Computational Biology Group
The recent transition of molecular biology into a data-driven discipline made the application of statistical (machine) learning practical for informing experimental designs and for unveiling the causal gene regulatory mechanisms that shape organismal complexity from genotype to phenotype.
We employ statistical approaches in form of intelligent software, which we implement for efficient deployment in high-performance cloud architectures to scale our exploration of natural variation to the entire tree of life and our mechanistic understanding of gene expression regulation to all genes of a eukaryotic organism.
Intelligent software is adaptive, scalable, and user-friendly and we harness it to translate the predictive capacity of artificial intelligence into holistic molecular biology research. Our ultimate aim is to predict the regulatory evolution of gene expression for all genes of a eukaryotic organism and causally associate molecular mechanisms of gene regulation with phenotypic changes in complex traits.
We approach this milestone by integrating comparative and functional genomics at tree-of-life scale with the causal inference of gene regulatory networks to derive a generically applicable predictive framework of trait evolvability.
We successfully applied aspects of this research strategy to a diverse portfolio of biological questions and keep refining this process to accommodate a wider range of molecular biological applications. A more detailed summary of our Scientific Software and previous research question can be found on our Biological Research Portfolio page.
- Scientific Software Engineering and HPC Cloud Computing
- Statistical (Machine) Learning and Causal Inference
- Theory of Gene Regulatory Network Inference and evolution of gene regulation
- Alexandru Tomescu, University of Helsinki, Finland
- Krikamol Muandet, CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
- Uta Paszkowski, University of Cambridge, UK
- Irina Mohorianu, University of Cambridge, UK
B Buchfink, K Reuter, HG Drost*. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nature Methods, 18, 366–368 (2021).
I Moutsopoulos, L Maischak, E Lauzikaite, SA Vasquez Urbina, Eleanor C Williams, HG Drost, II Mohorianu. noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise. Nucleic Acids Research, 49 (14), e83 (2021).
Josué Barrera-Redondo*, Jaruwatana Sodai Lotharukpong, Hajk-Georg Drost* and Susana M Coelho*. Uncovering gene-family founder events during major evolutionary transitions in animals, plants and fungi using GenEra. bioRxiv, 2022.07.07.498977 (2022).
M Quint, HG Drost et al. A transcriptomic hourglass in plant embryogenesis. Nature 490 (7418), 89-101 (2012). (journal cover).