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Polymorphism-Aware Phylogenetic Models

(with Nicola De Maio and Dominik Schrempf)

We have developed a new method called Polymorphism-aware phylogenetic model (PoMo). PoMo is a phylogenetic Markov model with states that represent fixed alleles and states representing polymorphic alleles at different frequencies. A substitution is thus modeled from mutation, through the transient polymorphic stage, and to fixation (or loss). Polymorphic states can be observed in the tips of the phylogeny as well as at ancestral nodes. We are developing applications of this novel approach to the inference of evolutionary parameters, such as mutation and selection, and to the estimation of phylogenies.

Modelling Evolutionary Trajectories from Experimental Evolution Studies

(with Agnes Jonas, Hande Topa, Antti Honkela, Christian Schlötterer)

Recent advances in sequencing technologies have made it possible to observe evolutionary trajectories-- allele frequency changes through time-- in great detail. For example, in addition to knowing the genetic changes at the end of a long-term selection experiment, we can also monitor allele frequency changes during the experiment. Using High Throughput Sequencing (HTS) technologies, we have collected data on allele frequency changes from populations undergoing laboratory selection for temperature adaptation. Using Gaussian Process models, we are developing methods to use, as fully as possible, the extent of the information in the evolutionary trajectories to identify sites responding to selection.

Empirical Codon Models

(with Nick Goldman, Ian Holmes, Maria Anisimova)

We have estimated the first empirical codon model using maximum likelihood methods. We show that models of the evolutionary process are improved by allowing for single, double and triple nucleotide changes, and that the affiliation between DNA triplets and the amino acid they encode is a major factor driving evolution. We plan to extend this approach to the estimation of a codon model assuming rate heterogeneity among codon sites caused by the influence of natural selection.

Natural Selection on the Mammalian Tree

(with Tomáš Vinař, Rute Da Fonseca, Rasmus Nielsen, Carlos Bustamante and Adam Siepel)

We have performed several comprehensive examination of positive selection on mammalian genomes using classic as well as next generation sequencing genome assemblies. The increased phylogenetic depth for the primates and other mammals results in substantially improved statistical power, and permits several new lineage- and clade-specific tests to be applied.

Estimation of Population Genetic Parameters from Pooled Next Generation Sequencing Data

(Robert Kofler, Nicola De Maio, Andreas Futschik, Christian Schlötterer)

Pooled sequencing at population level is often cheaper and more accurate than individual sequencing. We are developing software which implements the our unbiased estimates of Tajima’s pi and Waterson’s theta. We not only account for the bias derived by pooled sequencing, but also for the one generated by sequencing errors, that are higher in next generation sequencing.