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 the quantitative description of evolutionary processes through the development of new probabilistic models and computational methods.
A strong focus of the lab lies on cancer as an evolutionary system and selection as a readout. 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 high heterogeneity of mutation rate in cancer genomes, driven by sequence context dependence and external mutation rate-determining factors. We are interested in different modes of selection active during tumorigenesis (including negative selection), happening in the coding or the noncoding part of the genome. We extend some of the analyses to the level of human population genetics, which is another active area of research in our lab.
External lab website: weghornlab.net
We are currently looking for a postdoctoral researcher who is excited about quantitative and computational science and would like to work in the fields of cancer evolution or human population genetics. You should have a degree in physics, statistics, computer science, bioinformatics, theoretical biology, genetics or a related discipline. Applications are accepted via this link (deadline: February 16, 2020).
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: