The Model-Based Evolutionary Genomics Unit works at the interface of computational and evolutionary biology. Currently, our two main focus areas are:
i) reconstructing the Tree of Life, including the history of early life forms by continuing to develop and apply state-of-art probabilistic models of molecular evolution and using machine learning to model co-evolutionary dependencies across biological scales to reconstruct ancient phenotypes and environments;
ii) understanding somatic evolution in hierarchically organised tissues and across the Tree of Life, both from a theoretical standpoint (e.g., To what extent has tissue organisation evolved to minimise somatic evolution humans? Why do both plants and animals have stem cells?) and from a more data orientated perspective (e.g., What can emerging data on genetic variation in healthy tissues tell us about tissue organisation, the emergence of tumours and ageing?).
Recent papers of ours concerning these topics include:
“A rooted phylogeny resolves early bacterial evolution” Coleman, Davin, Mahendrarajah, Szánthó, Spang, Hugenholtz*, Szöllősi*, Williams*
Science 2021
https://science.sciencemag.org/content/372/6542/eabe0511.abstract
"Divergent genomic trajectories predate the origin of animals and fungi"
Ocaña-Pallarès, Williams, …, Bapteste, Tikhonenkov, Keeling, Szöllősi, Ruiz-Trillo
Nature 2022
https://www.nature.com/articles/s41586-022-05110-4
“Trade-off between reducing mutational accumulation and increasing commitment to differentiation determines tissue organization” Demeter, Derényi, Szöllősi
Nature Communications 2022
https://www.nature.com/articles/s41467-022-29004-1
“Compositionally Constrained Sites Drive Long-Branch Attraction ” Szánthó, Lartillot, Szöllősi*, Schrempf*
Systematic Biology 2023
https://doi.org/10.1093/sysbio/syad013
“AleRax: A tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss” Morel, Williams, Stamatakis, Szöllősi
Bioinformatics 2024 (to appear)
https://doi.org/10.1101/2023.10.06.561091
