Björn Grüning
Affiliations
Contributions
The following list includes only slides and tutorials where the individual or organisation has been added to the contributor list. This may not include the sum total of their contributions to the training materials (e.g. GTN css or design, tutorial datasets, workflow development, etc.) unless described by a news post.
Editorial Roles
This contributor has taken on additional responsibilities as an editor for the following topics. They are responsible for ensuring that the content is up to date, accurate, and follows GTN best practices.
- Topic: Galaxy Server administration
- Topic: Computational chemistry
- Topic: Contributing to the Galaxy Training Material
- Topic: Proteomics
- Topic: Variant Analysis
Tutorials
- Proteomics / Clinical Metaproteomics 4: Quantitation 🧐
- Proteomics / Secretome Prediction ✍️ 🧐
- Proteomics / Proteogenomics 2: Database Search 🧐
- Proteomics / DIA Analysis using OpenSwathWorkflow 🧐
- Proteomics / Clinical Metaproteomics 5: Data Interpretation 🧐
- Proteomics / Library Generation for DIA Analysis 🧐
- Proteomics / MaxQuant and MSstats for the analysis of label-free data 🧐
- Proteomics / Annotating a protein list identified by LC-MS/MS experiments 🧐
- Proteomics / Label-free data analysis using MaxQuant 🧐
- Proteomics / Multiomics data analysis using MultiGSEA 🧐
- Proteomics / Clinical Metaproteomics 2: Discovery 🧐
- Proteomics / Peptide and Protein ID using SearchGUI and PeptideShaker ✍️ 🧐
- Proteomics / Peptide and Protein ID using OpenMS tools ✍️ 🧐
- Proteomics / Machine Learning Modeling of Anticancer Peptides 🧐
- Proteomics / Peptide Library Data Analysis 🧐
- Proteomics / EncyclopeDIA 🧐
- Proteomics / Label-free versus Labelled - How to Choose Your Quantitation Method ✍️ 🧐
- Proteomics / Metaproteomics tutorial 🧐
- Proteomics / Proteogenomics 3: Novel peptide analysis 🧐
- Proteomics / metaQuantome 3: Taxonomy 🧐
- Proteomics / Peptide and Protein Quantification via Stable Isotope Labelling (SIL) ✍️ 🧐
- Proteomics / Statistical analysis of DIA data 🧐
- Proteomics / Clinical Metaproteomics 1: Database-Generation 🧐
- Proteomics / metaQuantome 1: Data creation 🧐
- Proteomics / Mass spectrometry imaging: Loading and exploring MSI data ✍️ 🧐
- Proteomics / Detection and quantitation of N-termini (degradomics) via N-TAILS ✍️ 🧐
- Proteomics / metaQuantome 2: Function 🧐
- Proteomics / MaxQuant and MSstats for the analysis of TMT data 🧐
- Proteomics / Clinical Metaproteomics 3: Verification 🧐
- Proteomics / Protein FASTA Database Handling ✍️ 🧐
- Proteomics / Proteogenomics 1: Database Creation 🧐
- Metabolomics / Mass spectrometry imaging: Examining the spatial distribution of analytes 🧐
- Metabolomics / Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms) 🧐
- Metabolomics / Mass spectrometry: LC-MS preprocessing with XCMS 🧐
- Metabolomics / Mass spectrometry imaging: Finding differential analytes 🧐
- Metabolomics / Mass spectrometry: LC-MS analysis 🧐
- Sequence analysis / Quality and contamination control in bacterial isolate using Illumina MiSeq Data 🧐
- Sequence analysis / NCBI BLAST+ against the MAdLand 🧐
- Sequence analysis / Quality Control 🧐
- Sequence analysis / Mapping 🧐
- Evolution / Tree thinking for tuberculosis evolution and epidemiology 🧐
- Evolution / Identifying tuberculosis transmission links: from SNPs to transmission clusters 🧐
- Introduction to Galaxy Analyses / From peaks to genes ✍️ 🧐
- Introduction to Galaxy Analyses / IGV Introduction 🧐
- Introduction to Galaxy Analyses / How to reproduce published Galaxy analyses 🧐
- Introduction to Galaxy Analyses / NGS data logistics 🧐
- Introduction to Galaxy Analyses / A short introduction to Galaxy 🧐
- Introduction to Galaxy Analyses / Introduction to Genomics and Galaxy 🧐
- Introduction to Galaxy Analyses / Galaxy Basics for everyone 🧐
- Introduction to Galaxy Analyses / Data Manipulation Olympics 🧐
