Welcome to the Galaxy Training Video Library!

This tutorial guides you through the preprocessing of sequencing data of bronchoalveolar lavage fluid (BALF) samples obtained from early COVID-19 patients in China. Since such samples are expected to be contaminated signficantly with human sequenced reads, the goal is to enrich the data for SARS-CoV-2 reads by identifying and discarding reads of human origin before trying to assemble the viral genome sequence.

• Slides: Version in Video | Latest Version
• Tutorial: Version in Video | Latest Version
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• Slack: #assembly_assembly-with-preprocessing
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In this tutorial, you will assemble a chloroplast genome using a combination of short and long read data.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #assembly_chloroplast-assembly
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• Assemble a chloroplast genome from long reads
• Polish the assembly with short reads
• Annotate the assembly and view
• Map reads to the assembly and view

This lecture walks you through the basic concepts involved in assembling a genome from short-read sequencing data.

• Slides: Version in Video | Latest Version

• Slack: #assembly_general-introduction
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• assemble some paired end reads using Velvet
• examine the output of the assembly.

This demo walks you through running the VGP Genome Assembly workflows, rather than the whole tutorial (as most GTN Tapas videos do)

• Tutorial: Version in Video | Latest Version
  FAQs
• Non-GTN Lecture

• Slack: #assembly_vgp_genome_assembly-workflow
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This tutorial will walk you through a Galaxy analysis step by step

• Tutorial: Version in Video | Latest Version
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• Slack: #climate_fates
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• Setting up a CLM-FATES case.
• Customizing your run.
• Interactive visualization with Panoply.
• Automating your analyzes and visualisations of your CLM-FATES case.
• Creating multi-case scenarios.
• Composing, executing and publishing CML-FATES workflow.

This lecture provides a brief overview of FATES

• Slides: Version in Video | Latest Version

• Slack: #climate_fates
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• Setting up a CLM-FATES case.
• Customizing your run.
• Interactive visualization with Panoply.
• Automating your analyzes and visualisations of your CLM-FATES case.
• Creating multi-case scenarios.
• Composing, executing and publishing CML-FATES workflow.

In this tutorial, you will learn how to manipulate netCDF data files using Xarray Galaxy Tools. NetCDF stands for network Common Data Form and is one of the most popular file format in climate science. It is used for storing multidimensional scientific data variables such as temperature or humidity, and metadata can be added to facilitate sharing of netCDF data. netCDF is widely used outside the Climate Science community and each community has its own set of conventions, especially for metadata. The Climate and Forecast metadata convention, also called CF-convention is used by the Climate community and is designed to promote the processing and sharing of netCDF files.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #climate_pangeo
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• Understand what Pangeo and Xarray are
• Learn to get metadata information using Xarray Galaxy Tools
• Learn to select data
• Learn to visualize geographical data on a map
• Learn to filter, make reduction operations (mean, max, min)
• Learn to resample my data

In this tutorial, we will learn about Xarray, one of the most used Python library from the Pangeo ecosystem.

We will be using data from Copernicus Atmosphere Monitoring Service and more precisely PM2.5 (Particle Matter < 2.5 μm) 4 days forecast from December, 22 2021. Parallel data analysis with Pangeo is not covered in this tutorial.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #climate_pangeo-notebook
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• Learn to get metadata information using Xarray Galaxy Tools
• Learn to select data
• Learn to visualize data
• Learn to filter, make reduction operations (mean, max, min)
• Learn to resample my data
• Learn to cite and contribute to Pangeo

This short video gives an overview of the worldwide Galaxy community, and different ways you can get involved! Video created by Beatriz Serrano-Solano.

• galaxyproject.org
• Find a Galaxy server near you!
• Start learning Galaxy
• Galaxy Help Forum
• Zotero
• Find and Join a community
• Galaxy Event Horizon
• Stay informed of Galaxy activity!
• Find and join a WG
• galaxyproject
• Start talking with the Galaxy Community!
• Join the training discussion here!
• Monthly Collaboration Fest
• Everybody Welcome!
• follow @galaxyproject
• follow the GTN @gxytraining

• Slack: #general
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Molecular dynamics (MD) is a method to simulate molecular motion by iterative application of Newton’s laws of motion. This tutorial provides an introduction to using high-throughput molecular dynamics to study protein-ligand interaction, as applied to the N-terminal domain of Hsp90 (heat shock protein 90).

• Tutorial: Version in Video | Latest Version
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• Slack: #computational-chemistry_htmd-analysis
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• Learn about force-fields and MD parameterization
• Learn how to conduct MD simulation and analysis for a protein-ligand system
• Understand how different molecular interactions contribute to the binding affinity of various ligands for the Hsp90 protein.

An advanced tutorial covering downstream analysis of RNA-seq data using R.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #data-science_r-advanced
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• Be able to load and explore the shape and contents of a tabular dataset using base R functions.
• Understand factors and how they can be used to store and work with categorical data.
• Apply common `dplyr` functions to manipulate data in R.
• Employ the ‘pipe’ operator to link together a sequence of functions.

