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