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Workshops

will take place on Monday, 22 September 2025

Overview:

WS1) Bioinformatics education
WS2) simpleVM [deNBi]
WS3) From a Collection of Scripts to a Pipeline
WS4) Datavzrd
WS5) Mastering GHGA: From Metadata Preparation to Secure Data Access
WS6) Computational Pangenomics [deNBI]
WS7) Automated metabolic modelling: Building, analysing and simulating genome-scale metabolic models in Python
WS8) Spatial domain identification: computational methods for discovering tissue architecture

 

Detailed Workshop Programme:

WS1) Bioinformatics education

Organizers: Jan Grau (MLU Halle); Stefan Kurtz (Universität Hamburg); Kay Nieselt (Eberhard Karls Universität Tübingen); Sven Rahmann (Universität des Saarlandes, Saarbrücken); Ralf Zimmer (Ludwig-Maximilians-Universität München)

Participants: max. 30

  • Which essential topics and skills should be covered by a B.Sc. in bioinformatics with regard to
    • Mathematics
    • Computer Science, incl. theory and application of Machine Learning / Artificial Intelligence
    •  Life Sciences
    •  Core Bioinformatics
  • How should ECTS/CPs be distributed among these topics? What proportion of core bioinformatics modules should be reached?
  • What is a reasonable balance between theoretical and practical (wet lab, programming, etc.) courses?
  • Which proportion of ECTS/CPs should be held flexible to
    • allow for setting a university-specific focus/specialization or
    • allow students to follow their topics of interest?
  • What language requirement should be stated, should the B.Sc. be taught (entirely) in English?

WS2) simpleVM [deNBi]

Organizers: Peter Belmann & Alexander Sczyrba (FZ Jülich / deNBI)

  • Cloud Computing Basics
  • Managing Instances
  • Managing Volumes
  • Using Research Environments for Analyzing Results
  • Scaling with a SimpleVM Cluster
  • Handling Data in Object Storage

WS3) From a Collection of Scripts to a Pipeline

Organizers: Mark Polster, Famke Bäuerle & Sven Nahnsen (University of Tuebingen)

  • nextflow principles
  • nf-core and its relationship to nextflow
  • exploration of an example pipeline
  • hands-on creation of a pipeline
    • using the nf-core template
    • exploration of available modules
    • writing new modules

WS4) Datavzrd

Organizers: Felix Wiegand & Johannes Köster (Universität Duisburg-Essen)

  • Brief introduction to Datavzrd
  • Highlight of its core strengths in generating portable, visually rich, and interactive reports
  • Hands-on tutorial on how to use the tool effectively

WS5) Mastering GHGA: From Metadata Preparation to Secure Data Access

Organizer: Vanessa González Ribao (German Human Genome-Phenome Archive / DKFZ)

  • Understanding Genome Archives and FAIR Principles
  • Metadata & Data Preparation for Submission
  • Efficiently Browsing & Searching the GHGA Data Portal
  • Best-Practice Data Access & Legal Considerations
  • Interactive Q&A and Hands-On Experience

WS6) Computational Pangenomics [deNBI]

Organizers: Tizian Schulz & Jens Stoye (Universität Bielefeld / deNBI)

  • Introduction to computational pangenomics
  • Investigating a pangenome's diversity with panacus and hands-on
  • Pangenomic core detection with Corer and hands-on
  • Querying a graphical pangenome with PLAST and hands-on
  • Phylogenomic reconstruction with SANS and hands-on

WS7) Automated metabolic modelling: Building, analysing and simulating genome-scale metabolic models in Python

Organizers: Carolin Brune & Andreas Dräger (Martin Luther University Halle-Wittenberg)

  • Basic understanding of constraint-based metabolic models and their application(s)
  • Learning the basic steps of how genome-scale metabolic models are generated and further curated
  • Getting insights into how these models can be utilised
  • Hands-on experience on how to use the toolbox refineGEMs
  • Experience in model reconstruction using different workflows from SPECIMEN

WS8) Spatial domain identification: computational methods for discovering tissue architecture

Organizers: Robin Khatri (University Medical Center Hamburg-Eppendorf)

  • Identify spatial domains in single-cell spatial transcriptomics data
  • Use unsupervised methods for detection and interpretation
  • Analyze tissue dynamics in health and disease
  • Apply domain identification algorithms hands-on
DECHEMA e.V.

 

Supported by

Heinrich Heine University Düsseldorf FaBI GBM_webGesellschaft für Biochemie und Molekularbiologie, GBM) West German Genome Center