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.
| Organization | ASHG 2026 Attendance |
|---|---|
Technical University of Munich Munich, Germany | 3 PhD Students · 2 Staff Scientists · 1 Postdoc |
Chair of Computational Molecular Medicinecs.cit.tum.de/en/cmm/home 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.
| Talk Wed Oct 21 1:35 pm DeepRVAT2: Unified modeling of coding and regulatory rare variation at genome scale for enhanced gene discovery and diagnostics Artificial intelligenceLarge-scale biobanksMendelian disorderRare variants Poster Wed Oct 21 2:30 pm ★ Reviewers’ Choice Improved gene-trait association discovery using functional gene embeddings and multi-trait modellingGenotype-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 DiagnosticsArtificial intelligenceLarge-scale biobanksMendelian disorderRare variants Talk Fri Oct 23 11:15 am Standardized transcriptome analysis improves rare disease diagnosis in large rare disease consortia BioinformaticsClinical geneticsMendelian disorderMulti-omics |
Data Science in Systems Biologymls.ls.tum.de/en/daisybio/home 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.
| Poster Wed Oct 21 2:30 pm GNExT uncovers pharmacological targets from genome-wide association studies through network medicine integration BioinformaticsGenome-wide association studyIdentification of disease genesPharmacologic therapy |
NFDI GHGAghga.de/about-us/team-members Develops standardised omics workflows for WGS, WES, RNA sequencing, 10x single-cell and Xenium data. Supports variant detection, rare disease diagnostics and cancer research.
| Poster Wed Oct 21 2:30 pm Omics outlier analysis in rare cancers reveals the function of rare germline variants in predisposition genes CancerVariant interpretationAlternative splicingMulti-omics |
Workflowsghga.de/de/ueber-uns/das-team 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.
| Poster Thu Oct 22 4:15 pm Refined NMD and splicing annotations improve aberrant gene expression prediction BioinformaticsClinical geneticsComputational toolsRare variants |
Poster Thu Oct 22 4:15 pm ★ Reviewers’ Choice Causal variant prioritization for rare disease using biobank-calibrated gene impairment and functional gene embeddingsIdentification of disease genesRare variantsMachine learningLarge-scale biobanks |
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