MBI Videos

Videos by Workshop 4: Inference in Stochastic Models of Sequence Evolution

  • Many-Core Algorithms for Statistical Phylogenetics
    Marc Suchard
    Massive numerical integration plagues the statistical inference of partially observed stochastic processes. An important biological example entertains partially observed continuous-time Markov chains (CTMCs) to model molecular sequence evolution. Joint inference of phylogenetic trees and codon-based substitution models of sequence evolution remains computationally impracti...
  • Transposable elements: germline invaders with a lasting impact on genome evolution
    Cedric Feschotte
    I will provide an overview of our studies of the evolutionary dynamics of transposable elements and their impact on eukaryotic evolution. The emphasis will be on class 2 or DNA transposons in mammalian genomes. Genomic analyses reveal that a vast diversity of DNA transposons exists in mammalian genomes and that large cohorts of elements have been integrated in a lineage-sp...
  • Inference of population structure using dense genotype data
    Daniel Falush
    Inference of population structure using dense genotype data...
  • Model fitting for mixture models
    Anna Kedzierska
    Phylogenetic data arising on different tree topologies might be mixed on a given alignment. Models taking into it account usually contain a large number of parameters and the usual tests for model fitting cannot deal with them. Here we discuss an approach for model fitting of algebraic models on m-tree mixtures. This is joint work in progress with Marta Casanellas and Jesu...
  • Estimation of ancestral population divergence times and effective population sizes from complete human genome sequences
    Adam Siepel
    Complete genome sequences are now available for individuals representing several distinct human populations. The primary motivation for collecting these sequences has been to characterize human diversity, facilitate disease association studies, and pave the way for an era of "personalized medicine". However, these data also contain valuable information about huma...
  • Bayesian Gene-tree Reconstruction and Learning in Phylogenomics
    Matthew Rasmussen
    Advances in genome sequencing have enabled the study of evolution across both genomes and large clades of species, and has been especially useful for studying gene families as they expand and contract over evolutionary time by gene duplication and loss events. Here, we present a new approach for the reconstruction of gene-tree phylogenies that models simultaneously gene an...
  • Models for Amino Acid Substitution and Gene Duplication With Roots in Molecular and Evolutionary Processes
    David Liberles
    In Darwinian evolution, mutations occur approximately at random in a gene, turned into amino acid mutations by the genetic code. Some mutations are fixed to become substitutions and some are eliminated from the population. Partitioning pairs of closely related species with complete genome sequences by population size, we look at the PAM matrices generated for these partiti...
  • Parallel mutations and partial sweeps
    Graham Coop
    Parallel mutations and partial sweeps...
  • A coalescent process Markov in time and space
    Thomas Mailund
    A coalescent process Markov in time and space...
  • Using empirical codon models to understand the patterns and pressures of natural variation reflected in genomic re-sequencing data
    Carolin Kosiol
    In the past, two kinds of Markov models have been considered to describe protein sequence evolution. Codon-level models have been mechanistic, with a small number of parameters designed to take into account features such as transition-transversion bias, codon frequency bias and synonymous-nonsynonymous amino acid substitution bias. Amino acid models have been empirical, at...
  • Parameter estimation in models for sequence alignment
    Ana Arribas-Gil
    Models for pairwise alignment based on the TKF (Thorne, Kishino and Felsenstein 1991) indel process fit into the pair-Hidden Markov Model (pair-HMM). Observations in a pair-HMM are formed by the couple of sequences to be aligned and the hidden alignment is a Markov chain. Many efficient algorithms have been developed to estimate alignments and evolution parameters in this ...

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