This tutorial will provide an introduction to using R with Rstudio in Galaxy.

• Tutorial: Version in Video | Latest Version
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• Slack: #data-science_r-basics
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• Know advantages of analyzing data using R within Galaxy.
• Compose an R script file containing comments, commands, objects, and functions.
• Be able to work with objects (i.e. applying mathematical and logical operators, subsetting, retrieving values, etc).

This tutorial aims to present the PAMPA Galaxy workflow, how to use it to compute common biodiversity metrics from species abundance data and analyse it through generalized linear (mixed) models (GLM and GLMM). This workflow made up of 5 tools will allow you to process temporal series data that include at least year, location and species sampled along with abundance value and, finally, generate article-ready data products.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #ecology_PAMPA-toolsuite-tutorial
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• Upload data from DATRAS portal of ICES
• Pre-process population data with Galaxy
• Learning how to use an ecological analysis workflow from raw data to graphical representations
• Learning how to construct a Generalized Linear (Mixed) Model from a usual ecological question
• Learning how to interpret a Generalized Linear (Mixed) Model

Based on this OBITools official tutorial, you will learn here how to analyze DNA metabarcoding data produced on Illumina sequencers

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #ecology_Obitools-metabarcoding
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• Deal with paired-end data to create consensus sequences
• Clean, filter and anlayse data to obtain strong results

• Tutorial: Version in Video | Latest Version
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• Slack: #ecology_biodiversity-data-exploration
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• Explore Biodiversity data with taxonomic, temporal and geographical informations
• Have an idea about quality content of the data regarding statistical tests like normality or homoscedasticity and coverage like temporal or geographical coverage

This lecture will introduce the basic concepts involved in ATAC-Seq analysis.

• Slides: Version in Video | Latest Version

• Slack: #epigenetics_atac-seq
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• Apply appropriate analysis and quality control steps for ATAC-Seq
• Generate a heatmap of transcription start site accessibility
• Visualise peaks for specific regions

This tutorial will walk you through a ATAC-Seq data analysis.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #epigenetics_atac-seq
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• Apply appropriate analysis and quality control steps for ATAC-Seq
• Generate a heatmap of transcription start site accessibility
• Visualise peaks for specific regions

To make a workflow reusable with slightly different settings you can define and use workflow parameters.

• Tutorial: Version in Video | Latest Version
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• Slack: #galaxy-interface_workflow-parameters
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• Learn how to use Workflow Parameters to improve your Workflows

Learn how to view, customize and share Galaxy workflow reports

• Tutorial: Version in Video | Latest Version
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• Slack: #galaxy-interface_workflow-reports
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• Understanding, viewing and creating workflow reports

• Tutorial: Version in Video | Latest Version
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• Slack: #galaxy-interface_collections
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• Understand and master dataset collections

This lecture will introduce the SRA Aligned Read format available in the cloud from SRA, as well as some accompanying metadata that can help you search and filter the data. This sessions is aimed specifically at SARS-CoV-2 runs in SRA.

• Slides: Version in Video | Latest Version

• Slack: #galaxy-interface_ncbi-sarf
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• Learn about SRA aligned read format and vcf files for Runs containing SARS-CoV-2 content
• Understand how to search the metadata for these Runs to find your dataset of interest and then import that data in your preferred format

This tutorial will walk you through accessing and using SRA Aligned read format in Galaxy.

• Tutorial: Version in Video | Latest Version
  FAQs
• Related Tutorial: Version in Video | Latest Version

• Slack: #galaxy-interface_ncbi-sarf
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• Learn about SRA aligned read format and vcf files for Runs containing SARS-CoV-2 content
• Understand how to search the metadata for these Runs to find your dataset of interest and then import that data in your preferred format

Through a series of examples, this tutorial aims to familiarize the reader with building Galaxy collections from tabular data containing URLs, sample sheets, list of accessions or identifiers, etc..

• Tutorial: Version in Video | Latest Version
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• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #galaxy-interface_upload-rules
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So you’ve used Galaxy workflows to analyze your SARS-CoV-2 samples? Learn in this tutorial how to export results to your favorite datastore.

• Slack: #galaxy-interface_upload-to-ena
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This is a more technical tutorial which will teach you how to run workflows from the command line and scale your analyses. If you are on Windows, you will need WSL/WSL2 setup before this session.

• Installing WSL on Windows
• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #galaxy-interface_workflow-automation
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This tutorial will show you how you can start Rstudio from within Galaxy. This option is only available on Galaxy EU for the time being. If you are working on a different Galaxy server, you can use Rstudio Cloud (https://rstudio.cloud/)

• Try Rstudio Cloud instead

• Slack: #data-science_rstudio-galaxy
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So you’ve used Galaxy workflows to analyze your SARS-CoV-2 samples? Learn in this tutorial how to export results to your favorite datastore.

• Slack: #galaxy-interface_upload-to-ena
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See in this demo how, on usegalaxy.*, we’ve used Planemo and Bioblend to build and operate an automated SARS-CoV-2 genome surveillance system based on the Galaxy workflows for variant calling, consensus building and reporting.