- Introduction to Galaxy Analyses / Galaxy Basics for genomics ✍️ 🧐
- Contributing to the Galaxy Training Material / Principles of learning and how they apply to training and teaching 🧐
- Contributing to the Galaxy Training Material / Contributing with GitHub via its interface 🧐
- Contributing to the Galaxy Training Material / FAIR-by-Design methodology 🧐
- Contributing to the Galaxy Training Material / Preview the GTN website as you edit your training material ✍️ 🧐
- Contributing to the Galaxy Training Material / Including a new topic 🧐
- Contributing to the Galaxy Training Material / GTN Metadata 🧐
- Contributing to the Galaxy Training Material / Creating a new tutorial 🧐
- Contributing to the Galaxy Training Material / Adding auto-generated video to your slides 🧐
- Contributing to the Galaxy Training Material / Design and plan session, course, materials 🧐
- Contributing to the Galaxy Training Material / Updating diffs in admin training 🧐
- Contributing to the Galaxy Training Material / Teaching Python 🧐
- Contributing to the Galaxy Training Material / Creating content in Markdown 📝 🧐
- Contributing to the Galaxy Training Material / Tools, Data, and Workflows for tutorials ✍️ 🧐
- Contributing to the Galaxy Training Material / Adding Quizzes to your Tutorial 🧐
- Contributing to the Galaxy Training Material / Contributing to the Galaxy Training Network with GitHub 🧐
- Contributing to the Galaxy Training Material / Creating Interactive Galaxy Tours ✍️ 🧐
- Teaching and Hosting Galaxy training / Organizing a workshop 🧐
- Teaching and Hosting Galaxy training / Running a workshop as instructor ✍️ 🧐
- Teaching and Hosting Galaxy training / Training Infrastructure as a Service 🧐
- Teaching and Hosting Galaxy training / Motivation and Demotivation 🧐
- Teaching and Hosting Galaxy training / Teaching online 🧐
- Teaching and Hosting Galaxy training / Assessment and feedback in training and teachings 🧐
- Teaching and Hosting Galaxy training / Hybrid training 🧐
- Teaching and Hosting Galaxy training / Set up a Galaxy for Training 🧐
- Teaching and Hosting Galaxy training / Galaxy Admin Training 🧐
- Teaching and Hosting Galaxy training / Training techniques to enhance learner participation and engagement 🧐
- Epigenetics / Infinium Human Methylation BeadChip 🧐
- Epigenetics / CUT&RUN data analysis 🧐
- Epigenetics / ATAC-Seq data analysis 🧐
- Epigenetics / Identification of the binding sites of the T-cell acute lymphocytic leukemia protein 1 (TAL1) 🧐
- Epigenetics / Identification of the binding sites of the Estrogen receptor 🧐
- Epigenetics / DNA Methylation data analysis 🧐
- Epigenetics / Hi-C analysis of Drosophila melanogaster cells using HiCExplorer 🧐
- Epigenetics / Formation of the Super-Structures on the Inactive X 🧐
- Ecology / Regional GAM 🧐
- Ecology / RAD-Seq to construct genetic maps 🧐
- Ecology / RAD-Seq de-novo data analysis 🧐
- Ecology / Cleaning GBIF data using OpenRefine 🧐
- Ecology / QGIS Web Feature Services 🧐
- Ecology / Obis marine indicators 🧐
- Ecology / From NDVI data with OpenEO to time series visualisation with Holoviews 🧐
- Ecology / RAD-Seq Reference-based data analysis 🧐
- Ecology / Life Traits Ecoregionalization workflow 🧐
- Ecology / Marine Omics identifying biosynthetic gene clusters 🧐
- Ecology / Creating metadata using Ecological Metadata Language (EML) standard with EML Assembly Line functionalities 🧐
- Ecology / Ecoregionalization workflow tutorial 🧐
- Ecology / Compute and analyze biodiversity metrics with PAMPA toolsuite 🧐
- Ecology / Creating FAIR Quality assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS 🧐
- Digital Humanities / Text-Mining Differences in Chinese Newspaper Articles 🧐
- Variant Analysis / Exome sequencing data analysis for diagnosing a genetic disease ✍️ 🧐
- Variant Analysis / Mapping and molecular identification of phenotype-causing mutations 🧐
