Date(s) - 23/09/2015
15 h 00 min - 16 h 00 min
The talk is divided into two independent parts.
The first focuses on the detection of evolutionary traces of phenotypic convergence in genetic sequences. We consider a complex trait which is shared by a subset of species and not observed among others. In order to assess in what extent the genetic sequences of the species support the convergent evolution of the trait, we ask the question “How much positions have evolved independently to a same amino acid (or nucleotide, i.e “letter” of sequences) for the species bearing the trait?”. This question cannot be answered without uncertainty since we don’t have access to the past of the genetic sequences (only the contemporary ones are available). We show how to compute the expectation of this number for each position under a standard model of sequences evolution. (joint work with O. Chabrol and P. Pontarotti, I2M)
In the second part, we are interested in how fast species born and die (i.e. diversify). Diversification rates are estimated from phylogenies (i.e. trees figuring the parenthood of species) which contains extant and (sometimes) fossil taxa. By nature, rate estimations depend heavily on the time data provided in phylogenies, which are divergence times and (when used) fossil ages. Among these temporal data, fossil ages are by far the most precisely known (divergence times are inferences calibrated with fossils). We present an original method to compute the likelihood of a phylogenetic tree with fossils in which the only known time information is the fossil ages.
Testing our approach on simulated data shows that the maximum likelihood rate estimates from the phylogenetic tree shape and the fossil dates are almost as accurate as the ones obtained by taking into account all the data, including the divergence times. Moreover they are substantially more accurate than the estimates obtained only from the exact divergence times (without taking into account the fossil record). We finally estimate the diversification and fossilization rates from a paleontological dataset (eupelycosaurs tree). (joint work with Michel Laurin, Museum d’Histoire Naturelle de Paris)