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 |
|---|---|---|---|---|---|
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Introduction to Transcriptomics
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| Reference-based RNA-Seq data analysis | |||||
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De novo transcriptome reconstruction with RNA-Seq
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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.
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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:
Cristóbal Gallardo
Toby Hodges
Hans-Rudolf Hotz
Wolfgang Maier
Florian Heyl
Pavankumar Videm
Simon Bray
Armin Dadras
Lucille Delisle
Andrea Bagnacani
Mallory Freeberg
Saskia Hiltemann
Matt Ritchie
Erwan Corre
Sanjay Kumar Srikakulam
Marius van den Beek
Martin Čech
Daniel Maticzka
Harriet Dashnow
Fotis E. Psomopoulos
Linelle Abueg
Mo Heydarian
Teresa Müller
Olivier Dameron
Tristan Reynolds
Graeme Tyson
Anne Siegel
Nicola Soranzo
Jovana Maksimovic
Mateo Boudet
Markus Wolfien
Ekaterina Polkh
Anthony Bretaudeau
Clemens Blank
José Manuel Domínguez
Iacopo Cristoferi
William Durand
Matti Hoch
Mehmet Tekman
Peter-Bram 't Hoen
Anika Erxleben
Beatriz Serrano-Solano
Sofoklis Keisaris
Myrthe van Baardwijk
Peter van Heusden
Shian Su
Maria Doyle
Xavier Garnier
Clea Siguret
Casper de Visser
IGC Bioinformatics Unit
Anton Nekrutenko
Chao Zhang
Anne Fouilloux
Marek Ostaszewski
Amirhossein Naghsh Nilchi
Mira Kuntz
Björn Grüning
Anna Trigos
Belinda Phipson
Charlotte Adang
Charity Law
Xi Liu
Helena Rasche
Gildas Le Corguillé
Niall Beard
Bérénice Batut
James Taylor
Mateusz Kuzak
Funding
These individuals or organisations provided funding support for the development of this resource
Melbourne Bioinformatics
de.NBI
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)
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.
The University of Melbourne
Australian BioCommons
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