Probabilistic Reconciliation Analysis for Genes and Pseudogenes

Sammanfattning: Phylogeneticists have studied the evolution of life from single celled organisms to the astonishing biodiversity around us for a long time now. The relationship between species is often expressed as a binary tree - the tree of life. Availability of fully sequenced genomes across species provides us the opportunity to investigate and understand the evolutionary processes, and to reconstruct the gene and species phylogeny in greater detail and more accurately. However, the effect of interacting evolutionary processes, such as gene duplications, gene losses, pseudogenizations, and lateral gene transfers, makes the inference of gene phylogenies challenging.In this thesis, probabilistic  Bayesian methods are introduced  to infer gene hylogenies in the guidance of species phylogeny. The distinguishing feature f this work from the earlier reconciliation-based methods is that evolutionary vents are mapped to detailed time intervals on the evolutionary time-scale. he proposed probabilistic approach reconciles the evolutionary events to the pecies phylogeny by integrating  gene duplications, gene losses, lateral gene ransfers and sequence evolution under a relaxed molecular clock. Genome- ide gene families for vertebrates and prokaryotes are  analyzed using this pproach that provides interesting insight into the evolutionary processes.Finally, a probabilistic  model is introduced that  models evolution  of genes and pseudogenes  simultaneously. The model incorporates birth-death  pro- cess according to which genes are duplicated, pseudogenized and lost under a sequence evolution  model with  a relaxed molecular clock.  To model  the evolutionary scenarios realistically, the model employs two different sequence evolution  models for the  evolution  of genes  and pseudogenes. The recon- ciliation  of evolutionary events to the species phylogenies enable us to infer the evolutionary scenario with  a higher resolution.  Some subfamilies of two interesting gene superfamilies,  i.e.  olfactory receptors and zinc fingers, are analyzed using this approach, which provides interesting insights.