- Variant Analysis / Microbial Variant Calling 🧐
- Variant Analysis / Avian influenza viral strain analysis from gene segment sequencing data 🧐
- Variant Analysis / Deciphering Virus Populations - Single Nucleotide Variants (SNVs) and Specificities in Baculovirus Isolates 🧐
- Variant Analysis / Mutation calling, viral genome reconstruction and lineage/clade assignment from SARS-CoV-2 sequencing data 🧐
- Variant Analysis / Identification of somatic and germline variants from tumor and normal sample pairs 🧐
- Variant Analysis / Somatic Variant Discovery from WES Data Using Control-FREEC 🧐
- Variant Analysis / Calling variants in diploid systems 🧐
- Variant Analysis / Trio Analysis using Synthetic Datasets from RD-Connect GPAP 🧐
- Variant Analysis / From NCBI's Sequence Read Archive (SRA) to Galaxy: SARS-CoV-2 variant analysis 🧐
- Variant Analysis / M. tuberculosis Variant Analysis 🧐
- Variant Analysis / Calling very rare variants 🧐
- Variant Analysis / Calling variants in non-diploid systems 🧐
- Visualisation / Genomic Data Visualisation with JBrowse 🧐
- Visualisation / Ploting a Microbial Genome with Circos 🧐
- Visualisation / Visualisation with Circos 🧐
- Single Cell / Generating a single cell matrix using Alevin and combining datasets (bash + R) 🧐
- Single Cell / Filter, plot, and explore single cell RNA-seq data with Seurat (R) 🧐
- Single Cell / Understanding Barcodes 🧐
- Single Cell / Clustering 3K PBMCs with Scanpy 🧐
- Single Cell / Clustering 3K PBMCs with Seurat 🧐
- Single Cell / Comparing inferred cell compositions using MuSiC deconvolution 🧐
- Single Cell / Importing files from public atlases 🧐
- Single Cell / Filter, plot and explore single-cell RNA-seq data with Scanpy (Python) 🧐
- Single Cell / Inferring single cell trajectories with Scanpy (Python) 🧐
- Single Cell / Removing the effects of the cell cycle 🧐
- Single Cell / Analysis of plant scRNA-Seq Data with Scanpy 🧐
- Single Cell / Pre-processing of 10X Single-Cell ATAC-seq Datasets 🧐
- Single Cell / Inferring single cell trajectories with Monocle3 (R) 🧐
- Single Cell / Scanpy Parameter Iterator 🧐
- Single Cell / Evaluating Reference Data for Bulk RNA Deconvolution 🧐
- Single Cell / Converting NCBI Data to the AnnData Format 🧐
- Single Cell / Single-cell ATAC-seq standard processing with SnapATAC2 📝 🧐
- Single Cell / Bulk RNA Deconvolution with MuSiC 🧐
- Single Cell / Inferring single cell trajectories with Scanpy 🧐
- Single Cell / Pre-processing of Single-Cell RNA Data 🧐
- Single Cell / Pseudobulk Analysis with Decoupler and EdgeR 🧐
- Single Cell / Downstream Single-cell RNA analysis with RaceID 🧐
- Single Cell / Single-cell quality control with scater 🧐
- Single Cell / Combining single cell datasets after pre-processing 🧐
- Single Cell / Inferring single cell trajectories with Monocle3 🧐
- Single Cell / Filter, plot and explore single-cell RNA-seq data with Scanpy 🧐
- Single Cell / GO Enrichment Analysis on Single-Cell RNA-Seq Data 📝 🧐
- Single Cell / Generating a single cell matrix using Alevin 🧐
- Single Cell / Converting between common single cell data formats 🧐
- Single Cell / Pre-processing of 10X Single-Cell RNA Datasets 🧐
- Single Cell / Filter, plot, and explore single cell RNA-seq data with Seurat 🧐
- Galaxy Server administration / Customizing the look of Galaxy 🧐
- Galaxy Server administration / Reference Data with CVMFS 🧐
- Galaxy Server administration / Ansible 🧐
- Galaxy Server administration / External Authentication 🧐
- Galaxy Server administration / Galaxy Monitoring with gxadmin 🧐
- Galaxy Server administration / Training Infrastructure as a Service (TIaaS) 🧐
- Galaxy Server administration / Setting up Celery Workers for Galaxy 🧐
- Galaxy Server administration / Distributed Object Storage 🧐
- Galaxy Server administration / Upgrading Galaxy 🧐
- Galaxy Server administration / Galaxy Monitoring with Telegraf and Grafana 🧐
