ALVIS: visualization of horizontal dependencies

Schwarz Lab

Evolutionary and Cancer Genomics

Profile

Our lab develops and applies computational methods for understanding tumour heterogeneity and cancer evolution.

Our computational research group develops and applies algorithms and models to understand how cellular and intra-tumour heterogeneity (ITH) arises and how it affects tissue and patient phenotypes in space and time. We are particularly interested in the evolution of chromosomal instability and somatic copy-number alterations. To improve our understanding of ITH we leverage statistical and machine learning approaches as well as classical computer science algorithms and simulations. 

Our methods focus on three specific areas: (i) haplotype reconstruction and phasing of genomic variants and alterations; (ii) evolutionary inference and phylogenetic reconstruction from genomic rearrangements; (iii) understanding the haplotype-specific effects of genetic variation on gene regulation.

We see it as our mission to bridge theoretical and applied biomedical research, and develop, train, and validate our methods in close collaboration with our experimental partners and on large clinical cohorts. To this end, we have joined several international large-scale consortia, including the Pan-Cancer Analysis of Whole Genomes (PCAWG), ICGC-ARGO, and the TRACERx Consortium in which our methods have found wide-spread acceptance.

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