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Efficient gene tree correction guided by genome evolution

Emmanuel Noutahi 1 Magali Semeria 2 Manuel Lafond 1 Jonathan Seguin 1 Bastien Boussau 2 Laurent Guéguen 2 Nadia El-Mabrouk 1 Eric Tannier 2, 3
3 BEAGLE - Artificial Evolution and Computational Biology
LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558, Inria Grenoble - Rhône-Alpes, LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Gene trees inferred solely from multiple alignments of homologous sequences often contain weakly supported and uncertain branches. Information for their full resolution may lie in the dependency between gene families and their genomic context. Integrative methods, using species tree information in addition to sequence information, have therefore been developed. They often rely on a computationally intensive tree space search which forecloses an application to large genomic databases. We propose a new method, called ProfileNJ, that takes a gene tree with statistical supports on its branches, and corrects its weakly supported parts by using a combination of information from a species tree and a distance matrix. Its low running time enabled us to use it on the whole Ensembl Compara database, for which we propose an alternative, arguably more plausible set of gene trees. This allowed us to perform a genome-wide analysis of duplication and loss patterns on the history of 63 eukaryote species, and predict ancestral gene content and order for all ancestors along the phylogeny.
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Submitted on : Wednesday, August 17, 2016 - 2:17:03 PM
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Emmanuel Noutahi, Magali Semeria, Manuel Lafond, Jonathan Seguin, Bastien Boussau, et al.. Efficient gene tree correction guided by genome evolution. PLoS ONE, Public Library of Science, 2016, 11 (8), e0159559 (22 p.). ⟨10.1371/journal.pone.0159559⟩. ⟨hal-01162963v4⟩

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