Evolution through segmental duplications and losses : A Super-Reconciliation approach

The classical gene and species tree reconciliation, used to infer the history of gene gain and loss explaining the evolution of gene families, assumes an independent evolution for each family. While this assumption is reasonable for genes that are far apart in the genome, it is not appropriate for genes grouped into syntenic blocks, which are more plausibly the result of a concerted evolution. Here, we introduce the Super-Reconciliation problem which consists in inferring a history of segmental duplication and loss events (involving a set of neighboring genes) leading to a set of present-day syntenies from a single ancestral one. In other words, we extend the traditional Duplication-Loss reconciliation problem of a single gene tree, to a set of trees, accounting for segmental duplications and losses. Existency of a Super-Reconciliation depends on individual gene tree consistency. In addition, ignoring rearrangements implies that existency also depends on gene order consistency. We first show that the problem of reconstructing a most parsimonious Super-Reconciliation, if any, is NP-hard and give an exact exponential-time algorithm to solve it. Alternatively, we show that accounting for rearrangements in the evolutionary model, but still only minimizing segmental duplication and loss events, leads to an exact polynomial-time algorithm. We finally assess time efficiency of the former exponential time algorithm for the Duplication-Loss model on simulated datasets, and give a proof of concept on the opioid receptor genes

From gene trees to species trees through a supertree approach

International audienceGene trees are leaf-labeled trees inferred from molecular se- quences. Due to duplication events arising in genome evolution, gene trees usually have multiple copies of some labels, i.e. species. Inferring a species tree from a set of multi-labeled gene trees (MUL trees) is a well- known problem in computational biology. We propose a novel approach to tackle this problem, mainly to transform a collection of MUL trees into a collection of evolutionary trees, each containing single copies of labels. To that aim, we provide several algorithmic building stones and describe how they fit within a general species tree inference process. Most algorithms have a linear-time complexity, except for an FPT algorithm proposed for a problem that we show to be intractable