Detection of recombination in DNA multiple alignments with hidden markov models

CConventional phylogenetic tree estimation methods assume that all sites in a DNA multiple alignment have the same evolutionary history. This assumption is violated in data sets from certain bacteria and viruses due to recombination, a process that leads to the creation of mosaic sequences from different strains and, if undetected, causes systematic errors in phylogenetic tree estimation. In the current work, a hidden Markov model (HMM) is employed to detect recombination events in multiple alignments of DNA sequences. The emission probabilities in a given state are determined by the branching order (topology) and the branch lengths of the respective phylogenetic tree, while the transition probabilities depend on the global recombination probability. The present study improves on an earlier heuristic parameter optimization scheme and shows how the branch lengths and the recombination probability can be optimized in a maximum likelihood sense by applying the expectation maximization (EM) algorithm. The novel algorithm is tested on a synthetic benchmark problem and is found to clearly outperform the earlier heuristic approach. The paper concludes with an application of this scheme to a DNA sequence alignment of the argF gene from four Neisseria strains, where a likely recombination event is clearly detected

Parametric inference of recombination in HIV genomes

Recombination is an important event in the evolution of HIV. It affects the global spread of the pandemic as well as evolutionary escape from host immune response and from drug therapy within single patients. Comprehensive computational methods are needed for detecting recombinant sequences in large databases, and for inferring the parental sequences. We present a hidden Markov model to annotate a query sequence as a recombinant of a given set of aligned sequences. Parametric inference is used to determine all optimal annotations for all parameters of the model. We show that the inferred annotations recover most features of established hand-curated annotations. Thus, parametric analysis of the hidden Markov model is feasible for HIV full-length genomes, and it improves the detection and annotation of recombinant forms. All computational results, reference alignments, and C++ source code are available at http://bio.math.berkeley.edu/recombination/.Comment: 20 pages, 5 figure

An HMM-based Comparative Genomic Framework for Detecting Introgression in Eukaryotes

One outcome of interspecific hybridization and subsequent effects of evolutionary forces is introgression, which is the integration of genetic material from one species into the genome of an individual in another species. The evolution of several groups of eukaryotic species has involved hybridization, and cases of adaptation through introgression have been already established. In this work, we report on a new comparative genomic framework for detecting introgression in genomes, called PhyloNet-HMM, which combines phylogenetic networks, that capture reticulate evolutionary relationships among genomes, with hidden Markov models (HMMs), that capture dependencies within genomes. A novel aspect of our work is that it also accounts for incomplete lineage sorting and dependence across loci. Application of our model to variation data from chromosome 7 in the mouse (Mus musculus domesticus) genome detects a recently reported adaptive introgression event involving the rodent poison resistance gene Vkorc1, in addition to other newly detected introgression regions. Based on our analysis, it is estimated that about 12% of all sites withinchromosome 7 are of introgressive origin (these cover about 18 Mbp of chromosome 7, and over 300 genes). Further, our model detects no introgression in two negative control data sets. Our work provides a powerful framework for systematic analysis of introgression while simultaneously accounting for dependence across sites, point mutations, recombination, and ancestral polymorphism

Comparative genomics and concerted evolution of β-tubulin paralogs in Leishmania spp

BACKGROUND: Tubulin isotypes and expression patterns are highly regulated in diverse organisms. The genome sequence of the protozoan parasite Leishmania major contains three distinct β-tubulin loci. To investigate the diversity of β-tubulin genes, we have compared the published genome sequence to draft genome sequences of two further species L. infantum and L. braziliensis. Untranscribed regions and coding sequences for each isoform were compared within and between species in relation to the known diversity of β-tubulin transcripts in Leishmania spp. RESULTS: All three β-tubulin loci were present in L. infantum and L. braziliensis, showing conserved synteny with the L. major sequence, hence confirming that these loci are paralogous. Flanking regions suggested that the chromosome 21 locus is an amastigote-specific isoform and more closely related (either structurally or functionally) to the chromosome 33 'array' locus than the chromosome 8 locus. A phylogenetic network of all isoforms indicated that paralogs from L. braziliensis and L. mexicana were monophyletic, rather than clustering by locus. CONCLUSION: L. braziliensis and L. mexicana sequences appeared more similar to each other than each did to its closest relative in another species; this indicates that these sequences have evolved convergently in each species, perhaps through ectopic gene conversion; a process not yet evident among the more recently derived L. major and L. infantum isoforms. The distinctive non-coding regions of each β-tubulin locus showed that it is the regulatory regions of these loci that have evolved most during the diversification of these genes in Leishmania, while the coding regions have been conserved and concerted. The various loci in Leishmania satisfy a need for innovative expression of β-tubulin, rather than elaboration of its structural role

