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

Introduction

Start here if you are new to RNA-Seq analysis in Galaxy

End-to-End Analysis

These tutorials take you from raw sequencing reads to pathway analysis

Visualisation

Tutorials covering data visualisation

Other

Assorted Tutorials

Galaxy instances

You can use a public Galaxy instance which has been tested for the availability of the used tools. They are listed along with the tutorials above.

You can also use the following Docker image for these tutorials:

docker run -p 8080:80 quay.io/galaxy/transcriptomics-training

NOTE: Use the -d flag at the end of the command if you want to automatically download all the data-libraries into the container.

It will launch a flavored Galaxy instance available on http://localhost:8080. This instance will contain all the tools and workflows to follow the tutorials in this topic. Login as admin with password password to access everything.

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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Editorial Board

This material is reviewed by our Editorial Board:

Bérénice Batut

Maria Doyle

Florian Heyl

Contributors

This material was contributed to by:

Matt Ritchie

Iacopo Cristoferi

Anton Nekrutenko

Xi Liu

Saskia Hiltemann

Shian Su

Chao Zhang

Anna Trigos

Mallory Freeberg

IGC Bioinformatics Unit

Fotis E. Psomopoulos

Peter van Heusden

Pavankumar Videm

Andrea Bagnacani

Olivier Dameron

Björn Grüning

Anika Erxleben

Helena Rasche

Belinda Phipson

Sanjay Kumar Srikakulam

Clemens Blank

Florian Heyl

Matti Hoch

Sofoklis Keisaris

Charity Law

Xavier Garnier

Toby Hodges

The Carpentries

Mo Heydarian

Daniel Maticzka

Harriet Dashnow

Myrthe van Baardwijk

Lucille Delisle

Markus Wolfien

Cristóbal Gallardo

Maria Doyle

Jovana Maksimovic

Mateo Boudet

Marek Ostaszewski

Clea Siguret

José Manuel Domínguez

Gildas Le Corguillé

Erwan Corre

Anthony Bretaudeau

James Taylor

Bérénice Batut

Mira Kuntz

Beatriz Serrano-Solano

Anne Siegel

Nicola Soranzo

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.

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)

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