Bioinformatics and Genomics
October 2018 Group Leader in the Bioinformatics and Genomics Programme at the Centre for Genomic Regulation, Barcelona, Spain.
2013-2018 Postdoctoral researcher at the Department of Medicine and Department of Biomedical Informatics, Harvard Medical School, Boston, USA
2012 PhD in Theoretical Physics, University of Cologne, Germany
Mutation, selection and stochasticity combine to leave their footprint in genetic data. With the proper tools and analyses, the resulting information can be leveraged to describe mutational processes, obtain estimates of selection, and reveal more intricate evolutionary dynamics. We are interested in a variety of topics in the field of genetics. A strong focus of the lab lies on cancer as an evolutionary system and selection as a readout. This involves investigating different modes of selection active during tumorigenesis, including negative selection, in both the coding as well as the noncoding part of the genome. We analyze sequencing and other datasets and develop mathematical and computational approaches to estimate selection. An important aspect of this effort is to account for the heterogeneity of mutation rate. Estimates of the strength of selection in cancer allow for a prioritization of genes and noncoding regions by their disease relevance, with the ultimate goal of promoting therapeutic advances. We extend some of the analyses to the level of human population genetics, which is another active area of research in our lab. The main goal of our work is the quantitative description of evolutionary processes through the development of new probabilistic models and computational methods.
We are always looking for highly motivated researchers of any level with a background or strong interest in the quantitative description of evolutionary phenomena. This involves mathematical modeling, data analysis, and development of computational approaches to address questions regarding cancer or human evolution. Please direct any enquiries to email@example.com.
Cancer Bayesian Selection Estimation (CBaSE)
We have developed a tool which derives gene-specific estimates of the strength of negative and positive selection in cancer. It accounts for the heterogeneity of mutation rate across the cancer genome, independent of external input information on mutation rate covariates. CBaSE estimates also take into account the context-dependent cancer type-specific mutational signature.
The CBaSE method and results were published in Weghorn & Sunyaev, Nature Genetics, 2017:
The CBaSE software can be downloaded as a standalone tool or used in a browser-based application here: