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