• Slack: #sars-cov2_usegalaxy-star-bot
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Get an overview of the workshop: production-ready Galaxy workflows for SARS-CoV-2 sequencing data, tools you should know to automate workflow execution, and how you combine all of it to turn Galaxy into a platform for genome-surveillance

• Slack: #sars-cov2_using-galaxy
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How to visualize tens of thousands of SARS-CoV-2 analysis results? Learn about the Viral Beacon project’s solution!

• Slack: #sars-cov2_viral-beacon
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In this demo you will get to know the ObservableHQ platform for interactive data visualization. You will see how covid19.galaxyproject.org uses it to build a dashboard for their SARS-CoV-2 analysis efforts and will learn how to customize this solution to fit your own purposes.

• Slack: #sars-cov2_observablehq
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• Slack: #sars-cov2_upload-ena
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This video will introduce the Galaxy data analysis platform, and give a short demo on how to use it.

• Related Slides: Version in Video | Latest Version

• Slack: #galaxy-intro
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In this tutorial, we will walk you through your first (toy) analysis in Galaxy. This tutorial is aimed at familiarizing you with the Galaxy platform, and some basic NGS concepts.

• Overview Presentation
• Tutorial: Version in Video | Latest Version
  FAQs
• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #galaxy-intro
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This demo videos shows you how you can run Interactive tools such as RStudio and Jupyter notebooks within Galaxy.

• Slack: #galaxy-interface_interactive-tools
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This video will introduce the Galaxy data analysis platform, and give a short demo on how to use it.

• Related Slides: Version in Video | Latest Version

• Slack: #galaxy-intro
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• Galaxy Help Forum

This video tutorial will provide a non-genomics based first look at the Galaxy platform, how to use it, and discusses how you can get support for your data analysis.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #galaxy-intro
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• Familiarize yourself with the basics of Galaxy
• Learn how to obtain data from external sources
• Learn how to tag datasets
• Learn how to run tools
• Learn how histories work
• Learn how to create a workflow
• Learn how to share your work

In this tutorial, we will walk you through your first (toy) analysis in Galaxy. This tutorial is aimed at familiarizing you with the Galaxy platform, and some basic NGS concepts.

• Overview Presentation
• Tutorial: Version in Video | Latest Version
  FAQs
• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #galaxy-intro
  • Slack Invite
• Gitter
• Galaxy Help Forum

This demo videos shows you how you can run Interactive tools such as RStudio and Jupyter notebooks within Galaxy.

• Slack: #galaxy-interface_interactive-tools
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• Gitter
• Galaxy Help Forum

This lecture will introduce the basic concepts involved in CRISPR screen analysis.

• Slides: Version in Video | Latest Version

• Slack: #genome-annotation_crispr-screen
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• Apply appropriate analysis and quality control steps for CRISPR screen data
• Identify differentially enriched genes across conditions
• Generate volcano plot to visualise results

This tutorial will walk you through a CRISPR screen analysis.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #genome-annotation_crispr-screen
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• Apply appropriate analysis and quality control steps for CRISPR screen data
• Identify differentially enriched genes across conditions
• Generate volcano plot to visualise results

Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.

• Slides: Version in Video | Latest Version

• Slack: #genome-annotation_annotation-with-prokka
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• Load genome into Galaxy
• Annotate genome with Prokka
• View annotations in JBrowse

In this tutorial we will use Prokka to annotate a draft genome sequence. Prokka finds and annotates features (both protein coding regions and RNA genes, i.e. tRNA, rRNA) present on on a sequence. Prokka can be used to annotate bacterial, archaeal and viral genomes quickly, generating standard output files in GenBank, EMBL and gff formats.”

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #genome-annotation_annotation-with-prokka
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• Load genome into Galaxy
• Annotate genome with Prokka
• View annotations in JBrowse

This lecture gives a quick overview of Apollo

• Slides: Version in Video | Latest Version

• Slack: #genome-annotation_apollo
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• Load a genome into Galaxy
• View annotations in JBrowse
• Learn how to load JBrowse data into Apollo
• Learn how to manually refine genome annotations within Apollo
• Export refined genome annotations

In this tutorial you will get some hands-on experience using Apollo in Galaxy

• Tutorial: Version in Video | Latest Version
  FAQs
• Apollo User's Guide

• Slack: #genome-annotation_apollo
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• Load a genome into Galaxy
• View annotations in JBrowse
• Learn how to load JBrowse data into Apollo
• Learn how to manually refine genome annotations within Apollo
• Export refined genome annotations

The aim of repeat masking is to identify the location of all repeated elements along a genome sequence. Other tools (like annotation pipelines) can then take this information into account when producing their results.

• Tutorial: Version in Video | Latest Version
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• Slack: #genome-annotation_repeatmasker
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• Use RepeatMasker to soft mask a newly assmbled genome

Genome annotation of eukaryotes is a little more complicated than for prokaryotes: eukaryotic genomes are usually larger than prokaryotes, with more genes. The sequences determining the beginning and the end of a gene are generally less conserved than the prokaryotic ones. Many genes also contain introns, and the limits of these introns (acceptor and donor sites) are not highly conserved.

