Transcriptomics

Jekyll 2026-03-30T10:09:15+00:00 https://training.galaxyproject.org/training-material/topics/transcriptomics/feed.xml Transcriptomics Recently added tutorials, slides, FAQs, and events in the transcriptomics topic https://training.galaxyproject.org/training-material/assets/images/GTN-60px.png 📚 Análisis de datos RNA-Seq basados en referencias https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial_ES.html 2026-03-30T10:09:15+00:00

En los últimos años, la secuenciación del ARN (abreviada RNA-Seq) se ha convertido en una tecnología muy utilizada para analizar el transcriptoma celular en continuo cambio, es decir, el conjunto de todas las moléculas de ARN de una célula o de una población de células. Uno de los objetivos más comunes de RNA-Seq es el perfilado de la expresión génica mediante la identificación de genes o rutas moleculares que se expresan de forma diferencial (DE) entre dos o más condiciones biológicas. Este tutorial muestra un flujo de trabajo computacional para la detección de genes y rutas de expresión diferencial a partir de datos de ARN-Seq, proporcionando un análisis completo de un experimento de ARN-Seq en células de Drosophila tras la eliminación de un gen regulador.

Negli ultimi anni, il sequenziamento dell’RNA (RNA-Seq) è diventata una tecnologia ampiamente utilizzata per analizzare il trascrittoma cellulare in continua evoluzione, ovvero l’insieme di tutte le molecole di RNA presenti in una cellula o in una popolazione di cellule. Uno degli obiettivi più comuni dell’RNA-Seq è la profilazione dell’espressione genica, ossia l’identificazione dei geni o delle vie molecolari che risultano differenzialmente espressi (DE) tra due o più condizioni biologiche. Questo tutorial illustra un flusso di lavoro computazionale per l’individuazione di geni e percorsi DE a partire da dati di RNA-Seq, fornendo un’analisi completa di un esperimento RNA-Seq che profila cellule di Drosophila dopo la deplezione di un gene regolatore.

In den letzten Jahren hat sich die RNA-Sequenzierung (kurz RNA-Seq) zu einer weit verbreiteten Technologie entwickelt, um das sich ständig verändernde zelluläre Transkriptom zu analysieren, d. h. die Menge aller RNA-Moleküle in einer Zelle oder einer Zellpopulation. Eines der häufigsten Ziele von RNA-Seq ist die Erstellung von Profilen der Genexpression durch die Identifizierung von Genen oder molekularen Pfaden, die zwischen zwei oder mehreren biologischen Bedingungen unterschiedlich exprimiert werden. Dieses Tutorial demonstriert einen computergestützten Arbeitsablauf für die Erkennung von diffrentiell exprimierten Genen und -Pfaden aus RNA-Seq-Daten, indem es eine vollständige Analyse eines RNA-Seq-Experiments vorstellt, bei dem Drosophila-Zellen nach der Deletion eines regulatorischen Gens profiliert wurden.

This course introduces scientists to the data analysis platform Galaxy. The course is designed for beginners; no prior programming skills are required.

Daniela Schneider https://training.galaxyproject.org/training-material/hall-of-fame/Sch-Da/ Diana Chiang Jurado https://training.galaxyproject.org/training-material/hall-of-fame/dianichj/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Amirhossein Naghsh Nilchi https://training.galaxyproject.org/training-material/hall-of-fame/Nilchia/ Wolfgang Maier https://training.galaxyproject.org/training-material/hall-of-fame/wm75/ Paul Zierep https://training.galaxyproject.org/training-material/hall-of-fame/paulzierep/ 📅 From data to discovery - Galaxy workshop at University of Graz https://training.galaxyproject.org/training-material/events/2025-09-24-wokshop-graz.html 2025-09-23T15:06:50+00:00

This course introduces scientists to the data analysis platform Galaxy. The course is an intermediate course; there is no requirement of any programming skills.

Sebastian Preissl https://training.galaxyproject.org/training-material/hall-of-fame/sebastian-preissl/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Amirhossein Naghsh Nilchi https://training.galaxyproject.org/training-material/hall-of-fame/Nilchia/ 🛠️ Workflow constructed from history 'Computer assignment RNA seq analysis and visualisation -BMW2 - attempt 3' https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-analysis-clustering-viz/workflows/RNA_seq_analysis_and_visualisation.html 2025-05-28T12:51:29+00:00

🎥 Recording of Pseudobulk Analysis with Decoupler and EdgeR https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/pseudobulk-analysis/recordings/#tutorial-recording-2-may-2025 2025-05-02T00:00:00+00:00

A 50M long recording is now available.

Diana Chiang Jurado https://training.galaxyproject.org/training-material/hall-of-fame/dianichj/ 📅 From Data to Discovery: Metagenomics, RNA-Seq - NGS Bioinformatics with Galaxy https://training.galaxyproject.org/training-material/events/2025-06-30-hts-workshop-freiburg.html 2025-03-26T13:14:41+00:00

This course introduces scientists to the data analysis platform Galaxy. The course is a beginner course; no programming skills are required.

Daniela Schneider https://training.galaxyproject.org/training-material/hall-of-fame/Sch-Da/ Teresa Müller https://training.galaxyproject.org/training-material/hall-of-fame/teresa-m/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Diana Chiang Jurado https://training.galaxyproject.org/training-material/hall-of-fame/dianichj/ Engy Nasr https://training.galaxyproject.org/training-material/hall-of-fame/EngyNasr/ Paul Zierep https://training.galaxyproject.org/training-material/hall-of-fame/paulzierep/ Mina Hojat Ansari https://training.galaxyproject.org/training-material/hall-of-fame/minamehr/ 📚 Multiomics data analysis using MultiGSEA https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.html 2025-03-10T15:22:38+00:00

The multiGSEA package was designed to run a robust GSEA-based pathway enrichment for multiple omics layers (Canzler and Hackermüller, 2020) Canzler and Hackermüller 2020. The enrichment is calculated for each omics layer separately and aggregated p-values are calculated afterwards to derive a composite multi-omics pathway enrichment.

Thorben Stehling https://training.galaxyproject.org/training-material/hall-of-fame/tStehling/ Matthias Bernt https://training.galaxyproject.org/training-material/hall-of-fame/bernt-matthias/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ 📚 Pseudobulk Analysis with Decoupler and EdgeR https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/pseudobulk-analysis/tutorial.html 2025-02-12T16:29:15+00:00

Pseudobulk analysis is a powerful technique that bridges the gap between single-cell and bulk RNA-seq data. It involves aggregating gene expression data from groups of cells within the same biological replicate, such as a mouse or patient, typically based on clustering or cell type annotations (Murphy and Skene 2022).

