Statistical analysis of phylogenetic models is one of the most active areas of research in computational biology today with wide applications in the Theory of Evolution, epidemiology, forensics, etc. Current phylogenetic software packages limit the user to the set of phylogenetic models and inference strategies that have been pre-programmed in the tool. Inference under certain important phylogenetic models is very difficult with the Markov chain Monte-Carlo strategy implemented in current packages for phylogenetic analysis. The new paradigm of probabilistic programming, coming from computational statistics and theoretical computer science, solves the model expression problem and enables the user to implement novel inference methods. We utilize probabilistic programming to automatically generate Sequential Monte Carlo (SMC) inference machinery for MCMC-hard problems in phylogentics. SMC algorithms may be more efficient, provide unbiased solutions, and provide likelihoods estimates for model comparison. The goal of the proposed research is to carry out some of the first applications of probabilistic programming to real-world problems of empirical interest in evolutionary biology. The objectives are (1) to design and implement statistical inference machinery for complex diversification models with variable tree topology and a trait-dependent branching process under probabilistic programming, (2) to do a pilot study on the applicability of this inference machinery by studying the effect of the orogeny of the Andes on Neotropical biodiversity, and (3) contribute to the design and implementation of a novel probabilistic programming language for phylogenetics, TreePPL, by utilizing the insights gained from (1) and (2). We also propose dissemination and communication measures that target scientists and the general public throughout Europe and in particular new and aspiring EU member states.

This proposed project investigates novel ways of predicting the effects of future climate change on the survival of animal species. We will focus on a case study of two genetically and morphologically distinct wolf populations living in Eurasia. One of these survived the climate changes that have occurred over the past 40,000 years, whereas the other did not. We will describe the details of this extinction and replacement event, and determine the genetic processes that led to this difference between the two populations. To achieve this, we will carry out next-generation sequencing of wolf ancient DNA samples, and take advantage of ancient samples already collected and sequenced from across Eurasia. Together, this dataset will constitute the largest ancient wolf genome dataset ever collected. The project will end with a workshop that includes experts in climate and population viability modeling, and conservation practicioners, to explore the ways that the findings can be incorporated into future climate change mitigation planning. This workshop will provide new research avenues that the experienced researcher will be able to exploit in order to reach and reinforce a position of professional maturity and independence. The experienced researcher will also be trained in cutting edge scientific techniques and analyses, as well as a number of skills that are transferable between countries and sectors (including management, grant writing, communication and teaching), and that will facilitate his development into a research leader.

In this proposal, I seek to quantify the impact of structural variants on species genome diversity and extinction by sequencing ancient genomes of extinct megafauna. Structural variants are large genomic rearrangements capable of significantly affecting gene function and fitness. While in previous work I have investigated the genomes of extinct species at the single nucleotide polymorphism (SNP) level, the accurate identification of structural variants has been hampered by molecular and computational challenges, thus impeding their incorporation into genomic extinction research. Recent advancements in long-read sequencing technologies have led to considerable improvements in accuracy and affordability, making it feasible to characterize structural variants at population level scale in non-model organisms. Despite the fragmented nature of ancient DNA, our preliminary results show that millions of ancient DNA sequences exceeding 500 basepairs in length can be obtained from extraordinarily well-preserved samples. This project capitalizes on these samples, combined with state-of-the-art sequencing technologies, to recover high-quality long-read sequencing data from ancient specimens and subsequently reconstruct extinct genomes de-novo. Focussing on megafaunal that went extinct during the late Pleistocene, this project will examine the evolution of structural variants under scenarios of increased genetic drift and reduced selection at unprecedent resolution. Moreover, the project will pioneer the direct quantification of within-species structural variant changes through time, and provide new insights into genetic variants within previously unassembled genomic segments. By expanding our understanding of the genetic role of structural variants on extinction events, we will contribute valuable insights into the genetic determinants beyond SNPs that influence species diversity, adaptations, survival and eventual extinction.