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