Diana Chiang Jurado https://training.galaxyproject.org/training-material/hall-of-fame/dianichj/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Diana Chiang Jurado https://training.galaxyproject.org/training-material/hall-of-fame/dianichj/ 🛠️ Music: Pre-grouping cell types https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/bulk-music/workflows/music_pre-grouping_cell_types.html 2025-02-10T14:23:01+00:00

Workflow for the second half of the "Bulk RNA Deconvolution with MuSiC" tutorial. Implements the "Estimation of cell type proportions with pre-grouping of cell types" section

Morgan Howells https://training.galaxyproject.org/training-material/hall-of-fame/hexhowells/ 📚 Evaluating Reference Data for Bulk RNA Deconvolution https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.html 2025-02-02T21:13:57+00:00

There are various methods to estimate the proportions of cell types in bulk RNA data. Since the actual cell proportions of the data are unknown, how do we know if our tools are producing accurate results?

Morgan Howells https://training.galaxyproject.org/training-material/hall-of-fame/hexhowells/ Carlos Chee Mendonça https://training.galaxyproject.org/training-material/hall-of-fame/carloscheemendonca/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Morgan Howells https://training.galaxyproject.org/training-material/hall-of-fame/hexhowells/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ 🛠️ scRNA Plant Analysis https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-plant/workflows/scRNA-Plant-Analysis.html 2024-12-13T18:54:19+00:00

Downstream Single-cell RNA Plant analysis with ScanPy

Mehmet Tekman Beatriz Serrano-Solano https://training.galaxyproject.org/training-material/hall-of-fame/beatrizserrano/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ 📅 Workshop on high-throughput sequencing data analysis with Galaxy https://training.galaxyproject.org/training-material/events/2025-03-10-hts-workshop-freiburg.html 2024-10-15T12:38:08+00:00

This course introduces scientists to the data analysis platform Galaxy. The course is a beginner course; no programming skills are required.

Daniela Schneider https://training.galaxyproject.org/training-material/hall-of-fame/Sch-Da/ Teresa Müller https://training.galaxyproject.org/training-material/hall-of-fame/teresa-m/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Amirhossein Naghsh Nilchi https://training.galaxyproject.org/training-material/hall-of-fame/Nilchia/ Wolfgang Maier https://training.galaxyproject.org/training-material/hall-of-fame/wm75/ Engy Nasr https://training.galaxyproject.org/training-material/hall-of-fame/EngyNasr/ Paul Zierep https://training.galaxyproject.org/training-material/hall-of-fame/paulzierep/ 🎥 Recording of Visualization of RNA-Seq results with Volcano Plot https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-volcanoplot/recordings/#tutorial-recording-6-september-2024 2024-09-06T00:00:00+00:00

A 13M long recording is now available.

Saim Momin https://training.galaxyproject.org/training-material/hall-of-fame/SaimMomin12/ 📅 Workshop on high-throughput sequencing data analysis with Galaxy https://training.galaxyproject.org/training-material/events/2024-07-22-freiburg-july.html 2024-07-10T17:01:39+00:00

This course introduces scientists to the data analysis platform Galaxy. The course is a beginner course; no programming skills are required.

Anika Erxleben https://training.galaxyproject.org/training-material/hall-of-fame/erxleben/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Amirhossein Naghsh Nilchi https://training.galaxyproject.org/training-material/hall-of-fame/Nilchia/ Teresa Müller https://training.galaxyproject.org/training-material/hall-of-fame/teresa-m/ Wolfgang Maier https://training.galaxyproject.org/training-material/hall-of-fame/wm75/ Engy Nasr https://training.galaxyproject.org/training-material/hall-of-fame/EngyNasr/ Paul Zierep https://training.galaxyproject.org/training-material/hall-of-fame/paulzierep/ 📅 Bioconductor Carpentries workshop: Analysis and Interpretation of Bulk RNA-Seq Data https://training.galaxyproject.org/training-material/events/2024-07-22-bioconductor-carpentries-workshop-analysis-and-interpretation-of-bulk-rna-seq-data.html 2024-07-09T10:00:21+00:00

Join the Bioconductor Carpentries RNA-Seq Workshop on July 22-23, 2024, at the Van Andel Institute. Ideal for biologists and researchers with some R experience, this hands-on workshop offers expert training in RNA-seq analysis, practical skills enhancement, and networking opportunities.

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ 📅 A practical introduction to bioinformatics and RNA-seq using Galaxy https://training.galaxyproject.org/training-material/events/2024-09-10-biont-rnaseq.html 2024-07-03T08:50:25+00:00

Join us for an engaging 4-half day online workshop and dive into the captivating world of RNA-seq data analysis using Galaxy! This hands-on workshop will equip you with the skills to effectively analyze RNA-seq data from start to finish. You will learn about: Galaxy, Quality Control, Mapping and Quantification and Downstream Analysis. Don't miss this opportunity to enhance your bioinformatics skills.

Teresa Müller https://training.galaxyproject.org/training-material/hall-of-fame/teresa-m/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ 🛠️ BY-COVID: Data Download https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/minerva-pathways/workflows/Galaxy-Workflow-BY-COVID__Data_Download.html 2024-06-11T12:50:30+00:00

Downloads data from NCBI assuming the correct data input format. (I.e. it should be two columns, with a header, Run and Group, where Run is an SRR identifier.)

Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ 🛠️ mRNA-Seq BY-COVID Pipeline: Counts https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/minerva-pathways/workflows/Galaxy-Workflow-mRNA-Seq_BY-COVID_Pipeline__Counts.html 2024-03-28T21:41:17+00:00

This portion of the workflow produces sets of feature Counts ready for analysis by limma/etc.

Iacopo Cristoferi https://training.galaxyproject.org/training-material/hall-of-fame/PapXis/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Clinical Bioinformatics Unit, Pathology Department, Eramus Medical Center 🛠️ mRNA-Seq BY-COVID Pipeline: Analysis https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/minerva-pathways/workflows/Galaxy-Workflow-mRNA-Seq_BY-COVID_Pipeline__Analysis.html 2024-03-28T21:41:17+00:00

Analyse Bulk RNA-Seq data in preparation for downstream Pathways analysis with MINERVA

Iacopo Cristoferi https://training.galaxyproject.org/training-material/hall-of-fame/PapXis/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Clinical Bioinformatics Unit, Pathology Department, Eramus Medical Center 📚 Pathway analysis with the MINERVA Platform https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/minerva-pathways/tutorial.html 2024-03-28T21:41:17+00:00

This tutorial is a partial reproduction of Togami et al. 2022 wherein they evaluated mRNA and miRNA in a selection of COVID-19 patients and healthy controls.