In this tutorial we will use a software tool called Funannotate (Palmer and Stajich 2020) to annotate the genome sequence of a small eukaryote: Mucor mucedo (a fungal plant pathogen).

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #genome-annotation_funannotate
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• Load genome into Galaxy
• Annotate genome with Funannotate
• Perform functional annotation using EggNOG-mapper and InterProScan
• Evaluate annotation quality with BUSCO
• View annotations in JBrowse

This lecture talks about the basics of TnSeq analysis

• Non-GTN Lecture

• Slack: #genome-annotation_tnseq-lecture
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This tutorial will introduce you to Galaxy. You will familiarize yourself with tools, workflows and histories. Those skills will be needed the next days

• Tutorial: Version in Video | Latest Version
  FAQs
• History on GalaxyEU

• Slack: #galaxy-intro
  • Slack Invite
• Gitter
• Galaxy Help Forum

• Familiarize yourself with the basics of Galaxy
• Learn how to obtain data from external sources
• Learn how to run tools
• Learn how histories work
• Learn how to create a workflow
• Learn how to share your work

In this demo video, we will show how to perform an NGS data analysis, using a SARS-CoV-2 example dataset. If you would like to run the full tutorial yourself, please find the link below (duration 1h-1.5h).

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #galaxy-intro
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Nanopore Sequencing has many applications in a clinical setting. In this lecture, Astrid Heikema discusses how her group at the Erasmus Medical Center uses Nanopore for sequencing of bacterial Genomes.

• Slack: #metagenomics_clinical-applications
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This lecture briefly introduces 16S sequencing, a popular technique used for taxonomic profiling of microbial communities.

• Slides: Version in Video | Latest Version

• Slack: #metagenomics_introduction
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Metatranscriptomics analysis examines how the microbiome responds to the environment by studying the taxonomic composition and functional analysis of genes expressed by the microbiome, using microbial community RNASeq data and subsequent metatranscriptomics workflows. This workshop will introduce researchers to the basic concepts and tools from the ASaiM-MT workflow. ASaiM-MT provides a curated collection of tools to explore and visualize taxonomic and functional information from metatranscriptomic sequences.

• Slides from video
• Related Slides: Version in Video | Latest Version
• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #metagenomics_metatranscriptomics
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This lecture briefly introduces 16S sequencing, a popular technique used for taxonomic profiling of microbial communities.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #metagenomics_mothur-miseq-sop-short
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• Analyze of 16S rRNA sequencing data using the mothur toolsuite in Galaxy
• Using a mock community to assess the error rate of your sequencing experiment
• Visualize sample diversity using Krona and Phinch

This tutorial will guide you through the process of assembling your Nanopore-sequenced bacterial samples and determining the presence of antibiotic resistance genes.

• Tutorial: Version in Video | Latest Version
  FAQs
• Related Tutorial: Version in Video | Latest Version

• Slack: #metagenomics_plasmid-metagenomics-nanopore
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• Perform Quality control on your reads
• Assemble a genome with Minimap2/Miniasm/Racon
• Determine the structure of the genome(s)
• Scan for antimicrobial resistance genes with Staramr

The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world’s population. Accurate and timely diagnosis of COVID-19 infection is an important step for providing care and containing its further spread. In this tutorial, attendees will be introduced to two workflows – a) database search workflow and b) peptide validation workflow. Attendees of the workshop will get an in-depth knowledge of the Galaxy workflows that detect SARS-CoV-2 peptides (10.1186/s12014-021-09321-1)) and co-infecting pathogen peptides (10.1021/acs.jproteome.0c00822).

• Slides
• Workflow

• Slack: #proteomics_pandemic
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• Galaxy Help Forum

This lecture will introduce EncyclopeDIA.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #proteomics_encyclopedia
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This tutorial will walk you through an analysis with EncyclopeDIA step by step.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #proteomics_encyclopedia
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• Understand the difference between DDA and DIA methods
• Performing quantitative analysis using DIA data
• Understand the purpose and benefits of using a Chromatogram Library for detection of peptides
• You can use Bloom's Taxonomy to write effective learning objectives

This lecture will provide an introduction to mass spectrometry (MS) based proteomics analysis. Slides created by Melanie Föll.

• Slides: Version in Video | Latest Version

• Slack: #proteomics_introduction
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In this tutorial, the basic workflow for metaproteomics is described, which includes database search, seeking taxonomy information and functional analysis. Related metaproteomics and functional microbiome tools and GTN tutorials are also mentioned in this tutorial.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #proteomics_metaproteomics
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• A taxonomy and functional analysis of metaproteomic mass spectrometry data.

In this tutorial, we will provide a walkthrough on how to generate a customized protein sequence database using RNA-Seq data. The resultant FASTA database contains sequences with single amino acid variants (SAVs), insertions and deletions (indels)and transcript assemblies (splicing variants). This database can then be used to identify protein sequence variants from the mass spectrometry data.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #proteomics_proteogenomics-dbcreation
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• Generating a customized Protein sequence database with variants, indels, splice junctions and known sequences.

