transcriptomics

Tutorials covering transcriptomics

Material

You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides () and tutorial () buttons below.

Proteomics

Lesson Slides Hands-on Recordings Input dataset Workflows
Multiomics data analysis using MultiGSEA
multi-omics transcriptomics proteomics metabolomics

Single Cell

Lesson Slides Hands-on Recordings Input dataset Workflows
Bulk RNA Deconvolution with MuSiC
transcriptomics
Pseudobulk Analysis with Decoupler and EdgeR
transcriptomics pseudobulk
Comparing inferred cell compositions using MuSiC deconvolution
transcriptomics
Evaluating Reference Data for Bulk RNA Deconvolution
transcriptomics
Bulk matrix to ESet | Creating the bulk RNA-seq dataset for deconvolution
transcriptomics data management

Transcriptomics

Lesson Slides Hands-on Recordings Input dataset Workflows
1: RNA-Seq reads to counts
collections mouse QC
2: RNA-seq counts to genes
limma-voom mouse QC
3: RNA-seq genes to pathways
mouse
CLIP-Seq data analysis from pre-processing to motif detection
Differential abundance testing of small RNAs
GO Enrichment Analysis
Genome-wide alternative splicing analysis
alternative splicing isoform switching
Introduction to Transcriptomics
Network analysis with Heinz
metatranscriptomics network analysis
RNA Seq Counts to Viz in R
interactive-tools
RNA-RNA interactome data analysis
RNA-Seq analysis with AskOmics Interactive Tool
RNA-seq Alignment with STAR
Small Non-coding RNA Clustering using BlockClust
Visualization of RNA-Seq results with CummeRbund
Visualization of RNA-Seq results with Volcano Plot
Visualization of RNA-Seq results with Volcano Plot in R
interactive-tools
Visualization of RNA-Seq results with heatmap2
Whole transcriptome analysis of Arabidopsis thaliana
miRNA plants stress tolerance
Pathway analysis with the MINERVA Platform
bulk rna-seq viz cyoa
Reference-based RNA-Seq data analysis
bulk rna-seq collections drosophila QC cyoa deutsch español italiano
De novo transcriptome reconstruction with RNA-Seq

Contributors

This material was contributed to by:

Thorben Stehling avatar Thorben StehlingChao Zhang avatar Chao Zhangorcid logoAnthony Bretaudeau avatar Anthony BretaudeauXavier Garnier avatar Xavier GarnierPablo Moreno avatar Pablo MorenoNiall Beard avatar Niall Beardorcid logoJulia Jakiela avatar Julia Jakielaorcid logoCristóbal Gallardo avatar Cristóbal Gallardoorcid logoHelena Rasche avatar Helena RascheAnne Siegel avatar Anne SiegelAndrea Bagnacani avatar Andrea Bagnacaniorcid logoSaskia Hiltemann avatar Saskia HiltemannShian Su avatar Shian Suorcid logoFlorian Heyl avatar Florian Heylorcid logoClemens Blank avatar Clemens Blankorcid logoNicola Soranzo avatar Nicola SoranzoXi Liu avatar Xi LiuCharity Law avatar Charity LawJovana Maksimovic avatar Jovana MaksimovicBelinda Phipson avatar Belinda PhipsonMarkus Wolfien avatar Markus WolfienGildas Le Corguillé avatar Gildas Le Corguilléorcid logoMaria Doyle avatar Maria Doyleorcid logoBjörn Grüning avatar Björn GrüningCharlotte Adang avatar Charlotte Adangorcid logoToby Hodges avatar Toby Hodgesorcid logoDiana Chiang Jurado avatar Diana Chiang JuradoJames Taylor avatar James Taylororcid logoPeter van Heusden avatar Peter van Heusdenorcid logoMo Heydarian avatar Mo Heydarianorcid logoAmirhossein Naghsh Nilchi avatar Amirhossein Naghsh Nilchiorcid logoMorgan Howells avatar Morgan Howellsorcid logoMarisa Loach avatar Marisa LoachMateusz Kuzak avatar Mateusz Kuzakorcid logoGraeme Tyson avatar Graeme Tysonorcid logoMarek Ostaszewski avatar Marek Ostaszewskiorcid logoClea Siguret avatar Clea SiguretDaniel Maticzka avatar Daniel Maticzkaorcid logoMateo Boudet avatar Mateo Boudetorcid logoBérénice Batut avatar Bérénice Batutorcid logoBeatriz Serrano-Solano avatar Beatriz Serrano-SolanoCasper de Visser avatar Casper de Visserorcid logoAnika Erxleben avatar Anika Erxlebenorcid logoWendi Bacon avatar Wendi BaconMatthias Bernt avatar Matthias Berntorcid logoMatti Hoch avatar Matti Hochorcid logoLucille Delisle avatar Lucille Delisleorcid logoMallory Freeberg avatar Mallory FreebergMehmet Tekman avatar Mehmet TekmanCarlos Chee Mendonça avatar Carlos Chee Mendonçaorcid logoPavankumar Videm avatar Pavankumar VidemAnna Trigos avatar Anna TrigosOlivier Dameron avatar Olivier DameronEkaterina Polkh avatar Ekaterina Polkhorcid logoLinelle Abueg avatar Linelle AbuegJosé Manuel Domínguez avatar José Manuel Domínguezorcid logoAnne Fouilloux avatar Anne FouillouxWilliam Durand avatar William Durandorcid logoFotis E. Psomopoulos avatar Fotis E. PsomopoulosPeter-Bram 't Hoen avatar Peter-Bram 't Hoenorcid logoWolfgang Maier avatar Wolfgang MaierMarius van den Beek avatar Marius van den Beekorcid logoHans-Rudolf Hotz avatar Hans-Rudolf Hotzorcid logoMyrthe van Baardwijk avatar Myrthe van Baardwijkorcid logoIacopo Cristoferi avatar Iacopo Cristoferiorcid logoTeresa Müller avatar Teresa Müllerorcid logoArmin Dadras avatar Armin Dadrasorcid logoErwan Corre avatar Erwan CorreIGC Bioinformatics Unit avatar IGC Bioinformatics UnitSanjay Kumar Srikakulam avatar Sanjay Kumar SrikakulamMatt Ritchie avatar Matt Ritchieorcid logoMira Kuntz avatar Mira KuntzSimon Bray avatar Simon BrayAnton Nekrutenko avatar Anton Nekrutenkoorcid logoMartin Čech avatar Martin Čechorcid logoSofoklis Keisaris avatar Sofoklis KeisarisHarriet Dashnow avatar Harriet Dashnow

Funding

These individuals or organisations provided funding support for the development of this resource

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de.NBI
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DASH UK
This Fellowship was funded through the ELIXIR-UK DaSH project as part of the UKRI Innovation Scholars: Data Science Training in Health and Bioscience call (DaSH). (MR/V038966/1). The project aims to embed Research Data Management (RDM) know-how into UK universities and institutes by producing and delivering training in FAIR data stewardship using ELIXIR-UK knowledge and resources.
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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.
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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)