Marek Ostaszewski https://training.galaxyproject.org/training-material/hall-of-fame/mjostaszewski/ Matti Hoch https://training.galaxyproject.org/training-material/hall-of-fame/MattiHoch/ Iacopo Cristoferi https://training.galaxyproject.org/training-material/hall-of-fame/PapXis/ Myrthe van Baardwijk https://training.galaxyproject.org/training-material/hall-of-fame/mbaardwijk/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Matti Hoch https://training.galaxyproject.org/training-material/hall-of-fame/MattiHoch/ Mira Kuntz https://training.galaxyproject.org/training-material/hall-of-fame/mira-miracoli/ José Manuel Domínguez https://training.galaxyproject.org/training-material/hall-of-fame/kysrpex/ Sanjay Kumar Srikakulam https://training.galaxyproject.org/training-material/hall-of-fame/sanjaysrikakulam/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Linelle Abueg https://training.galaxyproject.org/training-material/hall-of-fame/abueg/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ 📰 Learn to use MINERVA Platform's COVID-19 Disease Map with Galaxy https://training.galaxyproject.org/training-material/news/2024/03/28/by-covid-pathways.html 2024-03-28T00:00:00+00:00

As part of the work of BeYond COVID, we have developed a new integration between the MINERVA Platform’s COVID-19 Disease Map and Galaxy! Datasets created within Galaxy can now be seamlessly visualized in the MINERVA Platform, allowing you to explore your data in the context of the COVID-19 Disease Map.

Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Marek Ostaszewski https://training.galaxyproject.org/training-material/hall-of-fame/mjostaszewski/ Matti Hoch https://training.galaxyproject.org/training-material/hall-of-fame/MattiHoch/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ 🖼️ Identification of non-canonical ORFs and their potential biological function https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/differential-isoform-expression/slides.html 2023-07-11T10:15:34+00:00

Index of contents

Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Wolfgang Maier https://training.galaxyproject.org/training-material/hall-of-fame/wm75/ 🛠️ Filter, Plot and Explore Single-cell RNA-seq Data updated https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_basic-pipeline/workflows/Filter,-Plot-and-Explore-Single-cell-RNA-seq-Data-updated.html 2023-06-13T11:38:52+00:00

Filter, Plot and Explore Single-cell RNA-seq Data

Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ 🛠️ GTN - Preprocessing of 10X scRNA-seq data https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-preprocessing-tenx/workflows/scRNA-seq-Preprocessing-TenX.html 2023-05-19T11:33:23+00:00

Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Mehmet Tekman Hans-Rudolf Hotz https://training.galaxyproject.org/training-material/hall-of-fame/hrhotz/ Daniel Blankenberg https://training.galaxyproject.org/training-material/hall-of-fame/blankenberg/ 🛠️ GTN_differential_isoform_expression https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/differential-isoform-expression/workflows/main_workflow.html 2023-05-17T16:20:54+00:00

Analyze differential isoform expression

Discovered over 40 years ago, alternative splicing (AS) formed a large part of the puzzle explaining how proteomic complexity can be achieved with a limited set of genes (Alt 1980). The majority of eukaryote genes have multiple transcriptional isoforms, and recent data indicate that each transcript of protein-coding genes contain 11 exons and produce 5.4 mRNAs on average (Piovesan et al. 2016). In humans, approximately 95% of multi-exon genes show evidence of AS and approximately 60% of genes have at least one alternative transcription start site, some of which exert antagonistic functions (Carninci et al. 2006, Miura et al. 2012). Its regulation is essential for providing cells and tissues their specific features, and for their response to environmental changes (Wang et al. 2008, Kalsotra and Cooper 2011).

Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Wolfgang Maier https://training.galaxyproject.org/training-material/hall-of-fame/wm75/ 📚 RNA-seq Alignment with STAR https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-bash-star-align/tutorial.html 2023-05-15T12:12:40+00:00

In recent years, RNA sequencing (in short RNA-Seq) has become a very widely used technology to analyze the continuously changing cellular transcriptome, i.e. the set of all RNA molecules in one cell or a population of cells. One of the most common aims of RNA-Seq is the profiling of gene expression by identifying genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This tutorial demonstrates a computational workflow for counting and locating the genes in RNA sequences. The first and most critical step in an RNA-seq analysis.

Sofoklis Keisaris https://training.galaxyproject.org/training-material/hall-of-fame/Sofokli5/ Fotis E. Psomopoulos https://training.galaxyproject.org/training-material/hall-of-fame/fpsom/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ The Carpentries https://training.galaxyproject.org/training-material/hall-of-fame/carpentries/ Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ Sofoklis Keisaris https://training.galaxyproject.org/training-material/hall-of-fame/Sofokli5/ Anthony Bretaudeau https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/ 📰 New Galaxy training: Genome-wide alternative splicing analysis https://training.galaxyproject.org/training-material/news/2023/05/15/isoform-usage-training.html 2023-05-15T00:00:00+00:00

The GTN hosts a new training for analyzing alternative splicing at genome-wide scale!

Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ 🎥 Recording of Analisi dei dati RNA-Seq basata su riferimenti https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial_IT.md#tutorial-recording-15-may-2023 2023-05-15T00:00:00+00:00

A 2H50M long recording is now available.

Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ 🎥 Recording of Análisis de datos RNA-Seq basados en referencias https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial_ES.md#tutorial-recording-15-may-2023 2023-05-15T00:00:00+00:00

A 2H50M long recording is now available.

Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ 🎥 Recording of Referenzbasierte RNA-Seq-Datenanalyse https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial_DE.md#tutorial-recording-15-may-2023 2023-05-15T00:00:00+00:00

A 2H50M long recording is now available.

Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ 🎥 Recording of Reference-based RNA-Seq data analysis https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/recordings/#tutorial-recording-15-may-2023 2023-05-15T00:00:00+00:00

A 2H50M long recording is now available.

Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ 🛠️ Cell Cycle Regression Workflow https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_cell-cycle/workflows/main_workflow.html 2023-01-25T09:43:32+00:00

Marisa Loach https://training.galaxyproject.org/training-material/hall-of-fame/MarisaJL/ 📚 Bulk matrix to ESet | Creating the bulk RNA-seq dataset for deconvolution https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/bulk-music-3-preparebulk/tutorial.html 2023-01-20T10:58:39+00:00

After completing the MuSiC deconvolution tutorial (Wang et al. 2019), you are hopefully excited to apply this analysis to data of your choice. Annoyingly, getting data in the right format is often what prevents us from being able to successfully apply analyses. This tutorial is all about reformatting a raw bulk RNA-seq dataset pulled from a public resource (the EMBL-EBI Expression atlas (Moreno et al. 2021). Let’s get started!

Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ Mehmet Tekman https://training.galaxyproject.org/training-material/hall-of-fame/mtekman/ Marisa Loach https://training.galaxyproject.org/training-material/hall-of-fame/MarisaJL/ Mehmet Tekman https://training.galaxyproject.org/training-material/hall-of-fame/mtekman/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Pablo Moreno https://training.galaxyproject.org/training-material/hall-of-fame/pcm32/ Julia Jakiela https://training.galaxyproject.org/training-material/hall-of-fame/wee-snufkin/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ 📚 Comparing inferred cell compositions using MuSiC deconvolution https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/bulk-music-4-compare/tutorial.html 2023-01-20T10:58:39+00:00

The goal of this tutorial is to apply bulk RNA deconvolution techniques to a problem with multiple variables - in this case, a model of diabetes is compared with its healthy counterparts. All you need to compare inferred cell compositions are well-annotated, high quality reference scRNA-seq datasets, transformed into MuSiC-friendly Expression Set objects, and your bulk RNA-samples of choice (also transformed into MuSiC-friendly Expression Set objects). For more information on how MuSiC works, you can check out their github site MuSiC or published article (Wang et al. 2019).

Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ Mehmet Tekman https://training.galaxyproject.org/training-material/hall-of-fame/mtekman/ Marisa Loach https://training.galaxyproject.org/training-material/hall-of-fame/MarisaJL/ Mehmet Tekman https://training.galaxyproject.org/training-material/hall-of-fame/mtekman/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Carlos Chee Mendonça https://training.galaxyproject.org/training-material/hall-of-fame/carloscheemendonca/ Morgan Howells https://training.galaxyproject.org/training-material/hall-of-fame/hexhowells/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ 📰 New Tutorial Suite: Deconvolution with MuSiC, from public data to disease interrogation! https://training.galaxyproject.org/training-material/news/2022/11/29/deconvolution.html 2022-11-29T00:00:00+00:00

The still new and shiny single-cell analysis topic now boasts a deconvolution tutorial suite! What does deconvolution do you ask? Well, in this context, it infers cell proportions from bulk RNA-seq data. You heard that correctly - instead of expensive new single-cell experiments, you can re-analyse old bulk RNA-seq data and estimate cell proportions. All you need is a reasonably good single cell dataset to use as a reference and you’re good to go! The tutorial suite shows you how to build your reference from publicly available single cell data, and apply analysis to some publicly available bulk RNA-seq data.

Mehmet Tekman https://training.galaxyproject.org/training-material/hall-of-fame/mtekman/ Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ 🛠️ QC + Mapping + Counting (single+paired) - Ref Based RNA Seq - Transcriptomics - GTN https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/workflows/qc-mapping-counting-paired-and-single.html 2022-04-25T11:33:54+00:00

Reference-based RNA-Seq data analysis

This is okay! Many aspects of the tutorial can potentially affect the exact results you obtain. For example, the reference genome version used and versions of tools. It’s less important to get the exact results shown in the tutorial, and more important to understand the concepts so you can apply them to your own data.

Mallory Freeberg https://training.galaxyproject.org/training-material/hall-of-fame/malloryfreeberg/ ❓ Can I use alternative tools for the Quantification step? https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/mirna-target-finder/faqs/quantification_alternatives.html 2022-02-28T12:17:18+00:00

There are some alternatives to Salmon for reference transcriptome-based RNA quantification. Kallisto and Sailfish use a similar approach, known as pseudoalignment.

Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ 🛠️ MuSiC: Deconvolution https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/bulk-music/workflows/main_workflow.html 2022-02-11T12:34:18+00:00

Workflow for the first half of the "Bulk RNA Deconvolution with MuSiC" tutorial.

Morgan Howells https://training.galaxyproject.org/training-material/hall-of-fame/hexhowells/ 📚 Bulk RNA Deconvolution with MuSiC https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/bulk-music/tutorial.html 2022-02-11T12:34:18+00:00

Bulk RNA-seq data contains a mixture of transcript signatures from several types of cells. We wish to deconvolve this mixture to obtain estimates of the proportions of cell types within the bulk sample. To do this, we can use single cell RNA-seq data as a reference for estimating the cell type proportions within the bulk data.

Mehmet Tekman https://training.galaxyproject.org/training-material/hall-of-fame/mtekman/ Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ Morgan Howells https://training.galaxyproject.org/training-material/hall-of-fame/hexhowells/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Mehmet Tekman https://training.galaxyproject.org/training-material/hall-of-fame/mtekman/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ ❓ I’m using the same training data, tools, and parameters as the tutorial, but I get a different number of transcripts with a significant change in gene expression between the G1E and megakaryocyte cellular states. Why? https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/de-novo/faqs/transcript.html 2021-11-22T14:46:46+00:00

Rahmot Afolabi https://training.galaxyproject.org/training-material/hall-of-fame/rahmot/ ❓ In 'infer experiments' I get unequal numbers, but in the IGV it looks like it is unstranded. What does this mean? https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/faqs/strandedness.html 2021-11-22T14:46:46+00:00

It’s also often the case that elimination of the second strand is not perfect, and there are genuine cases of bidirectional transcription in the genome. 70 / 30 % as in your report is not a good result for a stranded library. You can treat this as a stranded library in your analysis, but for instance you couldn’t make the conclusion that a given gene is actually transcribed from the reverse strand. Likely that the library preparation didn’t work perfectly. This can depend on many factors, one is that you need to completely digest your DNA using a high quality DNase before doing the reverse transcription.

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ ❓ RNAstar: Why do we set 36 for 'Length of the genomic sequence around annotated junctions'? https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/faqs/rnastar_tool.html 2021-11-22T14:46:46+00:00

RNA STAR is using the gene model to create the database of splice junctions, and that these don’t “need” to have a length longer than the reads (37bp).

Rahmot Afolabi https://training.galaxyproject.org/training-material/hall-of-fame/rahmot/ ❓ Is it possible to visualize the RNA STAR bam file using the JBrowse tool? https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/faqs/rnastar_bam.html 2021-11-22T14:46:46+00:00

Yes, that should work.

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ ❓ When is the "infer experiment" tool used in practice? https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/faqs/infer_experiments.html 2021-11-22T14:46:46+00:00

Often you are already aware whether the RNA-seq data is stranded or not in the first place because you sequenced it yourself or ordered it from a company.

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ ❓ Could I use a different p-adj value for filtering differentially expressed genes? https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/mirna-target-finder/faqs/different_padj_value.html 2021-11-22T14:46:46+00:00

Yes, you can modify this value, to perform a more rigorous analysis, or extend the range of genes selected. A higher p-value will significantly increase the number of genes selected, at the expense of including possible false positives.

Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ ❓ The tutorial uses the normalised count table for visualisation. What about using VST normalised counts or rlog normalised counts? https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/faqs/deseq2.html 2021-11-22T14:46:46+00:00

this depends on what you would like to do with the table. The DESeq2 wrapper in Galaxy can output all of these, and there is a nice discussion in the DESeq2 vignette about this topic.

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ 🎥 Recording of Whole transcriptome analysis of Arabidopsis thaliana https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/mirna-target-finder/recordings/#tutorial-recording-25-july-2021 2021-07-25T00:00:00+00:00

A 1H10M long recording is now available.

Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ 📰 New Tutorial: Visualization of RNA-Seq results with Volcano Plot in R https://training.galaxyproject.org/training-material/news/2021/06/26/tutorial-volcanoplot-r.html 2021-06-26T00:00:00+00:00

The Volcano plot tutorial introduced volcano plots and showed how they can be easily generated with the Galaxy Volcano plot tool. This new tutorial shows how you can customise a plot using the R script output from the tool and RStudio in Galaxy. A short video for the tutorial is also available on YouTube, created for the GCC2021 Training week.

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ 📚 Visualization of RNA-Seq results with Volcano Plot in R https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-volcanoplot-r/tutorial.html 2021-06-14T16:11:02+00:00

The Volcano plot tutorial, introduced volcano plots and showed how they can be generated with the Galaxy Volcano plot tool. In this tutorial we show how you can customise a plot using the R script output from the tool.

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Martin Čech https://training.galaxyproject.org/training-material/hall-of-fame/martenson/ 🎥 Recording of Whole transcriptome analysis of Arabidopsis thaliana https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/mirna-target-finder/recordings/#tutorial-recording-20-april-2021 2021-04-20T00:00:00+00:00

A 10M long recording is now available.

Automated Text-to-Speech https://training.galaxyproject.org/training-material/hall-of-fame/awspolly/ 🖼️ Whole transcriptome analysis of Arabidopsis thaliana https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/mirna-target-finder/slides.html 2021-04-16T10:55:25+00:00

Introduction

Plant lovers are in luck! A new tutorial on this fascinating kingdom has been added to the GTN. It details the necessary steps to identify potential targets of brassinosteroid-induced miRNAs. Brassinosteroids are phytohormones that have the ability to stimulate plant growth and confer resistance against abiotic and biotic stresses. These characteristics have led to great interest in the field of biotechnology due to its potential for increasing agricultural productivity.

This workflow is generated from the GTN tutorial Whole transcriptome analysis of Arabidopsis thaliana (https://gxy.io/GTN:T00292).

Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ 📚 Whole transcriptome analysis of Arabidopsis thaliana https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/mirna-target-finder/tutorial.html 2021-04-08T16:29:49+00:00

As sessile organisms, the survival of plants under adverse environmental conditions depends, to a large extent, on their ability to perceive stress stimuli and respond appropriately to counteract the potentially damaging effects. Coordination of phytohormones and reactive oxygen species are considered a key element for enhancing stress resistance, allowing fine-tuning of gene expression in response to environmental changes (Planas-Riverola et al. 2019, Ivashuta et al. 2011). These molecules constitute complex signalling networks, endowing with the ability to respond to a variable natural environment.

Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Beatriz Serrano-Solano https://training.galaxyproject.org/training-material/hall-of-fame/beatrizserrano/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Beatriz Serrano-Solano https://training.galaxyproject.org/training-material/hall-of-fame/beatrizserrano/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Nate Coraor https://training.galaxyproject.org/training-material/hall-of-fame/natefoo/ 🛠️ scRNA Plant Analysis https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-plant/workflows/main_workflow.html 2021-04-08T10:59:53+00:00

Downstream Single-cell RNA Plant analysis with ScanPy

🎥 Recording of Bulk RNA Deconvolution with MuSiC https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/bulk-music/recordings/#tutorial-recording-8-march-2021 2021-03-08T00:00:00+00:00

A 20M long recording is now available.

Wendi Bacon https://training.galaxyproject.org/training-material/hall-of-fame/nomadscientist/ 🎥 Recording of RNA Seq Counts to Viz in R https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-counts-to-viz-in-r/recordings/#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 30M long recording is now available.

Fotis E. Psomopoulos https://training.galaxyproject.org/training-material/hall-of-fame/fpsom/ 🎥 Recording of Visualization of RNA-Seq results with Volcano Plot in R https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-volcanoplot-r/recordings/#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 15M long recording is now available.

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ 🎥 Recording of Introduction to Transcriptomics https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/introduction/recordings/#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 30M long recording is now available.

Fotis E. Psomopoulos https://training.galaxyproject.org/training-material/hall-of-fame/fpsom/ 🎥 Recording of Visualization of RNA-Seq results with Volcano Plot https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-volcanoplot/recordings/#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 10M long recording is now available.

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ 🎥 Recording of Whole transcriptome analysis of Arabidopsis thaliana https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/mirna-target-finder/recordings/#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 10M long recording is now available.

Automated Text-to-Speech https://training.galaxyproject.org/training-material/hall-of-fame/awspolly/ 🎥 Recording of De novo transcriptome reconstruction with RNA-Seq https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/de-novo/recordings/#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 48M long recording is now available.

Mallory Freeberg https://training.galaxyproject.org/training-material/hall-of-fame/malloryfreeberg/ 🎥 Recording of Analisi dei dati RNA-Seq basata su riferimenti https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial_IT.md#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 2H30M long recording is now available.

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ 🎥 Recording of Análisis de datos RNA-Seq basados en referencias https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial_ES.md#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 2H30M long recording is now available.

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ 🎥 Recording of Referenzbasierte RNA-Seq-Datenanalyse https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial_DE.md#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 2H30M long recording is now available.

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ 🎥 Recording of Reference-based RNA-Seq data analysis https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/recordings/#tutorial-recording-15-february-2021 2021-02-15T00:00:00+00:00

A 2H30M long recording is now available.

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ 🛠️ QC report https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-reads-to-counts/workflows/qc_report.html 2020-11-15T11:49:58+00:00

rna-seq-reads-to-counts

🖼️ Integrate and query local datasets and distant RDF data with AskOmics using Semantic Web technologies https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-analysis-with-askomics-it/slides.html 2020-07-17T09:55:17+00:00

How to explore data

Xavier Garnier https://training.galaxyproject.org/training-material/hall-of-fame/xgaia/ Anthony Bretaudeau https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/ Anne Siegel https://training.galaxyproject.org/training-material/hall-of-fame/annesiegel/ Olivier Dameron https://training.galaxyproject.org/training-material/hall-of-fame/odameron/ Mateo Boudet https://training.galaxyproject.org/training-material/hall-of-fame/mboudet/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Xavier Garnier https://training.galaxyproject.org/training-material/hall-of-fame/xgaia/ Anthony Bretaudeau https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ 📚 RNA-Seq analysis with AskOmics Interactive Tool https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-analysis-with-askomics-it/tutorial.html 2020-07-09T08:03:59+00:00

AskOmics is a web application for data integration and querying using the Semantic Web technologies. It helps users to convert multiple data sources (CSV/TSV files, GFF and BED annotation) into “RDF triples” and store them in a specific kind of database: an “RDF triplestore”. Under this form, data can then be queried using a specific language: “SPARQL”. AskOmics hides the complexity of these technologies and allows to perform complex queries using a user-friendly interface.