- Galaxy Server administration / Configuring the Onedata connectors (remotes, Object Store, BYOS, BYOD) 🧐
- Galaxy Server administration / Deploying a compute cluster in OpenStack via Terraform 🧐
- Galaxy Server administration / Customizing the look of Galaxy (Manual) 🧐
- Galaxy Server administration / Create a subdomain for your community on UseGalaxy.eu 🧐
- Galaxy Server administration / Reference Data with CVMFS without Ansible 🧐
- Galaxy Server administration / Managing Galaxy on Kubernetes 🧐
- Galaxy Server administration / Adding file-sources to Galaxy 🧐
- Galaxy Server administration / Use Apptainer containers for running Galaxy jobs 🧐
- Galaxy Server administration / Connecting Galaxy to a compute cluster ✍️ 🧐
- Galaxy Server administration / Mapping Jobs to Destinations using TPV ✍️ 🧐
- Galaxy Server administration / Galaxy Tool Management with Ephemeris 🧐
- Galaxy Server administration / Galaxy Installation on Kubernetes 🧐
- Galaxy Server administration / Galaxy Interactive Tools 🧐
- Galaxy Server administration / Galaxy Installation with Ansible 🧐
- Galaxy Server administration / Data Libraries 🧐
- Galaxy Server administration / Running Jobs on Remote Resources with Pulsar 🧐
- Galaxy Server administration / Galaxy Database schema ✍️ 🧐
- Galaxy Server administration / Automation with Jenkins 🧐
- Galaxy Server administration / Galaxy Monitoring with Reports ✍️ 🧐
- Materials Science / Finding the muon stopping site with pymuon-suite in Galaxy 🧐
- Imaging / Analyse HeLa fluorescence siRNA screen 🧐
- Imaging / Introduction to Image Analysis using Galaxy 🧐
- Imaging / Quantification of single-molecule RNA fluorescence in situ hybridization (smFISH) in yeast cell lines 🧐
- Imaging / Tracking of mitochondria and capturing mitoflashes 🧐
- Imaging / Voronoi segmentation 🧐
- Imaging / Nucleoli segmentation and feature extraction using CellProfiler 🧐
- Imaging / Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy 📝 🧐
- Imaging / Using BioImage.IO models for image analysis in Galaxy 🧐
- FAIR Data, Workflows, and Research / Persistent Identifiers 🧐
- FAIR Data, Workflows, and Research / Metadata 🧐
- FAIR Data, Workflows, and Research / Exporting Workflow Run RO-Crates from Galaxy 🧐
- FAIR Data, Workflows, and Research / Data Registration 🧐
- FAIR Data, Workflows, and Research / FAIR and its Origins 🧐
- FAIR Data, Workflows, and Research / Access 🧐
- FAIR Data, Workflows, and Research / DataPLANT ARCs 🧐
- FAIR Data, Workflows, and Research / RO-Crate - Introduction 🧐
- FAIR Data, Workflows, and Research / RO-Crate in Python 🧐
- FAIR Data, Workflows, and Research / Best practices for workflows in GitHub repositories 🧐
- Transcriptomics / Differential abundance testing of small RNAs 🧐
- Transcriptomics / RNA Seq Counts to Viz in R 🧐
- Transcriptomics / Reference-based RNA-Seq data analysis 🧐
- Transcriptomics / RNA-Seq analysis with AskOmics Interactive Tool 🧐
- Transcriptomics / GO Enrichment Analysis 🧐
- Transcriptomics / 1: RNA-Seq reads to counts 🧐
- Transcriptomics / Whole transcriptome analysis of Arabidopsis thaliana 🧐
- Transcriptomics / Pathway analysis with the MINERVA Platform 🖥 🧐
- Transcriptomics / Genome-wide alternative splicing analysis 🧐
- Transcriptomics / 2: RNA-seq counts to genes 🧐
- Transcriptomics / RNA-RNA interactome data analysis 🧐
- Transcriptomics / CLIP-Seq data analysis from pre-processing to motif detection 🧐
- Transcriptomics / Visualization of RNA-Seq results with Volcano Plot 🧐
- Transcriptomics / Reference-based RNAseq data analysis (long) 🧐
- Transcriptomics / Visualization of RNA-Seq results with heatmap2 🧐
- Transcriptomics / De novo transcriptome assembly, annotation, and differential expression analysis 🧐
- Transcriptomics / 3: RNA-seq genes to pathways 🧐
- Transcriptomics / De novo transcriptome reconstruction with RNA-Seq 🧐
- Statistics and machine learning / Introduction to Machine Learning using R 🧐
