Probabilistic modeling of the evolution of gene synteny within reconciled phylogenies. Issue 14 (December 2015)

• Record Type: Journal Article
• Authors: Semeria, Magali; Tannier, Eric; Guéguen, Laurent
• Is Part Of: BMC bioinformatics. Volume 16:Issue 14(2015)
• Page Start: 1
• Page End: 11
• Publication Date: 2015-12
• Subjects: genome rearrangements -- gene order -- gene tree reconciliation -- Bio++; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/1471-2105-16-S14-S5
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Most models of genome evolution concern either genetic sequences, gene content or gene order. They sometimes integrate two of the three levels, but rarely the three of them. Probabilistic models of gene order evolution usually have to assume constant gene content or adopt a presence/absence coding of gene neighborhoods which is blind to complex events modifying gene content. Results We propose a probabilistic evolutionary model for gene neighborhoods, allowing genes to be inserted, duplicated or lost. It uses reconciled phylogenies, which integrate sequence and gene content evolution. We are then able to optimize parameters such as phylogeny branch lengths, or probabilistic laws depicting the diversity of susceptibility of syntenic regions to rearrangements. We reconstruct a structure for ancestral genomes by optimizing a likelihood, keeping track of all evolutionary events at the level of gene content and gene synteny. Ancestral syntenies are associated with a probability of presence. We implemented the model with the restriction that at most one gene duplication separates two gene speciations in reconciled gene trees. We reconstruct ancestral syntenies on a set of 12drosophila genomes, and compare the evolutionary rates along the branches and along the sites. We compare with a parsimony method and find a significant number of results not supported by the posterior probability. The model is implemented in the Bio++ library. It thus benefits from and enriches the classical models and methods for molecular evolution.