Xavier Garnier https://training.galaxyproject.org/training-material/hall-of-fame/xgaia/ Anthony Bretaudeau https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/ Anne Siegel https://training.galaxyproject.org/training-material/hall-of-fame/annesiegel/ Olivier Dameron https://training.galaxyproject.org/training-material/hall-of-fame/odameron/ Mateo Boudet https://training.galaxyproject.org/training-material/hall-of-fame/mboudet/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Xavier Garnier https://training.galaxyproject.org/training-material/hall-of-fame/xgaia/ Anthony Bretaudeau https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/ 🛠️ RNA-RNA interactome data analysis - chira v1.4.3 https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-interactome/workflows/rna-rna-interactome-data-analysis-chira.html 2020-03-23T21:25:12+00:00

RNA-RNA interactome analysis using ChiRA tool suite

📚 RNA-RNA interactome data analysis https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-interactome/tutorial.html 2020-03-23T21:25:12+00:00

With the advances in the next-generation sequencing technologies, genome-wide RNA-RNA interaction predictions are now

Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ 🛠️ trinity NG https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/full-de-novo/workflows/main_workflow.html 2020-02-13T15:48:56+00:00

roscoff hackathon

🛠️ Single-cell QC with scater https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-scater-qc/workflows/main_workflow.html 2019-10-23T18:48:12+00:00

Single-cell quality control with scater

Graham Etherington https://training.galaxyproject.org/training-material/hall-of-fame/ethering/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ 📚 RNA Seq Counts to Viz in R https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-counts-to-viz-in-r/tutorial.html 2019-10-08T17:25:07+00:00

This tutorial will show you how to visualise RNA Sequencing Counts with R

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Fotis E. Psomopoulos https://training.galaxyproject.org/training-material/hall-of-fame/fpsom/ Toby Hodges https://training.galaxyproject.org/training-material/hall-of-fame/tobyhodges/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Anthony Bretaudeau https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/ Anne Fouilloux https://training.galaxyproject.org/training-material/hall-of-fame/annefou/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Mateusz Kuzak https://training.galaxyproject.org/training-material/hall-of-fame/mkuzak/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Martin Čech https://training.galaxyproject.org/training-material/hall-of-fame/martenson/ 🛠️ QC + Mapping + Counting - Ref Based RNA Seq - Transcriptomics - GTN - subworkflows https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/workflows/qc-mapping-counting.html 2019-09-23T19:19:35+00:00

Reference-based RNA-Seq data analysis

Visualization of RNA-Seq results with Volcano Plot

Downstream Single-cell RNA analysis with RaceID

Mehmet Tekman Alex Ostrovsky https://training.galaxyproject.org/training-material/hall-of-fame/astrovsky01/ 🛠️ CelSeq2: Single Batch (mm10) https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-preprocessing/workflows/scrna_pp_celseq.html 2019-02-22T19:53:50+00:00

Pre-processing of Single-Cell RNA Data

🛠️ CelSeq2: Multi Batch (mm10) https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-preprocessing/workflows/scrna_mp_celseq.html 2019-02-22T19:53:50+00:00

Pre-processing of Single-Cell RNA Data

🛠️ GO Enrichment Workflow https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/goenrichment/workflows/goenrichment-workflow.html 2019-02-19T17:07:43+00:00

GO Enrichment Analysis Tutorial

📚 GO Enrichment Analysis https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/goenrichment/tutorial.html 2019-01-23T08:47:44+00:00

When we have a large list of genes of interest, such as a list of differentially expressed genes obtained from an RNA-Seq experiment, how do we extract biological meaning from it?

IGC Bioinformatics Unit https://training.galaxyproject.org/training-material/hall-of-fame/igcbioinformatics/ Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ IGC Bioinformatics Unit https://training.galaxyproject.org/training-material/hall-of-fame/igcbioinformatics/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ 📚 Visualization of RNA-Seq results with Volcano Plot https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-volcanoplot/tutorial.html 2018-12-31T10:26:01+00:00

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Armin Dadras https://training.galaxyproject.org/training-material/hall-of-fame/dadrasarmin/ Teresa Müller https://training.galaxyproject.org/training-material/hall-of-fame/teresa-m/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ 🛠️ Heatmap2 Workflow https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-heatmap2/workflows/rna-seq-viz-with-heatmap2.html 2018-12-31T00:41:48+00:00

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ 🛠️ RNA Seq Reads To Counts https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-reads-to-counts/workflows/rna-seq-reads-to-counts.html 2018-12-31T00:41:48+00:00

RNA-Seq reads to counts

🛠️ RNA Seq Genes To Pathways (imported from uploaded file) https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-genes-to-pathways/workflows/rna-seq-genes-to-pathways.html 2018-12-31T00:41:48+00:00

RNA-seq genes to pathways

🛠️ RNA Seq Counts To Genes https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-counts-to-genes/workflows/rna-seq-counts-to-genes.html 2018-12-31T00:41:48+00:00

RNA-seq counts to genes

📚 Visualization of RNA-Seq results with heatmap2 https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-heatmap2/tutorial.html 2018-12-31T00:41:48+00:00

Heatmaps are commonly used to visualize RNA-Seq results. They are useful for visualizing the expression of genes across the samples. In this tutorial we show how the heatmap2 tool in Galaxy can be used to generate heatmaps. The heatmap2 tool uses the heatmap.2 function from the R gplots package. Here we will demonstrate how to make a heatmap of the top differentially expressed (DE) genes in an RNA-Seq experiment, similar to what is shown for the fruitfly dataset in the RNA-seq ref-based tutorial. We will also show how a heatmap for a custom set of genes an be created.

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ 📚 2: RNA-seq counts to genes https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-counts-to-genes/tutorial.html 2018-12-31T00:41:48+00:00

Measuring gene expression on a genome-wide scale has become common practice over the last two decades or so, with microarrays predominantly used pre-2008. With the advent of next generation sequencing technology in 2008, an increasing number of scientists use this technology to measure and understand changes in gene expression in often complex systems. As sequencing costs have decreased, using RNA-Seq to simultaneously measure the expression of tens of thousands of genes for multiple samples has never been easier. The cost of these experiments has now moved from generating the data to storing and analysing it.

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Belinda Phipson https://training.galaxyproject.org/training-material/hall-of-fame/bphipson/ Jovana Maksimovic https://training.galaxyproject.org/training-material/hall-of-fame/JovMaksimovic/ Anna Trigos https://training.galaxyproject.org/training-material/hall-of-fame/annatrigos/ Matt Ritchie https://training.galaxyproject.org/training-material/hall-of-fame/mritchie/ Harriet Dashnow https://training.galaxyproject.org/training-material/hall-of-fame/hdashnow/ Shian Su https://training.galaxyproject.org/training-material/hall-of-fame/Shians/ Charity Law https://training.galaxyproject.org/training-material/hall-of-fame/charitylaw/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Xavier Garnier https://training.galaxyproject.org/training-material/hall-of-fame/xgaia/ Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ 📚 3: RNA-seq genes to pathways https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-genes-to-pathways/tutorial.html 2018-12-31T00:41:48+00:00

Sometimes there is quite a long list of genes to interpret after a differential expression analysis, and it is usually infeasible to go through the list one gene at a time trying to understand it’s biological function. A common downstream procedure is gene set testing, which aims to understand which pathways/gene networks the differentially expressed genes are implicated in. There are many different gene set testing methods that can be applied and it can be useful to try several.