Statistical power of phylo-HMM for evolutionarily conserved element detection

<p>Abstract</p> <p>Background</p> <p>An important goal of comparative genomics is the identification of functional elements through conservation analysis. Phylo-HMM was recently introduced to detect conserved elements based on multiple genome alignments, but the method has not been rigorously evaluated.</p> <p>Results</p> <p>We report here a simulation study to investigate the power of phylo-HMM. We show that the power of the phylo-HMM approach depends on many factors, the most important being the number of species-specific genomes used and evolutionary distances between pairs of species. This finding is consistent with results reported by other groups for simpler comparative genomics models. In addition, the conservation ratio of conserved elements and the expected length of the conserved elements are also major factors. In contrast, the influence of the topology and the nucleotide substitution model are relatively minor factors.</p> <p>Conclusion</p> <p>Our results provide for general guidelines on how to select the number of genomes and their evolutionary distance in comparative genomics studies, as well as the level of power we can expect under different parameter settings.</p

Accurate Detection of Recombinant Breakpoints in Whole-Genome Alignments

We propose a novel method for detecting sites of molecular recombination in multiple alignments. Our approach is a compromise between previous extremes of computationally prohibitive but mathematically rigorous methods and imprecise heuristic methods. Using a combined algorithm for estimating tree structure and hidden Markov model parameters, our program detects changes in phylogenetic tree topology over a multiple sequence alignment. We evaluate our method on benchmark datasets from previous studies on two recombinant pathogens, Neisseria and HIV-1, as well as simulated data. We show that we are not only able to detect recombinant regions of vastly different sizes but also the location of breakpoints with great accuracy. We show that our method does well inferring recombination breakpoints while at the same time maintaining practicality for larger datasets. In all cases, we confirm the breakpoint predictions of previous studies, and in many cases we offer novel predictions

Phylogenetic Detection of Recombination with a Bayesian Prior on the Distance between Trees

Genomic regions participating in recombination events may support distinct topologies, and phylogenetic analyses should incorporate this heterogeneity. Existing phylogenetic methods for recombination detection are challenged by the enormous number of possible topologies, even for a moderate number of taxa. If, however, the detection analysis is conducted independently between each putative recombinant sequence and a set of reference parentals, potential recombinations between the recombinants are neglected. In this context, a recombination hotspot can be inferred in phylogenetic analyses if we observe several consecutive breakpoints. We developed a distance measure between unrooted topologies that closely resembles the number of recombinations. By introducing a prior distribution on these recombination distances, a Bayesian hierarchical model was devised to detect phylogenetic inconsistencies occurring due to recombinations. This model relaxes the assumption of known parental sequences, still common in HIV analysis, allowing the entire dataset to be analyzed at once. On simulated datasets with up to 16 taxa, our method correctly detected recombination breakpoints and the number of recombination events for each breakpoint. The procedure is robust to rate and transition∶transversion heterogeneities for simulations with and without recombination. This recombination distance is related to recombination hotspots. Applying this procedure to a genomic HIV-1 dataset, we found evidence for hotspots and de novo recombination

Genome-wide inference of ancestral recombination graphs

The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the "ancestral recombination graph" (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of n chromosomes conditional on an ARG of n-1 chromosomes, an operation we call "threading." Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the true posterior distribution and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. Preliminary results also indicate that our methods can be used to gain insight into complex features of human population structure, even with a noninformative prior distribution.Comment: 88 pages, 7 main figures, 22 supplementary figures. This version contains a substantially expanded genomic data analysi

Improved Bayesian methods for detecting recombination and rate heterogeneity in DNA sequence alignments