- Statistics and machine learning / Prepare data from CbioPortal for Flexynesis integration
- Statistics and machine learning / Clustering in Machine Learning 🧐
- Statistics and machine learning / Unsupervised Analysis of Bone Marrow Cells with Flexynesis
- Statistics and machine learning / Pretraining a Large Language Model (LLM) from Scratch on DNA Sequences 🧐
- Statistics and machine learning / Supervised Learning with Hyperdimensional Computing 🧐
- Statistics and machine learning / Interval-Wise Testing for omics data 🧐
- Statistics and machine learning / Classification in Machine Learning 🧐
- Statistics and machine learning / Fine-tuning a LLM for DNA Sequence Classification 🧐
- Statistics and machine learning / Predicting Mutation Impact with Zero-shot Learning using a pretrained DNA LLM 🧐
- Statistics and machine learning / Generating Artificial Yeast DNA Sequences using a DNA LLM 🧐
- Statistics and machine learning / Optimizing DNA Sequences for Biological Functions using a DNA LLM 🧐
- Statistics and machine learning / Machine learning: classification and regression 🧐
- Statistics and machine learning / Introduction to deep learning 🧐
- Statistics and machine learning / Fine tune large protein model (ProtTrans) using HuggingFace 🧐
- Statistics and machine learning / Regression in Machine Learning 🧐
- Statistics and machine learning / A Docker-based interactive Jupyterlab powered by GPU for artificial intelligence in Galaxy 🧐
- Statistics and machine learning / Age prediction using machine learning 🧐
- Statistics and machine learning / Building the LORIS LLR6 PanCancer Model Using PyCaret 🧐
- Statistics and machine learning / Text-mining with the SimText toolset 🧐
- Statistics and machine learning / Basics of machine learning 🧐
- Statistics and machine learning / Deep Learning (Part 3) - Convolutional neural networks (CNN) 🧐
- Computational chemistry / Analysis of molecular dynamics simulations 🧐
- Computational chemistry / Running molecular dynamics simulations using NAMD 🧐
- Computational chemistry / Protein-ligand docking 🧐
- Computational chemistry / Setting up molecular systems 🧐
- Computational chemistry / Virtual screening of the SARS-CoV-2 main protease with rxDock and pose scoring 🧐
- Computational chemistry / Running molecular dynamics simulations using GROMACS 🧐
- Computational chemistry / High Throughput Molecular Dynamics and Analysis ✍️ 🧐
- Microbiome / Antibiotic resistance detection 🧐
- Microbiome / Assembly of metagenomic sequencing data 🧐
- Microbiome / Metatranscriptomics analysis using microbiome RNA-seq data (short) 🧐
- Microbiome / Calculating α and β diversity from microbiome taxonomic data 🧐
- Microbiome / 16S Microbial analysis with Nanopore data 🧐
- Microbiome / Analyses of metagenomics data - The global picture 🧐
- Microbiome / Pathogen detection from (direct Nanopore) sequencing data using Galaxy - Foodborne Edition 🧐
- Microbiome / 16S Microbial Analysis with mothur (extended) 🧐
- Microbiome / Query an annotated mobile genetic element database to identify and annotate genetic elements (e.g. plasmids) in metagenomics data 🧐
- Microbiome / Metatranscriptomics analysis using microbiome RNA-seq data 🧐
- Using Galaxy and Managing your Data / Automating Galaxy workflows using the command line 🧐
- Using Galaxy and Managing your Data / Creating, Editing and Importing Galaxy Workflows 🧐
- Using Galaxy and Managing your Data / Extracting Workflows from Histories 🧐
- Using Galaxy and Managing your Data / Creating high resolution images of Galaxy Workflows 🧐
- Using Galaxy and Managing your Data / Getting started with Onedata distributed storage 🧐
- Using Galaxy and Managing your Data / Exporting to Onedata remote 🧐
- Using Galaxy and Managing your Data / Name tags for following complex histories 🧐
- Using Galaxy and Managing your Data / Importing (uploading) data from Onedata 🧐
