Computational Multi-Omics

Cancer Genomics / Transcriptomics

The lab was and is an active contributor to several international cancer research consortia, including The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Our research mainly focuses on investigating aberrations in transcriptional regulation, specifically alternative splicing, and its relationship to underlying alterations of the somatic genotype. [Read more...]

Identification and Quantification of Alternative Splicing Events

Alternative splicing is a major contributing factor to transcriptome complexity in higher eukaryotes. Despite its importance, only a fraction of the landscape of alternative splicing is known, leaving many aspects to be elucidated. We developed SplAdder [1], a software that augments existing gene annotation with evidence from RNA-Sequencing to identify all alternative splicing events that are possible and supported by the data. [Read more...]

Prediction of Splicing-Associated Neoepitopes

Neoepitopes are at the core of modern Immunotherapies. Like how somatic mutations can generate specific peptide signatures that occur uniquely in the tumour but not in the normal cells, aberrant splicing can generate novel exon-exon junctions that translate into tumour-specific amino acid sequences. [Read more...]

Tumor Profiler Multi-Omics Data Integration

Within the Tumor Profiler Project, the lab applies analytical techniques and develops methods to recover a holistic view of the cell. The effort spans from  integration of multi-modal single-cell data, to predicting perturbation responses of single cells using Optimal Transport and  digital pathology image analysis & spatial transcriptomics. [Read more...]

Integration of Metabolomics Data in Cardiovascular Research

We design and analyse metabolomic studies of plasma samples from large human cohorts. In one such study, we collected LC-MS metabolomic data to analyze changes in the metabolome in response to treatment and, subsequently their association with cardiovascular disease outcomes. In another study, we integrated metabolomics and genetics data to study mechanistic pathways that explain the protective properties of physical activity against cardiovascular outcomes. [Read more...]