Population genomic analysis reveals a rich speciation and demographic history of orang-utans (Pongo pygmaeus and Pongo abelii)

To gain insights into evolutionary forces that have shaped the history of Bornean and Sumatran populations of orang-utans, we compare patterns of variation across more than 11 million single nucleotide polymorphisms found by previous mitochondrial and autosomal genome sequencing of 10 wild-caught orang-utans. Our analysis of the mitochondrial data yields a far more ancient split time between the two populations (~3.4 million years ago) than estimates based on autosomal data (0.4 million years ago), suggesting a complex speciation process with moderate levels of primarily male migration. We find that the distribution of selection coefficients consistent with the observed frequency spectrum of autosomal non-synonymous polymorphisms in orang-utans is similar to the distribution in humans. Our analysis indicates that 35% of genes have evolved under detectable negative selection. Overall, our findings suggest that purifying natural selection, genetic drift, and a complex demographic history are the dominant drivers of genome evolution for the two orang-utan populations.Publisher PDFPeer reviewe

The western painted turtle genome, a model for the evolution of extreme physiological adaptations in a slowly evolving lineage

Background: We describe the genome of the western painted turtle, Chrysemys picta bellii, one of the most widespread, abundant, and well-studied turtles. We place the genome into a comparative evolutionary context, and focus on genomic features associated with tooth loss, immune function, longevity, sex differentiation and determination, and the species' physiological capacities to withstand extreme anoxia and tissue freezing.Results: Our phylogenetic analyses confirm that turtles are the sister group to living archosaurs, and demonstrate an extraordinarily slow rate of sequence evolution in the painted turtle. The ability of the painted turtle to withstand complete anoxia and partial freezing appears to be associated with common vertebrate gene networks, and we identify candidate genes for future functional analyses. Tooth loss shares a common pattern of pseudogenization and degradation of tooth-specific genes with birds, although the rate of accumulation of mutations is much slower in the painted turtle. Genes associated with sex differentiation generally reflect phylogeny rather than convergence in sex determination functionality. Among gene families that demonstrate exceptional expansions or show signatures of strong natural selection, immune function and musculoskeletal patterning genes are consistently over-represented.Conclusions: Our comparative genomic analyses indicate that common vertebrate regulatory networks, some of which have analogs in human diseases, are often involved in the western painted turtle's extraordinary physiological capacities. As these regulatory pathways are analyzed at the functional level, the painted turtle may offer important insights into the management of a number of human health disorders

Enhancements to Hidden Markov Models for Gene Finding and Other Biological Applications

I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii In this thesis, we present enhancements of hidden Markov models for the problem of finding genes in DNA sequences. Genes are the parts of DNA that serve as a template for synthesis of proteins. Thus, gene finding is a crucial step in the analysis of DNA sequencing data. Hidden Markov models are a key tool used in gene finding. Yhis thesis presents three methods for extending the capabilities of hidden Markov models to better capture the statistical properties of DNA sequences. In all three, we encounter limiting factors that lead to trade-offs between the model accuracy and those limiting factors. First, we build better models for recognizing biological signals in DNA sequences. Our new models capture non-adjacent dependencies within these signals. In this case, the main limiting factor is the amount of training data: more training data allows more complex models. Second, we design methods for better representation of length distributions in hidden Markov models, where we balance the accuracy of the representation against the running time needed to find genes in novel sequences. Finally, we show that creating hidden Markov models with complex topologies may be detrimental to the prediction accuracy, unless we use more complex prediction algorithms. However, such algorithms require longer running time, and in many cases the prediction problem is NP-hard. For gene finding this means that incorporating some of the prior biological knowledge into the model would require impractical running times. However, we also demonstrate that our methods can be used for solving other biological problems, where input sequences are short. As a model example to evaluate our methods, we built a gene finder ExonHunter that outperforms programs commonly used in genome projects. ii