- Using Galaxy and Managing your Data / SRA Aligned Read Format to Speed Up SARS-CoV-2 data Analysis 🧐
- Using Galaxy and Managing your Data / JupyterLab in Galaxy 🧐
- Using Galaxy and Managing your Data / Group tags for complex experimental designs 🧐
- Using Galaxy and Managing your Data / Understanding Galaxy history system 📝 🧐
- Using Galaxy and Managing your Data / Rule Based Uploader: Advanced 🧐
- Using Galaxy and Managing your Data / Downloading and Deleting Data in Galaxy 🧐
- Using Galaxy and Managing your Data / Use Jupyter notebooks in Galaxy 🧐
- Using Galaxy and Managing your Data / Using dataset collections 🧐
- Using Galaxy and Managing your Data / Using Workflow Parameters 🧐
- Using Galaxy and Managing your Data / RStudio in Galaxy 🧐
- Using Galaxy and Managing your Data / Annotate, prepare tests and publish Galaxy workflows in workflow registries 🧐
- Using Galaxy and Managing your Data / Onedata user-owned storage 🧐
- Using Galaxy and Managing your Data / Submitting sequence data to ENA 🧐
- Development in Galaxy / ToolFactory: Generating Tools From Simple Scripts 🧐
- Development in Galaxy / Contributing to BioBlend as a developer 🧐
- Development in Galaxy / Galaxy Webhooks ✍️ 🧐
- Development in Galaxy / JavaScript plugins ✍️ 🧐
- Development in Galaxy / Generic plugins 🧐
- Development in Galaxy / Setting up a dev Onedata instance 🧐
- Development in Galaxy / Galaxy Interactive Tools 🧐
- Development in Galaxy / Debugging Galaxy 🧐
- Development in Galaxy / Data source integration 🧐
- Assembly / ERGA post-assembly QC 🧐
- Assembly / Genome Assembly of a bacterial genome (MRSA) sequenced using Illumina MiSeq Data 🧐
- Assembly / Decontamination of a genome assembly 🧐
- Assembly / Chloroplast genome assembly 🧐
- Assembly / Genome Assembly Quality Control 🧐
- Assembly / Vertebrate genome assembly using HiFi, Bionano and Hi-C data - Step by Step 🧐
- Assembly / Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads 🧐
- Assembly / Genome assembly using PacBio data 🧐
- Assembly / An Introduction to Genome Assembly 🧐
- Assembly / Assembly of the mitochondrial genome from PacBio HiFi reads 🧐
- Assembly / De Bruijn Graph Assembly 🧐
- Assembly / Genome Assembly of MRSA from Oxford Nanopore MinION data (and optionally Illumina data) 🧐
- Assembly / Unicycler Assembly 🧐
- Assembly / Using the VGP workflows to assemble a vertebrate genome with HiFi and Hi-C data 🧐
- Assembly / Making sense of a newly assembled genome 🧐
- Climate / Ocean's variables study 🧐
- Climate / Functionally Assembled Terrestrial Ecosystem Simulator (FATES) 🧐
- Climate / Visualize Climate data with Panoply netCDF viewer 🧐
- Climate / Getting your hands-on climate data 🧐
- Climate / Functionally Assembled Terrestrial Ecosystem Simulator (FATES) with Galaxy Climate JupyterLab 🧐
- Climate / Analyse Argo data 🧐
- Climate / Ocean Data View (ODV) 🧐
- Climate / Collaboration with JupyterGIS 🧐
- Galaxy Community Building / Make your tools available on your subdomain 🧐
- Foundations of Data Science / Introduction to sequencing with Python (part three) 🧐
- Foundations of Data Science / Versioning your code and data with git 🧐
- Foundations of Data Science / Data manipulation with Pandas 🧐
- Foundations of Data Science / A (very) brief history of genomics 🧐
- Foundations of Data Science / Introduction to sequencing with Python (part four) 🧐
- Foundations of Data Science / Introduction to sequencing with Python (part two) 🧐
- Foundations of Data Science / SQL Educational Game - Murder Mystery 🧐
- Foundations of Data Science / Make & Snakemake 🧐
- Foundations of Data Science / Introduction to sequencing with Python (part one) 🧐
- Genome Annotation / Functional annotation of protein sequences 🧐
- Genome Annotation / Genome annotation with Maker (short) 🧐
- Genome Annotation / Refining Genome Annotations with Apollo (prokaryotes) 🧐