In this demonstration, we’re continuing the process of proteogenomic analysis. Here, we search mass spectrometry data against a custom proteogenomics FASTA database to create peptide spectral matches (PSMs) for each dataset; we also isolate non-canonical peptides from the resulting data for further analysis. If you would like to view the results yourself, we provide example histories in the FAQ document

• Tutorial: Version in Video | Latest Version
  FAQs
• Slides

• Slack: #proteomics_proteogenomics-dbsearch
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• A proteogenomic data analysis of mass spectrometry data to identify and visualize variant peptides.

In this demonstration, we will investigate the peptides that were identified during proteogenomics tutorial 2. We will identify the presence of novel proteoforms that are absent in the reference database, annotate the novel peptides and visualize them using the Multi-omics Visualization Platform (MVP). If you would like to view the results yourself, we provide example histories in the FAQ document

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #proteomics_proteogenomics-novel-peptide-analysis
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• How to assign and visualize the genomic localization of these identified novel proteoforms?

This tutorial covers an annotation pipeline for a protein list identified by LC-MS/MS experiments.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #proteomics_proteome_annotation
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• Execute a complete annotation pipeline of a protein list identified by LC-MS/MS experiments

Modern mass spectrometry-based proteomics enables the identification and quantification of thousands of proteins. Therefore, quantitative mass spectrometry represents an indispensable technology for biological and clinical research. Statistical analyses are required for the unbiased answering of scientific questions and to uncover all important information in the proteomic data. In this training we will cover the full analysis workflow from label-free, data dependent acquisition (DDA) raw data to statistical results. We’ll use two popular quantitative proteomics software: MaxQuant and MSstats.

• Tutorial: Version in Video | Latest Version
  FAQs
• Galaxy History of this tutorial

• Slack: #proteomics_maxquant-msstats-dda-lfq
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• Learn how to use MaxQuant and MSstats for the analysis of label-free shotgun (DDA) data

In this opening presentation, the basic components of proteogenomics are described, including the main steps in the bioinformatics analysis workflow that make up this approach and will be detailed in the following tutorials. Some examples of research questions that benefit from a proteogenomics approach are also highlighted.

• Slides

• Slack: #proteogenomics_introduction
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• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #synthetic-biology_basic_assembly_analysis
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• Galaxy Help Forum

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #synthetic-biology_retrosynthesis_analysis
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• Galaxy Help Forum

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #synthetic-biology_pathway_analysis
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• Galaxy Help Forum

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #variant-analysis_sars-cov-2-variant-discovery
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• Repeat SARS-CoV-2 data preparation
• Select and run workflow to extract annotated allelic variants from FASTQ files
• Run workflow to summarize and generate report for previously called allelic variants
• Interpret summaries for annotated allelic variants
• Run workflow to extract consensus sequences
• Select and run tools to assign clades/lineages

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. According to the WHO, in 2018 there were 10.0 million new cases of TB worldwide and 1.4 million deaths due to the disease, making TB the world’s most deadly infectious disease.

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Variation in the genome of M. tuberculosis (Mtb) is associated with changes in phenotype, for example drug resistance and virulence. It is also useful for outbreak investigation as the single nucleotide polymorphisms (SNPs) in a sample can be used to build a phylogeny.

• Tutorial: Version in Video | Latest Version
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• Slack: #variant-analysis_tb-variant-analysis
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• How should we filter those variants
• How can we predict drug resistance from those variants
• How do we annotate those variants

This lecture covers the basic concepts involved in mapping sequencing reads to a reference genome.

• Slides: Version in Video | Latest Version

• Slack: #ngs_mapping
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• You will learn what mapping is
• A genome browser is shown that helps you to understand your data

In this tutorial you will map sequencing data to a reference genome, and explore the mapped reads in a genome browser.

• Tutorial: Version in Video | Latest Version
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• Slack: #ngs_mapping
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• You will learn what mapping is
• A genome browser is shown that helps you to understand your data

This lecture goes over the concepts involved in assessing the quality of your sequencing data.

• Slides: Version in Video | Latest Version

• Slack: #ngs_quality-control
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• Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
• Assess long reads FASTQ quality using Nanoplot and PycoQC
• Perform quality correction with Cutadapt (short reads)
• Summarise quality metrics MultiQC
• Process single-end and paired-end data

In this tutorial you will get some hand-on experience performing a quality assessment on sequencing data.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #ngs_quality-control
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• Assess short reads FASTQ quality using FASTQE 🧬😎 and FastQC
• Assess long reads FASTQ quality using Nanoplot and PycoQC
• Perform quality correction with Cutadapt (short reads)
• Summarise quality metrics MultiQC
• Process single-end and paired-end data

The lecture explains introductory concepts in machine learning such as supervised and unsupervised learning, classification and regression, hyperparameter optimisation, cross-validation, train, test and validation sets.

