Technical University of Munich at ASHG 2026

Oct 20–24, 2026 Montréal, Canada ~7.5k attendees Website

Technical University of Munich brings 8 presentations from 5 research groups to ASHG 2026 in Montréal. Its program centres on rare-disease diagnosis, cancer genomics and computational genetics, with therapeutic applications. The Chair of Computational Molecular Medicine presents genome-scale rare-variant modelling, Data Science in Systems Biology brings network medicine for drug-target discovery, and NFDI GHGA examines rare cancers and germline risk variants.

OrganizationASHG 2026 Attendance
3 PhD Students · 2 Staff Scientists · 1 Postdoc
Chair of Computational Molecular Medicinecs.cit.tum.de/en/cmm/home
Dry lab~26 people
Uses statistical modeling on next-generation sequencing, mass spectrometry, DNA/RNA sequencing and proteomics data. Supports rare-disease diagnosis with the Prokisch group and Solve-RD.
68 papers since 2024
›
CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
Genome biology, 2024
Species-aware DNA language models capture regulatory elements and their evolution
Genome biology, 2024
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing
Nature Communications, 2024
Source: OpenAlex author A5000244333
Funded by DFG, ERC
›
ERC, ERC Synergy grant EPIC · 2024-2030
“on a 6-year (2024-2030) ERC Synergy grant called EPIC”
DFG, INST 95/1655-1 FUGG; INST 95/1863-1 FUGG · active
“Co-financed by the DFG (INST 95/1655-1 FUGG and INST 95/1863-1 FUGG)”
Source: lab pages
9 platforms and techniques
›
Analyzes
next-generation sequencing, mass spectrometry-based proteomics, DNA sequencing, RNA sequencing
Techniques
statistical modeling, machine learning, deep learning, conditional autoencoder, de novo peptide sequencing
Source: lab pages
Currently hiring
›
“We are constantly seeking for highly motivated students in bioinformatics, physics and/or applied mathematics”
Source: lab positions page
Poster
Wed Oct 21
2:30 pm
★ Reviewers’ Choice
Improved gene-trait association discovery using functional gene embeddings and multi-trait modelling
Genotype-phenotype correlationsLarge-scale biobanksMachine learningPhenome-wide association
Poster
Thu Oct 22
4:15 pm
★ Reviewers’ Choice
DeepRVAT2: Unified Modeling of Coding and Regulatory Rare Variation at Genome Scale for Enhanced Gene Discovery and Diagnostics
Collaborators: Helmholtz Munich, German Cancer Research Center
Artificial intelligenceLarge-scale biobanksMendelian disorderRare variants
Talk
Fri Oct 23
11:15 am
Standardized transcriptome analysis improves rare disease diagnosis in large rare disease consortia
Collaborators: University of Tübingen, UCL Queen Square Institute of Neurology +6 more
BioinformaticsClinical geneticsMendelian disorderMulti-omics
Data Science in Systems Biologymls.ls.tum.de/en/daisybio/home
Wet + dry lab~30 people
Analyzes transcriptomics, proteomics, spatial and single-cell omics, and cohort data with machine learning. Uses them for drug-response prediction, network medicine and population-cohort analysis.
Funded by BMBF, Novo Nordisk Foundation +6 more
›
BMBF, DROP2AI · active
“The BMBF-funded DROP2AI project aims to predict drug response”
Novo Nordisk Foundation, MOPITAS · active
“The Novo Nordisk Foundation support the project MOPITAS”
DFG, DyHealthNet · active
“In the DFG-funded project DyHealthNet we build an explorative analysis platform”
+5 more on the lab page
Source: lab pages
13 platforms and techniques
›
Works with
Transcriptomics drug screens, Proteomics drug screens, Spatial omics, Single-cell omics, Shallow shotgun metagenomics sequencing, Bulk RNA-seq
Techniques
Machine learning, Data integration, Network medicine, Cell lines, Organoids, Mouse models, Human-in-the-loop analysis
Source: lab pages
No openings posted
Poster
Wed Oct 21
2:30 pm
GNExT uncovers pharmacological targets from genome-wide association studies through network medicine integration
Collaborators: Eurac Research, Friedrich-Alexander-Universität Erlangen-Nürnberg +1 more
BioinformaticsGenome-wide association studyIdentification of disease genesPharmacologic therapy
NFDI GHGAghga.de/about-us/team-members
Dry lab~101 people
Develops standardised omics workflows for WGS, WES, RNA sequencing, 10x single-cell and Xenium data. Supports variant detection, rare disease diagnostics and cancer research.
Funded by DFG
›
DFG, NFDI · active
“GHGA is funded via the DFG under the umbrella of the NFDI.”
Source: lab pages
12 platforms and techniques
›
Analyzes
Whole-genome sequencing (WGS), Targeted sequencing, Whole-exome sequencing (WES), RNA sequencing, 10x single-cell RNA-seq, Xenium
Techniques
Germline variant calling and annotation, Somatic variant calling, Variant benchmarking, RNA-seq analysis, RNA outlier detection, Spatial transcriptomics analysis
Source: lab pages
Currently hiring
›
“We are constantly looking for talents in software development and cloud computing”
Source: lab positions page
Poster
Wed Oct 21
2:30 pm
Omics outlier analysis in rare cancers reveals the function of rare germline variants in predisposition genes
Collaborators: Technische Universität Dresden, Deutsches Konsortium für Translationale Krebsforschung +5 more
CancerVariant interpretationAlternative splicingMulti-omics
Workflowsghga.de/de/ueber-uns/das-team
Dry lab~15 people
Standardizes NGS workflows for WGS, WES, RNA-seq, single-cell RNA-seq and Xenium data. Supports reproducible variant detection, rare-disease diagnostics and cancer research.
Funded by Deutsche Forschungsgemeinschaft (DFG)
›
Deutsche Forschungsgemeinschaft (DFG), NFDI · active
“Die GHGA wird über die DFG unter dem Dach der NFDI gefördert.”
Source: lab pages
15 platforms and techniques
›
Analyzes
Whole-genome sequencing (WGS), Targeted sequencing, Whole-exome sequencing (WES), RNA sequencing, Single-cell RNA-seq, Spatial transcriptomics, Xenium, Proteomics
Techniques
Germline variant calling and annotation, Somatic variant calling, Allele-specific copy-number estimation, Short tandem repeat analysis, Variant benchmarking, Aberrant expression and splicing detection, Continuous integration and deployment
Source: lab pages
Currently hiring
›
“We are constantly looking for talents in software development and cloud computing”
Source: lab positions page
Poster
Thu Oct 22
4:15 pm
Refined NMD and splicing annotations improve aberrant gene expression prediction
Collaborators: Institute of Computational Biology
BioinformaticsClinical geneticsComputational toolsRare variants
Niopek Labniopeklab.de
Works in rare disease and computational genetics.
Poster
Thu Oct 22
4:15 pm
★ Reviewers’ Choice
Causal variant prioritization for rare disease using biobank-calibrated gene impairment and functional gene embeddings
Identification of disease genesRare variantsMachine learningLarge-scale biobanks

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