- Genome Annotation / Bacterial Genome Annotation 🧐
- Genome Annotation / Long non-coding RNAs (lncRNAs) annotation with FEELnc 🧐
- Genome Annotation / Identification of AMR genes in an assembled bacterial genome 🧐
- Genome Annotation / Refining Genome Annotations with Apollo (eukaryotes) 🧐
- Genome Annotation / Essential genes detection with Transposon insertion sequencing 🧐
- Genome Annotation / Genome annotation with Funannotate 🧐
- Genome Annotation / Genome annotation with Helixer 🧐
- Genome Annotation / Genome annotation with Prokka 🧐
- Genome Annotation / Genome annotation with Maker 🧐
- Genome Annotation / Comparison of two annotation tools - Helixer and Braker3 🧐
- Genome Annotation / CRISPR screen analysis 🧐
- Genome Annotation / Masking repeats with RepeatMasker 🧐
- Genome Annotation / Genome Annotation ✍️ 🧐
Slides
- Materials Science / Introduction to Muon Spectroscopy 🧐
- Development in Galaxy / Galaxy from a developer point of view 🧐
- Proteomics / Introduction to proteomics, protein identification, quantification and statistical modelling 🧐
- Metabolomics / Mass spectrometry: LC-MS preprocessing - advanced 🧐
- Sequence analysis / Quality Control 🧐
- Sequence analysis / Mapping 🧐
- Introduction to Galaxy Analyses / Introduction to Galaxy 🧐
- Introduction to Galaxy Analyses / Options for using Galaxy 🧐
- Introduction to Galaxy Analyses / A Short Introduction to Galaxy 🧐
- Contributing to the Galaxy Training Material / Overview of the Galaxy Training Material 🧐
- Contributing to the Galaxy Training Material / Contributing with GitHub via command-line 🧐
- Contributing to the Galaxy Training Material / Creating Slides 🧐
- Teaching and Hosting Galaxy training / Overview of the Galaxy Training Material for Instructors 🧐
- Epigenetics / EWAS Epigenome-Wide Association Studies Introduction 🧐
- Epigenetics / Introduction to DNA Methylation data analysis 🧐
- Epigenetics / Introduction to ChIP-Seq data analysis 🧐
- Epigenetics / ChIP-seq data analysis 🧐
- Variant Analysis / Introduction to Variant analysis 🧐
- Visualisation / Visualisations in Galaxy 🧐
- Visualisation / JBrowse 🧐
- Visualisation / Circos 🧐
- Single Cell / Automated Cell Annotation 🧐
- Single Cell / Clustering 3K PBMCs with Scanpy 🧐
- Single Cell / Single-cell Formats and Resources 🧐
- Single Cell / Plates, Batches, and Barcodes 🧐
- Single Cell / An introduction to scRNA-seq data analysis 🧐
- Single Cell / Dealing with Cross-Contamination in Fixed Barcode Protocols 🧐
- Single Cell / Trajectory analysis 🧐
- Single Cell / GO Enrichment Analysis on Single-Cell RNA-Seq Data 🧐
- Galaxy Server administration / Server Maintenance: Cleanup, Backup, and Restoration ✍️ 🧐
- Galaxy Server administration / Reference Data with CVMFS 🧐
- Galaxy Server administration / Ansible 🧐
- Galaxy Server administration / Galaxy Monitoring with gxadmin ✍️ 🧐
- Galaxy Server administration / Storage Management 🧐
- Galaxy Server administration / Galaxy from an administrator's point of view ✍️ 🧐
- Galaxy Server administration / Galaxy Monitoring with Telegraf and Grafana ✍️ 🧐
- Galaxy Server administration / Terraform 🧐
- Galaxy Server administration / Advanced customisation of a Galaxy instance ✍️ 🧐
- Galaxy Server administration / uWSGI 🧐
- Galaxy Server administration / Connecting Galaxy to a compute cluster ✍️ 🧐
- Galaxy Server administration / Galaxy on the Cloud 🧐
- Galaxy Server administration / Galaxy Tool Management with Ephemeris 🧐
- Galaxy Server administration / Gearing towards production 🧐
- Galaxy Server administration / Galaxy Interactive Tools 🧐
- Galaxy Server administration / Empathy 🧐
- Galaxy Server administration / User, Role, Group, Quota, and Authentication managment ✍️ 🧐
- Galaxy Server administration / Galaxy Troubleshooting 🧐
- Galaxy Server administration / Reference Genomes in Galaxy 🧐
- Galaxy Server administration / Docker and Galaxy ✍️ 🧐