• Introduction to Machine Learning

• Slack: #machine_learning_introduction
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This hands-on tutorial covers Convolutional Neural Networks (CNN)

• Slides: Version in Video | Latest Version
• Tutorial: Version in Video | Latest Version
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• Slack: #machine_learning_CNN
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This hands-on tutorial covers Feedforward Neural Networks (FNN)

• Slides: Version in Video | Latest Version
• Tutorial: Version in Video | Latest Version
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• Slack: #machine_learning_FNN
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This hands-on tutorial covers Recurrenct Neural Networks (RNN)

• Slides: Version in Video | Latest Version
• Tutorial: Version in Video | Latest Version
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• Slack: #machine_learning_RNN
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The classification of fruits and vegetables offers many useful applications such as automated harvesting by robots, building up stocks for supermarkets, effective detection of specific defects, and determining fruit ripeness (Duong et al. 2020, Naranjo-Torres et al. 2020, Iswari et al. 2017). Machine Learning (ML) techniques such as Deep Learning (DL) are commonly used for image classification problems in various domains, including in agriculture (Kamilaris and Prenafeta-Boldú 2018). DL is a technique inspired by how a human brain operates. Due to the increased availability of compute capacity and training data, DL techniques have become very popular in recent years. In this tutorial, we will use Galaxy’s ML toolkit to build a DL model to classify fruit and vegetable images. Our DL model is trained and evaluated on Fruit 360 dataset (Mureşan and Oltean 2018)

• Slides: Version in Video | Latest Version
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• Slack: #machine_learning_fruit_360
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The talk includes a lecture followed by a hands-on session to apply multiple classification algorithms on the Quantitative structure-activity relationship (QSAR) dataset to predict the biodegradable activity of chemical compounds. QSAR models attempt to predict the activity or property of chemicals based on their chemical structure. To achieve this, a database of compounds is collected for which the property of interest is known. For each compound, molecular descriptors are collected which describe the structure (for example - molecular weight, number of nitrogen atoms, number of carbon-carbon double bonds). Using these descriptors, a model is constructed which is capable of predicting the property of interest for a new, unknown molecule. In this tutorial, we will use a database assembled from experimental data of the Japanese Ministry of International Trade and Industry to create a classification model by applying simple and complex classifiers to learn the nature of biodegradation. We will use this model to classify new molecules into one of two classes - biodegradable or non-biodegradable. Different visualisations are used to analyze the results after applying each classification algorithm. Hyperparameters of one of the classifiers are also optimised.

• Tutorial: Version in Video | Latest Version
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• Classification in Machine Learning

• Slack: #machine_learning_classification
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• Learn classification background
• Learn what a quantitative structure-analysis relationship (QSAR) model is and how it can be constructed in Galaxy
• Learn to apply logistic regression, k-nearest neighbors, support verctor machines, random forests and bagging algorithms
• Learn how visualizations can be used to analyze the classification results

This is an Introduction to Machine Learning in R, in which you’ll learn the basics of unsupervised learning for pattern recognition and supervised learning for prediction. At the end of this workshop, we hope that you will - appreciate the importance of performing exploratory data analysis (or EDA) before starting to model your data. - understand the basics of unsupervised learning and know the examples of principal component analysis (PCA) and k-means clustering. - understand the basics of supervised learning for prediction and the differences between classification and regression. - understand modern machine learning techniques and principles, such as test train split, k-fold cross validation and regularization. - be able to write code to implement the above techniques and methodologies using R, caret and glmnet. We will not be focusing on the mathematical foundation for each of the methods and approaches we’ll be discussing. There are many resources that can provide this context, but for the purposes of this workshop we believe that they are beyond the scope.

• Tutorial: Version in Video | Latest Version
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• Slack: #machine_learning_intro-to-ml-with-r
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• Understand the ML taxonomy and the commonly used machine learning algorithms for analysing -omics data
• Understand differences between ML algorithms categories and to which kind of problem they can be applied
• Understand different applications of ML in different -omics studies
• Use some basic, widely used R packages for ML
• Interpret and visualize the results obtained from ML analyses on omics datasets
• Apply the ML techniques to analyse their own datasets

The talk includes a lecture followed by a hands-on session to apply multiple regression algorithms on the DNA-methylation dataset to predict biological age. In this tutorial, we will build a regression model for chronological age prediction, based on DNA methylation. This is based on the work of Jana Naue et al. 2017, in which biomarkers are examined to predict the chronological age of humans by analyzing the DNA methylation patterns. Different machine learning algorithms are used in this study to make an age prediction. It has been recognized that within each individual, the level of DNA methylation changes with age. This knowledge is used to select useful biomarkers from DNA methylation datasets. The CpG sites with the highest correlation to age are selected as the biomarkers (and therefore features for building a regression model). In this tutorial, specific biomarkers are analyzed by machine learning algorithms to create an age prediction model. Multiple visualisations are also used to analyse the predictions made by simple and complex regressors and hyperparameters of one of the regressors also optimised.

