Evolutionary analysis of DNA and amino acid sequences
We develop and use mathematical probabilistic models that describe DNA sequence evolution, DNA sequencing, and storage of digital information in DNA. The main focus of the group has traditionally been the development of models that describe how DNA changes through time during the course of evolution. Our aim is to improve inference of evolutionary histories (phylogenies), and ancient genomes (ancestral sequence reconstruction), as well as to improve our capabilities at detecting the footprint of natural selection from genomic data. More recently, the group has expanded its focus over computational and mathematical methods to improve the storage of digital information in DNA --- a technology that promises to revolutionize how we store data in the long term. We are also developing probabilitstic and information theoretical models to improve the efficiency of DNA sequencing - in particular nanopore sequencing.
Perron, U., Kozlov, A. M., Stamatakis, A., Goldman, N., & Moal, I. H. (2019). Modeling structural constraints on protein evolution via side-chain conformational states. Molecular Biology and Evolution 36, 2086-2103.
Klopfstein, S., Massingham, T., & Goldman, N. (2017). More on the best evolutionary rate for phylogenetic analysis. Systematic Biology 66, 769-785.
Truszkowski, J., & Goldman, N. (2016). Maximum likelihood phylogenetic inference is consistent on multiple sequence alignments, with or without gaps. Systematic Biology 65, 328-333.