Small Non-coding RNA Clustering using BlockClust

📚 Small Non-coding RNA Clustering using BlockClust https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/small_ncrna_clustering/tutorial.html 2018-12-14T13:36:25+00:00

Small Non-coding RNAs (ncRNAs) play a vital role in many cellular processes such as RNA splicing, translation, gene regulation. The small RNA-seq is a type of RNA-seq in which RNA fragments are size selected to capture only short RNAs. One of the most common applications of the small RNA-seq is discovering novel small ncRNAs. Mapping the small RNA-seq data reveals interesting patterns that represent the traces of the small RNA processing.

Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ 🖼️ Network Analysis with Heinz https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/network-analysis-with-heinz/slides.html 2018-10-16T12:07:43+00:00

Metagenomics / metatranscriptomics

Sanne Abeln https://training.galaxyproject.org/training-material/hall-of-fame/sanneabeln/ Chao Zhang https://training.galaxyproject.org/training-material/hall-of-fame/ChaoZhang123/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ CicoZhang https://training.galaxyproject.org/training-material/hall-of-fame/CicoZhang/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ 🛠️ Workflow Constructed From History 'Heinz Workflow Trial Sep 11' https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/network-analysis-with-heinz/workflows/Galaxy-Workflow-Heinz.html 2018-10-11T09:07:16+00:00

Network analysis with Heinz

📚 Network analysis with Heinz https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/network-analysis-with-heinz/tutorial.html 2018-10-11T09:07:16+00:00

Overview

Chao Zhang https://training.galaxyproject.org/training-material/hall-of-fame/ChaoZhang123/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ CicoZhang https://training.galaxyproject.org/training-material/hall-of-fame/CicoZhang/ Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ 📚 1: RNA-Seq reads to counts https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-reads-to-counts/tutorial.html 2018-09-23T10:38:20+00:00

Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Belinda Phipson https://training.galaxyproject.org/training-material/hall-of-fame/bphipson/ Harriet Dashnow https://training.galaxyproject.org/training-material/hall-of-fame/hdashnow/ Tristan Reynolds https://training.galaxyproject.org/training-material/hall-of-fame/tflowers15/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Mehmet Tekman https://training.galaxyproject.org/training-material/hall-of-fame/mtekman/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ 🛠️ De novo transcriptome reconstruction with RNA-Seq https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/de-novo/workflows/transcriptomics-denovo-workflow.html 2018-09-17T08:57:36+00:00

De novo transcriptome reconstruction with RNA-Seq

🛠️ Tutorial CLIPseq Explorer Demultiplexed PEAKachu eCLIP Hg38 https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/clipseq/workflows/init_workflow.html 2018-08-17T20:40:15+00:00

CLIP-Seq data analysis from pre-processing to motif detection

📚 CLIP-Seq data analysis from pre-processing to motif detection https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/clipseq/tutorial.html 2018-08-17T20:40:15+00:00

The eCLIP data provided here is a subset of the eCLIP data of RBFOX2 from a study published by Nostrand et al. (Nostrand et al. 2016). The dataset contains the first biological replicate of RBFOX2 CLIP-seq and the input control experiment (FASTQ files). The data was changed and downsampled to reduce data processing time, consequently the data does not correspond to the original source pulled from Nostrand et al. (Nostrand et al. 2016). Also included is a text file (.txt) encompassing the chromosome sizes of hg38 and a genome annotation (.gtf) file taken from Ensembl.

Florian Heyl https://training.galaxyproject.org/training-material/hall-of-fame/heylf/ Daniel Maticzka https://training.galaxyproject.org/training-material/hall-of-fame/dmaticzka/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Florian Heyl https://training.galaxyproject.org/training-material/hall-of-fame/heylf/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Niall Beard https://training.galaxyproject.org/training-material/hall-of-fame/njall/ 🛠️ rna-seq-viz-with-cummerbund https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-cummerbund/_workflows/workflow.html 2017-12-07T11:07:28+00:00

Visualization of RNA-Seq results with CummeRbund

🖼️ Visualization of RNA-Seq results with CummeRbund https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-cummerbund/slides.html 2017-10-16T12:59:20+00:00

Why visualization?

Andrea Bagnacani https://training.galaxyproject.org/training-material/hall-of-fame/bagnacan/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Andrea Bagnacani https://training.galaxyproject.org/training-material/hall-of-fame/bagnacan/ Mallory Freeberg https://training.galaxyproject.org/training-material/hall-of-fame/malloryfreeberg/ Niall Beard https://training.galaxyproject.org/training-material/hall-of-fame/njall/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ 📚 Visualization of RNA-Seq results with CummeRbund https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-cummerbund/tutorial.html 2017-10-16T12:59:20+00:00

RNA-Seq analysis helps researchers annotate new genes and splice variants, and provides cell- and context-specific quantification of gene expression. RNA-Seq data, however, are complex and require both computer science and mathematical knowledge to be managed and interpreted.

Andrea Bagnacani https://training.galaxyproject.org/training-material/hall-of-fame/bagnacan/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Andrea Bagnacani https://training.galaxyproject.org/training-material/hall-of-fame/bagnacan/ Mallory Freeberg https://training.galaxyproject.org/training-material/hall-of-fame/malloryfreeberg/ Niall Beard https://training.galaxyproject.org/training-material/hall-of-fame/njall/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ 🛠️ sRNA Seq Step 2: Salmon And DESeq2 https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/srna/workflows/sRNA_seq_Step_2_Salmon_and_DESeq2.html 2017-06-29T12:29:59+00:00

This workflow completes the second part of the sRNA-seq tutorial from transcript quantification through differential abundance testing.

🛠️ sRNA Seq Step 1: Read Pre Processing And Removal Of Artifacts (no Grooming) https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/srna/workflows/sRNA_seq_Step_1_Read_preprocessing_and_removal_of_artifacts.html 2017-06-29T12:29:59+00:00

This workflow completes the first part of the small RNA-seq tutorial from read pre-processing to trimming.e

📚 Differential abundance testing of small RNAs https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/srna/tutorial.html 2017-06-29T12:29:59+00:00

Small, noncoding RNA (sRNA) molecules, typically 18-40nt in length, are key features of post-transcriptional regulatory mechanisms governing gene expression. Through interactions with protein cofactors, these tiny sRNAs typically function by perfectly or imperfectly basepairing with substrate RNA molecules, and then eliciting downstream effects such as translation inhibition or RNA degradation. Different subclasses of sRNAs - e.g. microRNAs (miRNAs), Piwi-interaction RNAs (piRNAs), and endogenous short interferring RNAs (siRNAs) - exhibit unique characteristics, and their relative abundances in biological contexts can indicate whether they are active or not. In this tutorial, we will examine expression of the piRNA subclass of sRNAs and their targets in Drosophila melanogaster.