• Tutorial: Version in Video | Latest Version
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• Regression in Machine Learning

• Slack: #machine_learning_regression_machinelearning
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• Learn regression background
• Apply regression based machine learning algorithms
• Learn ageing biomarkers and predict age from DNA methylation datasets
• Learn how visualizations can be used to analyze predictions

In this tutorial, we will use de novo transcript reconstruction to infer transcript structures from aligned reads. We will identify and quantify transcripts present in two different cell state and determine which transcripts are differentially expressed between the two states.

• Tutorial: Version in Video | Latest Version
  FAQs
• Dynamic shifts in occupancy by TAL1 are guided by GATA factors and drive large-scale reprogramming of gene expression during hematopoiesis.
• Zenodo

• Slack: #transcriptomics_de-novo
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• Analysis of RNA sequencing data using a reference genome
• Reconstruction of transcripts without reference transcriptome (de novo)
• Analysis of differentially expressed genes

This tutorial will take you from raw FASTQ files to a cell x gene data matrix in AnnData format. What’s a data matrix, and what’s AnnData format? Well you’ll find out! Importantly, this is the first step in processing single cell data in order to start analysing it.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_droplet-quantification-preprocessing
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• Repeat matrix generation for any droplet-based single cell sequencing data
• Apply data combination and metadata editing for particular experimental designs
• Interpret quality control (QC) plots to make informed decisions on cell thresholds
• Find relevant information in GTF files for the particulars of their study, and include this in data matrix metadata

RNA sequencing is used to assess the expression levels of genes. This video introduces the important concepts related to RNA-seq analysis.

• Slides: Version in Video | Latest Version

• Slack: #transcriptomics_introduction
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• Slides: Version in Video | Latest Version

• Slack: #transcriptomics_mirna-target-finder
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• Perform miRNA differential expression analysis
• Understand the quasi-mapping-based Salmon method for quantifying the expression of transcripts using RNA-Seq data
• Idenfity potential miRNAs involved in brassinosteroid-mediated regulation networks

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_mirna-target-finder
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• Perform miRNA differential expression analysis
• Understand the quasi-mapping-based Salmon method for quantifying the expression of transcripts using RNA-Seq data
• Idenfity potential miRNAs involved in brassinosteroid-mediated regulation networks

In this tutorial we will walk you through the process of an RNA-seq analysis, comparing gene expression levels between different conditions and assessing impacted gene pathways.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_ref-based
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• Check a sequence quality report generated by FastQC for RNA-Seq data
• Explain the principle and specificity of mapping of RNA-Seq data to an eukaryotic reference genome
• Select and run a state of the art mapping tool for RNA-Seq data
• Evaluate the quality of mapping results
• Describe the process to estimate the library strandness
• Estimate the number of reads per genes
• Explain the count normalization to perform before sample comparison
• Construct and run a differential gene expression analysis
• Analyze the DESeq2 output to identify, annotate and visualize differentially expressed genes
• Perform a gene ontology enrichment analysis
• Perform and visualize an enrichment analysis for KEGG pathways

In this tutorial, we will visualize our RNA-seq analysis results using R.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_rna-seq-counts-to-viz-in-r
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• Describe the role of data, aesthetics, geoms, and layers in `ggplot` functions.
• Customize plot scales, titles, subtitles, themes, fonts, layout, and orientation.

The previous 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.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_rna-seq-viz-with-volcanoplot
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• Learn how to use R to edit Volcano plot colours, points, labels and categories

This tutorial will show you how you can create volcano plots from your RNA-Seq analysis results using Galaxy tools.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_rna-seq-viz-with-volcanoplot
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• Create a volcano plot of RNA-seq data to visualize significant genes

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.

In this tutorial, we will use bulk and single-cell RNA-seq data, including matrices of similar tissues from different sources, to illustrate how to infer cell type abundances from bulk RNA-seq.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_bulk-music
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• Construct Bulk and scRNA Expression Set Objects
• Inspect these objects for various properties
• Measure the abundance of certain cell type cluster markers compared to others

You’ve done all the hard work of preparing a single cell matrix, processing it, plotting it, interpreting it, finding lots of lovely genes, all within the glorious Galaxy interface. Now you want to infer trajectories, or relationships between cells… and you’ve been threatened with learning Python to do so! Well, fear not. If you can have a run-through of a basic python coding introduction, then that will help you make more sense of this tutorial, however you’ll be able to make and interpret glorious plots even without understanding the Python coding language. This is the beauty of Galaxy - all the ‘set-up’ is identical across computers, because it’s browser based. So fear not!

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_scrna-JUPYTER-trajectories
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• Execute multiple plotting methods designed to maintain lineage relationships between cells
• Interpret these plots

Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies and rare transcript studies to examine cell heterogeneity within a populations of cells. The cellular resolution and genome wide scope make it possible to draw new conclusions that are not otherwise possible with bulk RNA-seq. Slides created by Mehmet Tekman.

• Slides: Version in Video | Latest Version

• Slack: #transcriptomics_scrna-intro
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• To understand the pitfalls in scRNA-seq sequencing and amplification, and how they are overcome.
• Know the types of variation in an analysis and how to control for them.
• Grasp what dimension reduction is, and how it might be performed.
• Be familiarised with the main types of clustering techniques and when to use them.

