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