School of Biological and Chemical Sciences

Integrating fossils and genomes to elucidate the evolutionary history of species through time

Project description

The genomes of organisms bear the footprint of their evolutionary history. By combining information from molecular sequences (genomes) with information from the fossil record, inferences about this evolutionary history can be obtained and placed in the right geological context. However, the fossil record is notoriously incomplete, and patterns of genome evolution vary substantially among species, providing important challenges to the study of ancient evolutionary events. Recent advances in Bayesian statistics allow probabilistic modelling of the uncertainties in fossils and genomic evolutionary rates, so that robust inferences about species divergence times, that integrate these sources of uncertainty, can now be made. The Bayesian method is now being used to study controversial topics such as the pattern of diversification of birds and mammals relative to the End-Cretaceous mass extinction, or the elucidation of the time of origin of animals over 540 million years ago in the pre-Cambrian. In this project the student will work in the application and/or development of Bayesian MCMC statistical methods to study species divergences through time. The project will include the collection of genomic and fossil data from online databases, and the use of computer software for analysis. Experience in the use of statistical packages (such as R) and computer programming would be an advantage. The project is suitable for students interested in genomics, palaeontology and Bayesian statistics. Students with backgrounds in the life sciences, earth sciences, physics, computing or maths are welcomed to apply.

The student will learn genomic analysis with software packages such as R (data vizualisation, ape, phytools, etc), BEAST, MCMCtree, PAML, RAxML. Student will learn general Bayesian analysis theory, palaeontology and taxonomy

References

  • Bayesian estimation of species divergence times using correlated quantitative characters
    Álvarez-Carretero S, Goswami A, Yang Z, and dos Reis M. (2019) Systematic Biology, 68: 967–986.
  • Bayesian molecular clock dating using genome-scale datasets
    dos Reis M and Yang Z. (2019) In: Anisimova M (eds.) Evolutionary Genomics. Methods in Molecular Biology, vol 1910. Humana, New York, NY.
  • Bayesian molecular clock dating of species divergences in the genomics era
    dos Reis M, Donoghue PCJ and Yang Z. (2016) Nature Reviews Genetics, 17: 71–80.

See also