Transcriptomics
Training material for all kinds of transcriptomics analysis.
You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides (slides) and tutorial (tutorial) buttons below.Requirements
Before diving into this topic, we recommend you to have a look at:
- Introduction to Galaxy Analyses
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Sequence analysis
- Quality Control: slides slides - tutorial hands-on
- Mapping: slides slides - tutorial hands-on
Material
Introduction
Start here if you are new to RNA-Seq analysis in GalaxyLesson | Slides | Hands-on | Recordings | Input dataset | Workflows | Galaxy servers |
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Introduction to Transcriptomics
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Reference-based RNA-Seq data analysis | tutorial Toggle Dropdown |
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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 analysisLesson | Slides | Hands-on | Recordings | Input dataset | Workflows | Galaxy servers |
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1: RNA-Seq reads to counts | tutorial Toggle Dropdown | instances | ||||
2: RNA-seq counts to genes | tutorial Toggle Dropdown | instances | ||||
3: RNA-seq genes to pathways | tutorial Toggle Dropdown | instances |
Single-cell RNA-seq
Tutorials about analysis of single-cell RNA-seq dataSingle-cell RNA-seq: Case Study
Tutorials using a single published single-cell RNA-seq dataset for a variety of analysesVisualisation
Tutorials covering data visualisationOther
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.Maintainers
This material is maintained by:
Bérénice Batut Maria Doyle Florian Heyl Wendi BaconFor any question related to this topic and the content, you can contact them or visit our Gitter channel.
Contributors
This material was contributed to by:
Maria Doyle Belinda Phipson Harriet Dashnow Jovana Maksimovic Anna Trigos Matt Ritchie Shian Su Charity Law Florian Heyl Daniel Maticzka Bérénice Batut Anthony Bretaudeau Gildas Le Corguillé Erwan Corre Xi Liu Mallory Freeberg IGC Bioinformatics Unit Chao (Cico) Zhang Anton Nekrutenko Fotis E. Psomopoulos Toby Hodges Erasmus+ Programme Pavankumar Videm Xavier Garnier Anne Siegel Olivier Dameron Mateo Boudet Andrea Bagnacani Cristóbal Gallardo Beatriz Serrano-Solano Anika Erxleben Markus Wolfien Mehmet Tekman Wolfgang Maier Mo Heydarian Clemens Blank Nicola Soranzo Peter van Heusden Lucille Delisle Wendi Bacon Alex Ostrovsky Graham Etherington Hans-Rudolf Hotz Daniel BlankenbergReferences
- Shirley Pepke et al: Computation for ChIP-seq and RNA-seq studies
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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
- Tekman, Mehmet and Batut, Bérénice; Ostrovsky, Alexander; Antoniewski, Christophe; Clements, Dave; Ramirez, Fidel; Etherington, Graham J; Hotz, Hans-Rudolf; Scholtalbers, Jelle; Manning, Jonathan R; Bellenger, Lea; Doyle, Maria A; Heydarian, Mohammad; Huang, Ni; Soranzo, Nicola; Moreno, Pablo; Mautner, Stefan; Papatheodorou, Irene; Nekrutenko, Anton; Taylor, James; Blankenberg, Daniel; Backofen, Rolf; Grüning, Björn;: A single-cell RNA-sequencing training and analysis suite using the Galaxy framework