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Murviel-lès-Montpellier, France

Swenson K.M.,Montpellier University | Swenson K.M.,Institute Of Biologie Computationnelle Ibc | Simonaitis P.,ENS Lyon | Blanchette M.,McGill University
Algorithms for Molecular Biology | Year: 2016

Background: Traditionally, the merit of a rearrangement scenario between two gene orders has been measured based on a parsimony criteria alone; two scenarios with the same number of rearrangements are considered equally good. In this paper, we acknowledge that each rearrangement has a certain likelihood of occurring based on biological constraints, e.g. physical proximity of the DNA segments implicated or repetitive sequences. Results: We propose optimization problems with the objective of maximizing overall likelihood, by weighting the rearrangements. We study a binary weight function suitable to the representation of sets of genome positions that are most likely to have swapped adjacencies. We give a polynomial-time algorithm for the problem of finding a minimum weight double cut and join scenario among all minimum length scenarios. In the process we solve an optimization problem on colored noncrossing partitions, which is a generalization of the Maximum Independent Set problem on circle graphs. Conclusions: We introduce a model for weighting genome rearrangements and show that under simple yet reasonable conditions, a fundamental distance can be computed in polynomial time. This is achieved by solving a generalization of the Maximum Independent Set problem on circle graphs. Several variants of the problem are also mentioned. © 2016 Swenson et al. Source


Swenson K.M.,Montpellier University | Swenson K.M.,Institute Of Biologie Computationnelle Ibc | Blanchette M.,McGill University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Traditionally, the merit of a rearrangement scenario between two genomes has been measured based on a parsimony criteria alone; two scenarios with the same number of rearrangements are considered equally good. In this paper, we acknowledge that each rearrangement has a certain likelihood of occurring based on biological constraints, e.g. physical proximity of the DNA segments implicated, or repetitive sequences. Accordingly, we propose optimization problems with the objective ofmaximizing overall likelihood, by weighting the rearrangements. We study a binary weight function suitable to the representation of sets of genome positions that are most likely to have swapped adjacencies. We give a polynomial-time algorithm for the problem of finding a minimum weight double cut and join (DCJ) scenario among all minimum length scenarios. In the process, we solve an optimization problem on colored noncrossing partitions which is a generalization of the Maximum Independent Set problem on circle graphs. © Springer-Verlag Berlin Heidelberg 2015. Source


To T.-H.,Montpellier University | To T.-H.,Institute Of Biologie Computationnelle Ibc | Scornavacca C.,Montpellier University | Scornavacca C.,Institute Of Biologie Computationnelle Ibc
BMC Genomics | Year: 2015

Reconciliation methods explain topology differences between a species tree and a gene tree by evolutionary events other than speciations. However, not all phylogenies are trees: hybridization can occur and create new species and this results into reticulate phylogenies. Here, we consider the problem of reconciling a gene tree with a species network via duplication and loss events. Two variants are proposed and solved with effcient algorithms: the first one finds the best tree in the network with which to reconcile the gene tree, and the second one finds the best reconciliation between the gene tree and the whole network. © 2015 To and Scornavacca et al. Source


Chan Y.-B.,Montpellier University | Ranwez V.,Montpellier SupAgro | Ranwez V.,Institute Of Biologie Computationnelle Ibc | Scornavacca C.,Montpellier University | Scornavacca C.,Institute Of Biologie Computationnelle Ibc
BMC Bioinformatics | Year: 2013

Background: Genes located in the same chromosome region share common evolutionary events more often than other genes (e.g. a segmental duplication of this region). Their evolution may also be related if they are involved in the same protein complex or biological process. Identifying co-evolving genes can thus shed light on ancestral genome structures and functional gene interactions.Results: We devise a simple, fast and accurate probability method based on species tree-gene tree reconciliations to detect when two gene families have co-evolved. Our method observes the number and location of predicted macro-evolutionary events, and estimates the probability of having the observed number of common events by chance.Conclusions: Simulation studies confirm that our method effectively identifies co-evolving families. This opens numerous perspectives on genome-scale analysis where this method could be used to pinpoint co-evolving gene families and thus help to unravel ancestral genome arrangements or undocumented gene interactions. © 2013 Chan et al.; licensee BioMed Central Ltd. Source


Pudlo P.,Montpellier University | Pudlo P.,Institute Of Biologie Computationnelle Ibc | Marin J.-M.,Montpellier University | Marin J.-M.,Institute Of Biologie Computationnelle Ibc | And 7 more authors.
Bioinformatics | Year: 2015

Motivation: Approximate Bayesian computation (ABC) methods provide an elaborate approach to Bayesian inference on complex models, including model choice. Both theoretical arguments and simulation experiments indicate, however, that model posterior probabilities may be poorly evaluated by standard ABC techniques. Results: We propose a novel approach based on a machine learning tool named random forests (RF) to conduct selection among the highly complex models covered by ABC algorithms. We thus modify the way Bayesian model selection is both understood and operated, in that we rephrase the inferential goal as a classification problem, first predicting the model that best fits the data with RF and postponing the approximation of the posterior probability of the selected model for a second stage also relying on RF. Compared with earlier implementations of ABC model choice, the ABC RF approach offers several potential improvements: (i) it often has a larger discriminative power among the competing models, (ii) it is more robust against the number and choice of statistics summarizing the data, (iii) the computing effort is drastically reduced (with a gain in computation efficiency of at least 50) and (iv) it includes an approximation of the posterior probability of the selected model. The call to RF will undoubtedly extend the range of size of datasets and complexity of models that ABC can handle. We illustrate the power of this novel methodology by analyzing controlled experiments as well as genuine population genetics datasets. Availability and implementation: The proposed methodology is implemented in the R package abcrf available on the CRAN. Contact: Supplementary information: Supplementary data are available at Bioinformatics online. © 2015 The Author 2015. Published by Oxford University Press. All rights reserved. Source

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