Evolutionary tools for genomic analysis
Evolution is the historical cause of the diversity of all life. The group’s research focuses on the development of data analysis methods for the study of molecular sequence evolution and for the exploitation of evolutionary information to draw powerful and robust inferences about phylogenetic history and genomic function. The evolutionary relationships between all organisms require that we analyse molecular sequences with consideration of the underlying structure relating those sequences.
We develop mathematical, statistical and computational techniques to reveal information present in genome data, to draw inferences about the processes that gave rise to that data and to make predictions about the biology of the systems whose components are encoded in those genomes.
Our three main research activities are: developing new evolutionary models and methods; providing these methods to other scientists via stand-alone software and web services; and applying such techniques to tackle biological questions of interest. We participate in comparative genomic studies, both independently and in collaboration with others, including the analysis of next-generation sequencing (NGS) data. This vast source of new data promises great gains in understanding genomes and brings with it many new challenges.
We were one of the teams that created the modern field of "DNA-storage", the use of DNA to archive digital information. Click here to find out more about our work on this topic.
De Maio, N., et al. (2020) Issues with SARS-CoV-2 sequencing data. virological.or
Slodkowicz G., Goldman N. (2020) Integrated structural and evolutionary analysis reveals common mechanisms underlying adaptive evolution in mammals. PNAS 117(11):5977-5986
Walker C., et al. (2020) Short-range template switching in great ape genomes explored using a pair hidden Markov model. bioRXiv doi:10.1101/2020.11.09.374694
Perron U., et al. (2019) Modeling structural constraints on protein evolution via side-chain conformational states. Mol Biol Evol 36(9):2086-2103
Löytynoja, A., Goldman, N. (2017). Short template switch events explain mutation clusters in the human genome. Genome research, 27(6), 1039-1049.
Klopfstein, S., Massingham, T., Goldman, N. (2017). More on the Best Evolutionary Rate for Phylogenetic Analysis. Systematic biology, 66(5), 769-785.
Schwarz, R. F., Tamuri, A. U., Kultys, ... Goldman, N. (2016) ALVIS: interactive non-aggregative visualization and explorative analysis of multiple sequence alignments. Nucleic acids research, 44(8), e77-e77
Gori, K., Suchan, T., Alvarez, N., Goldman, N., Dessimoz, C. (2016) Clustering genes of common evolutionary history. Molecular biology and evolution, 33(6), 1590-1605.
Truszkowski, J., Goldman, N. (2015). Maximum likelihood phylogenetic inference is consistent on multiple sequence alignments, with or without gaps. Systematic biology, 65(2), 328-333.
Parks, S. L., Goldman, N. (2014). Maximum likelihood inference of small trees in the presence of long branches. Systematic biology, 63(5), 798-811.
Goldman, N., et al. (2013) Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. Nature 494(7435), 77.
Altenhoff, A.M., et al. (2012) Resolving the ortholog conjecture: orthologs tend to be weakly, but significantly, more similar in function than paralogs. PLoS Comp Biol, 8, e1002514.
Jordan, G. and Goldman, N. (2012) The effects of alignment error and alignment filtering on the sitewise detection of positive selection. Mol Biol Evol 29, 1125–1139.
Löytynoja, A., Vilella, A.J., Goldman, N. (2012) Accurate extension of multiple sequence alignments using a phylogeny-aware graph algorithm. Bioinformatics 28, 1684–1691.
Scally, A., et al. (2012) Insights into hominid evolution from the gorilla genome sequence. Nature 483, 169–175.