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
Contributors
This material was contributed to by:
Nicola Soranzo
Anna Trigos
Erwan Corre
Mehmet Tekman
Amirhossein Naghsh Nilchi
Andrea Bagnacani
Peter van Heusden
Jovana Maksimovic
Armin Dadras
Mo Heydarian
Marius van den Beek
Florian Heyl
Mira Kuntz
Shian Su
Teresa Müller
Clemens Blank
Matti Hoch
Saskia Hiltemann
Belinda Phipson
José Manuel Domínguez
Clea Siguret
Lucille Delisle
Ekaterina Polkh
Sofoklis Keisaris
Linelle Abueg
Björn Grüning
Chao Zhang
Graeme Tyson
Toby Hodges
Maria Doyle
Hans-Rudolf Hotz
Markus Wolfien
Harriet Dashnow
Bérénice Batut
Mallory Freeberg
Mateusz Kuzak
IGC Bioinformatics Unit
Pavankumar Videm
Cristóbal Gallardo
Anne Siegel
Anthony Bretaudeau
Gildas Le Corguillé
Charity Law
Beatriz Serrano-Solano
Martin Čech
Fotis E. Psomopoulos
Xi Liu
Matt Ritchie
Anika Erxleben
Daniel Maticzka
Wolfgang Maier
Iacopo Cristoferi
Sanjay Kumar Srikakulam
Anton Nekrutenko
Marek Ostaszewski
William Durand
Simon Bray
Olivier Dameron
Niall Beard
Myrthe van Baardwijk
James Taylor
Helena Rasche
Mateo Boudet
Xavier Garnier
Anne Fouilloux
Funding
These individuals or organisations provided funding support for the development of this resource
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.
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