DNA sequence alignments are usually not homogeneous. Mosaic structures may result as a consequence of recombination or rate heterogeneity. Interspecific recombination, in which DNA subsequences are transferred between different (typically viral or bacterial) strains may result in a change of the topology of the underlying phylogenetic tree. Rate heterogeneity corresponds to a change of the nucleotide substitution rate. Various methods for simultaneously detecting recombination and rate heterogeneity in DNA sequence alignments have recently been proposed, based on complex probabilistic models that combine phylogenetic trees with factorial hidden Markov models or multiple changepoint processes. The objective of my thesis is to identify potential shortcomings of these models and explore ways of how to improve them. One shortcoming that I have identified is related to an approximation made in various recently proposed Bayesian models. The Bayesian paradigm requires the solution of an integral over the space of parameters. To render this integration analytically tractable, these models assume that the vectors of branch lengths of the phylogenetic tree are independent among sites. While this approximation reduces the computational complexity considerably, I show that it leads to the systematic prediction of spurious topology changes in the Felsenstein zone, that is, the area in the branch lengths configuration space where maximum parsimony consistently infers the wrong topology due to long-branch attraction. I demonstrate these failures by using two Bayesian hypothesis tests, based on an inter- and an intra-model approach to estimating the marginal likelihood. I then propose a revised model that addresses these shortcomings, and demonstrate its improved performance on a set of synthetic DNA sequence alignments systematically generated around the Felsenstein zone. The core model explored in my thesis is a phylogenetic factorial hidden Markov model (FHMM) for detecting two types of mosaic structures in DNA sequence alignments, related to recombination and rate heterogeneity. The focus of my work is on improving the modelling of the latter aspect. Earlier research efforts by other authors have modelled different degrees of rate heterogeneity with separate hidden states of the FHMM. Their work fails to appreciate the intrinsic difference between two types of rate heterogeneity: long-range regional effects, which are potentially related to differences in the selective pressure, and the short-term periodic patterns within the codons, which merely capture the signature of the genetic code. I have improved these earlier phylogenetic FHMMs in two respects. Firstly, by sampling the rate vector from the posterior distribution with RJMCMC I have made the modelling of regional rate heterogeneity more flexible, and I infer the number of different degrees of divergence directly from the DNA sequence alignment, thereby dispensing with the need to arbitrarily select this quantity in advance. Secondly, I explicitly model within-codon rate heterogeneity via a separate rate modification vector. In this way, the within-codon effect of rate heterogeneity is imposed on the model a priori, which facilitates the learning of the biologically more interesting effect of regional rate heterogeneity a posteriori. I have carried out simulations on synthetic DNA sequence alignments, which have borne out my conjecture. The existing model, which does not explicitly include the within-codon rate variation, has to model both effects with the same modelling mechanism. As expected, it was found to fail to disentangle these two effects. On the contrary, I have found that my new model clearly separates within-codon rate variation from regional rate heterogeneity, resulting in more accurate predictions

Classification of phylogenetic data via Bayesian mixture modelling

Conventional probabilistic models for phylogenetic inference assume that an evolutionary tree,andasinglesetofbranchlengthsandstochasticprocessofDNA evolutionare sufficient to characterise the generating process across an entire DNA alignment. Unfortunately such a simplistic, homogeneous formulation may be a poor description of reality when the data arise from heterogeneous processes. A well-known example is when sites evolve at heterogeneous rates. This thesis is a contribution to the modelling and understanding of heterogeneityin phylogenetic data. Weproposea methodfor the classificationof DNA sites based on Bayesian mixture modelling. Our method not only accounts for heterogeneous data but also identifies the underlying classes and enables their interpretation. We also introduce novel MCMC methodology with the same, or greater, estimation performance than existing algorithms but with lower computational cost. We find that our mixture model can successfully detect evolutionary heterogeneity and demonstrate its direct relevance by applying it to real DNA data. One of these applications is the analysis of sixteen strains of one of the bacterial species that cause Lyme disease. Results from that analysis have helped understanding the evolutionary paths of these bacterial strains and, therefore, the dynamics of the spread of Lyme disease. Our method is discussed in the context of DNA but it may be extendedto othertypesof molecular data. Moreover,the classification scheme thatwe propose is evidence of the breadth of application of mixture modelling and a step forwards in the search for more realistic models of theprocesses that underlie phylogenetic data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo