Goldman group


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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.

For a brief multi-lingual look at what we do, click here.

Selected publications

Goldman, N., et al. (2013) Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. Nature (in press).

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.

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