Mallory Freeberg https://training.galaxyproject.org/training-material/hall-of-fame/malloryfreeberg/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Mallory Freeberg https://training.galaxyproject.org/training-material/hall-of-fame/malloryfreeberg/ William Durand https://training.galaxyproject.org/training-material/hall-of-fame/willdurand/ Marius van den Beek https://training.galaxyproject.org/training-material/hall-of-fame/mvdbeek/ Niall Beard https://training.galaxyproject.org/training-material/hall-of-fame/njall/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ 📚 De novo transcriptome reconstruction with RNA-Seq https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/de-novo/tutorial.html 2017-02-19T23:07:52+00:00

The data provided here are part of a Galaxy tutorial that analyzes RNA-seq data from a study published by Wu et al. in 2014 DOI:10.1101/gr.164830.113. The goal of this study was to investigate “the dynamics of occupancy and the role in gene regulation of the transcription factor Tal1, a critical regulator of hematopoiesis, at multiple stages of hematopoietic differentiation.” To this end, RNA-seq libraries were constructed from multiple mouse cell types including G1E - a GATA-null immortalized cell line derived from targeted disruption of GATA-1 in mouse embryonic stem cells - and megakaryocytes. This RNA-seq data was used to determine differential gene expression between G1E and megakaryocytes and later correlated with Tal1 occupancy. This dataset (GEO Accession: GSE51338) consists of biological replicate, paired-end, poly(A) selected RNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to chromosome 19 and a subset of interesting genomic loci identified by Wu et al.

Mallory Freeberg https://training.galaxyproject.org/training-material/hall-of-fame/malloryfreeberg/ Mo Heydarian https://training.galaxyproject.org/training-material/hall-of-fame/moheydarian/ James Taylor https://training.galaxyproject.org/training-material/hall-of-fame/jxtx/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Anthony Bretaudeau https://training.galaxyproject.org/training-material/hall-of-fame/abretaud/ Gildas Le Corguillé https://training.galaxyproject.org/training-material/hall-of-fame/lecorguille/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Mallory Freeberg https://training.galaxyproject.org/training-material/hall-of-fame/malloryfreeberg/ Mo Heydarian https://training.galaxyproject.org/training-material/hall-of-fame/moheydarian/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ William Durand https://training.galaxyproject.org/training-material/hall-of-fame/willdurand/ Niall Beard https://training.galaxyproject.org/training-material/hall-of-fame/njall/ 🖼️ Introduction to Transcriptomics https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/introduction/slides.html 2016-10-05T15:28:00+00:00

What is RNA sequencing?

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Anika Erxleben https://training.galaxyproject.org/training-material/hall-of-fame/erxleben/ Markus Wolfien https://training.galaxyproject.org/training-material/hall-of-fame/mwolfien/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ William Durand https://training.galaxyproject.org/training-material/hall-of-fame/willdurand/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Martin Čech https://training.galaxyproject.org/training-material/hall-of-fame/martenson/ Armin Dadras https://training.galaxyproject.org/training-material/hall-of-fame/dadrasarmin/ 📚 Reference-based RNA-Seq data analysis https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html 2016-10-05T15:28:00+00:00

In recent years, RNA sequencing (in short RNA-Seq) has become a very widely used technology to analyze the continuously changing cellular transcriptome, i.e. the set of all RNA molecules in one cell or a population of cells. One of the most common aims of RNA-Seq is the profiling of gene expression by identifying genes or molecular pathways that are differentially expressed (DE) between two or more biological conditions. This tutorial demonstrates a computational workflow for the detection of DE genes and pathways from RNA-Seq data by providing a complete analysis of an RNA-Seq experiment profiling Drosophila cells after the depletion of a regulatory gene.

Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Mallory Freeberg https://training.galaxyproject.org/training-material/hall-of-fame/malloryfreeberg/ Mo Heydarian https://training.galaxyproject.org/training-material/hall-of-fame/moheydarian/ Anika Erxleben https://training.galaxyproject.org/training-material/hall-of-fame/erxleben/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Clemens Blank https://training.galaxyproject.org/training-material/hall-of-fame/blankclemens/ Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Peter van Heusden https://training.galaxyproject.org/training-material/hall-of-fame/pvanheus/ Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ Helena Rasche https://training.galaxyproject.org/training-material/hall-of-fame/hexylena/ Clea Siguret https://training.galaxyproject.org/training-material/hall-of-fame/clsiguret/ Wolfgang Maier https://training.galaxyproject.org/training-material/hall-of-fame/wm75/ Pavankumar Videm https://training.galaxyproject.org/training-material/hall-of-fame/pavanvidem/ Bérénice Batut https://training.galaxyproject.org/training-material/hall-of-fame/bebatut/ Lucille Delisle https://training.galaxyproject.org/training-material/hall-of-fame/lldelisle/ Björn Grüning https://training.galaxyproject.org/training-material/hall-of-fame/bgruening/ Nicola Soranzo https://training.galaxyproject.org/training-material/hall-of-fame/nsoranzo/ Hans-Rudolf Hotz https://training.galaxyproject.org/training-material/hall-of-fame/hrhotz/ Cristóbal Gallardo https://training.galaxyproject.org/training-material/hall-of-fame/gallardoalba/ Saskia Hiltemann https://training.galaxyproject.org/training-material/hall-of-fame/shiltemann/ Graeme Tyson https://training.galaxyproject.org/training-material/hall-of-fame/hrukkudyr/ Maria Doyle https://training.galaxyproject.org/training-material/hall-of-fame/mblue9/ Amirhossein Naghsh Nilchi https://training.galaxyproject.org/training-material/hall-of-fame/Nilchia/ Mehmet Tekman https://training.galaxyproject.org/training-material/hall-of-fame/mtekman/ Anika Erxleben https://training.galaxyproject.org/training-material/hall-of-fame/erxleben/ Simon Bray https://training.galaxyproject.org/training-material/hall-of-fame/simonbray/ Clemens Blank https://training.galaxyproject.org/training-material/hall-of-fame/blankclemens/ Ekaterina Polkh https://training.galaxyproject.org/training-material/hall-of-fame/polkhe/ William Durand https://training.galaxyproject.org/training-material/hall-of-fame/willdurand/ Marius van den Beek https://training.galaxyproject.org/training-material/hall-of-fame/mvdbeek/ Niall Beard https://training.galaxyproject.org/training-material/hall-of-fame/njall/ Martin Čech https://training.galaxyproject.org/training-material/hall-of-fame/martenson/ Armin Dadras https://training.galaxyproject.org/training-material/hall-of-fame/dadrasarmin/ Florian Heyl https://training.galaxyproject.org/training-material/hall-of-fame/heylf/