This tutorial demonstrates the what barcodes in your sequencing data are, and how to handle them during analysis. Please work through the full tutorial yourself to get all the details.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_scrna-umis
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• Demultiplex FASTQ data via UMI-tools
• Understand and validate the extraction of barcodes

10x genomics has provided not only a cost-effective high-throughput solution to understanding sample heterogeneity at the individual cell level, but has defined the standards of the field that many downstream analysis packages are now scrambling to accommodate. Please work through the full tutorial yourself to get all the details.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_scrna-preprocessing-tenx
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• Demultiplex single-cell FASTQ data from 10X Genomics
• Learn about transparent matrix formats
• Understand the importance of high and low quality cells

In this tutorial, we will investigate clustering of single-cell data from 10x Genomics, including preprocessing, clustering and the identification of cell types via known marker genes, using Scanpy. It will be illustrated using a dataset of Peripheral Blood Mononuclear Cells (PBMC), containing 2,700 single cells. Please work through the full tutorial yourself to get all the details.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_scrna-scanpy-pbmc3k
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• Describe an AnnData object to store single-cell data
• Explain the preprocessing steps for single-cell data
• Evaluate quality of single-cell data and apply steps to select and filter cells and genes based on QC
• Execute data normalization and scaling
• Identify highly variable genes
• Construct and run a dimensionality reduction using Principal Component Analysis
• Perform a graph-based clustering for cells
• Identify marker genes for the clusters
• Construct and run a cell type annotation for the clusters

This tutorial replicates the paper “Spatiotemporal Developmental Trajectories in the Arabidopsis Root Revealed Using High-Throughput Single-Cell RNA Sequencing” (Denyer et al. 2019), where the major plant cell types are recovered in the data as well as distinguishing between QC and meristematic cells. The original paper used the Seurat analysis suite (Satija et al. 2015), but here we will use the ScanPy analysis suite (Wolf et al. 2018) integrated within the single-cell resources in Galaxy (Tekman et al. 2020).

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_scrna-plant
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• Perform filtering, dimensionality reduction, and clustering
• Generate a DotPlot emulating the original paper using a different analysis tool
• Determine robust clusters across scRNA-seq pipelines

You’ve done all the work to make a single cell matrix, with gene counts and mitochondrial counts and buckets of cell metadata from all your variables of interest, now it’s time to fully process our data, to remove low quality cells, to reduce the many dimensions of data that make it difficult to work with, and ultimately to try to define our clusters and to find our biological meaning and insights!

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_scrna-seq-basic-pipeline
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• Interpret quality control plots to direct parameter decisions
• Repeat analysis from matrix to clustering
• Identify decision-making points
• Appraise data outputs and decisions
• Explain why single cell analysis is an iterative (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly) process

The previous 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.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_rna-seq-viz-with-volcanoplot
  • Slack Invite
• Gitter
• Galaxy Help Forum

• Learn how to use R to edit Volcano plot colours, points, labels and categories

This tutorial will show you how you can create volcano plots from your RNA-Seq analysis results using Galaxy tools.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_rna-seq-viz-with-volcanoplot
  • Slack Invite
• Gitter
• Galaxy Help Forum

• Create a volcano plot of RNA-seq data to visualize significant genes

Circos is a popular tool for creating circular graphs to display genomic data. This video will introduce this tool and how to use it within Galaxy

• Slides: Version in Video | Latest Version

• Slack: #visualisation_circos
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• Create a number of Circos plots using the Galaxy tool
• Familiarise yourself with the various different track types

Circos is a popular tool for creating circular graphs to display genomic data. This video will introduce this tool and how to use it within Galaxy

• Slides: Version in Video | Latest Version
• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #visualisation_circos
  • Slack Invite
• Gitter
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• Create a number of Circos plots using the Galaxy tool
• Familiarise yourself with the various different track types

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.

In this tutorial, we will use bulk and single-cell RNA-seq data, including matrices of similar tissues from different sources, to illustrate how to infer cell type abundances from bulk RNA-seq.

• Tutorial: Version in Video | Latest Version
  FAQs

• Slack: #transcriptomics_bulk-music
  • Slack Invite
• Gitter
• Galaxy Help Forum

• Construct Bulk and scRNA Expression Set Objects
• Inspect these objects for various properties
• Measure the abundance of certain cell type cluster markers compared to others

You’ve done all the hard work of preparing a single cell matrix, processing it, plotting it, interpreting it, finding lots of lovely genes, all within the glorious Galaxy interface. Now you want to infer trajectories, or relationships between cells… and you’ve been threatened with learning Python to do so! Well, fear not. If you can have a run-through of a basic python coding introduction, then that will help you make more sense of this tutorial, however you’ll be able to make and interpret glorious plots even without understanding the Python coding language. This is the beauty of Galaxy - all the ‘set-up’ is identical across computers, because it’s browser based. So fear not!

• Tutorial: Version in Video | Latest Version
  FAQs