- Galaxy Server administration / Galaxy Monitoring ✍️ 🧐
- Galaxy Server administration / Server: Other ✍️ 🧐
-
Imaging
/
Nucleoli Segmentation
&
Feature Extraction
using CellProfiler 🧐 - Transcriptomics / Introduction to Transcriptomics 🧐
- Transcriptomics / Identification of non-canonical ORFs and their potential biological function 🧐
- Statistics and machine learning / Classification in Machine Learning 🧐
- Statistics and machine learning / Fine-tuning Protein Language Model 🧐
- Statistics and machine learning / Regression in Machine Learning 🧐
- Statistics and machine learning / Introduction to Machine learning 🧐
- Statistics and machine learning / Convolutional neural networks (CNN) Deep Learning - Part 3 🧐
- Microbiome / Introduction to Microbiome Analysis 🧐
- Microbiome / Introduction to metatranscriptomics 🧐
- Using Galaxy and Managing your Data / Galaxy workflows in Dockstore 🧐
- Using Galaxy and Managing your Data / Submitting SARS-CoV-2 sequences to ENA 🧐
- Development in Galaxy / Scripting Galaxy using the API and BioBlend 🧐
- Development in Galaxy / Tool Dependencies and Containers ✍️ 🧐
- Development in Galaxy / Tool Dependencies and Conda ✍️ 🧐
- Development in Galaxy / Tool Shed: sharing Galaxy tools 🧐
- Development in Galaxy / Prerequisites for building software/conda packages 🧐
- Development in Galaxy / Galaxy Interactive Tours ✍️ 🧐
- Development in Galaxy / Galaxy Webhooks ✍️ 🧐
- Development in Galaxy / Tool development and integration into Galaxy ✍️ 🧐
- Development in Galaxy / Visualizations: JavaScript Plugins ✍️ 🧐
- Development in Galaxy / Generic plugins 🧐
- Development in Galaxy / Galaxy Interactive Environments ✍️ 🧐
- Development in Galaxy / Galaxy Code Architecture ✍️ 🧐
- Assembly / Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads 🧐
- Assembly / Unicycler Assembly 🧐
- Assembly / Genome assembly and assembly QC - Introduction short version 🧐
- Climate / Functionally Assembled Terrestrial Ecosystem Simulator (FATES) 🧐
- Genome Annotation / Introduction to Genome Annotation 🧐
- Genome Annotation / Genome annotation with Prokka 🧐
- Single Cell / Introducción al análisis de datos de scRNA-seq 🧐
- Single Cell / Una introducción al análisis de datos scRNA-seq 🧐
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Events
- 2023 Galaxy Admin Training (Ghent) 🎪
- Galaxy Training Academy 2025 🧑🏫
- Galaxy Training Academy 2024 🧑🏫
GitHub Activity
github Issues Reported
69 Merged Pull Requests
See all of the github Pull Requests and github Commits by Björn Grüning.
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Add EDAM topic to funannotate tutorial
genome-annotation -
add EDAM topic to "Masking repeats with RepeatMasker"
genome-annotation -
add one more EDAM topic to gene-centric tutorial
genome-annotation -
add EDAM topics to functional annotaiton of protein sequences
genome-annotation -
add Genomics as EDAM topic
genome-annotation
Reviewed 1013 PRs
We love our community reviewing each other's work!
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Update lldelisle details
template-and-tools -
Small fixes to the NGS data logistics tutorial
introduction -
typo error in the download URL for the bam file
genome-annotation -
blog post on how ace the Community is
news -
Improve explanations in microbiome diversity tutorial
microbiome
News

4th Mycobacterium tuberculosis complex NGS made easy

8 July 2024
Tuberculosis (TB) is a big killer in many countries of the world, particularly in those with low and middle income. Next-generation sequencing has been key in improving our understanding of drug resistance acquisition and of transmission of Mycobacterium tuberculosis. Yet, the need for expertise guiding NGS implementation in laboratories and the lack of bioinformatic expertise, are main obstacles hindering the implementation of NGS into TB programs.
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