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Gueguen, L.

Publications and source records attributed to Gueguen, L..

3 recordsLinked to original sources

Accurate detection of convergent substitutions

In the history of life, some phenotypes have been acquired several times independently, through convergent evolution. Recently, lots of genome-scale studies have been devoted to identify nucleotides or amino acids that changed in a convergent manner when the convergent phenotypes evolved. These efforts have had mixed results, probably because of differences in the detection methods, and because of conceptual differences about the definition of a convergent substitution. Some methods contend that substitutions are convergent only if they occur on all branches where the phenotype changed towards the exact same state at a given nucleotide or amino acid position. Others are much looser in their requirements and define a convergent substitution as one that leads the site at which they occur to prefer a phylogeny in which species with the convergent phenotype group together. Here we suggest to look for convergent shifts in amino acid preferences instead of convergent substitutions to the exact same amino acid. We define as convergent shifts substitutions that occur on all branches where the phenotype changed and such that they correspond to a change in the type of amino acid preferred at this position. We implement the corresponding model into a method named PCOC. We show on simulations that PCOC better recovers convergent shifts than existing methods in terms of sensitivity and specificity. We test it on a plant protein alignment where convergent evolution has been studied in detail and find that our method recovers several previously identified convergent substitutions and proposes credible new candidates.

evolutionary biology

Community-driven data analysis training for biology

The primary problem with the explosion of biomedical datasets is not the data itself, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web-browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences.

bioinformatics

Unbiased Estimate Of Synonymous And Non-Synonymous Substitution Rates With Non-Stationary Base Composition

The measure of synonymous and non-synonymous substitution rates (dS and dN) is useful for assessing selection operating on protein sequences or for investigating mutational processes affecting genomes. In particular, the ratio [Formula] is expected to be a good proxy of{omega} , the probability of fixation of non-synonymous mutations relative to that of neutral mutations. Standard methods for estimating dN, dS or{omega} rely on the assumption that the base composition of sequences is at the equilibrium of the evolutionary process. In many clades, this assumption of stationarity is in fact incorrect, and we show here through simulations and through analyses of empirical data that non-stationarity biases the estimate of dN, dS and{omega} . We show that the bias in the estimate of{omega} can be fixed by explicitly considering non-stationarity in the modeling of codon evolution, in a maximum likelihood framework. Moreover, we propose an exact method of estimate of dN and dS on branches, based on stochastic mapping, that can take into account non-stationarity. This method can be directly applied to any kind of model of evolution of codons, as long as neutrality is clearly parameterized.

bioinformatics