
Belinda Phipson
Teresa Müller
Linelle Abueg
Lucille Delisle
Iacopo Cristoferi
Pavankumar Videm
Marius van den Beek
Beatriz Serrano-Solano
Cristóbal Gallardo
Charlotte Adang
Funding
These individuals or organisations provided funding support for the development of this resource
The University of Melbourne
Gallantries
This project (2020-1-NL01-KA203-064717) is funded with the support of the Erasmus+ programme of the European Union. Their funding has supported a large number of tutorials within the GTN across a wide array of topics.
Australian BioCommons
de.NBI
Melbourne Bioinformatics
BeYond-COVID
BY-COVID is an EC funded project that tackles the data challenges that can hinder effective pandemic response.
This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement № 101046203 (BY-COVID)
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