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

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

Bioinformatics for High-throughput Virus Detection and Discovery

Pathogen detection is a challenging problem given that any given specimen may contain one or more of many different microbes. Additionally, a specimen may contain microbes that have yet to be discovered. Traditional diagnostics are ill-equipped to address these challenges because they are focused on the detection of a single agent or panel of agents. I have developed three innovative computational approaches for analyzing high-throughput genomic assays capable of detecting many microbes in a parallel and unbiased fashion. The first is a metagenomic sequence analysis pipeline that was initially applied to 12 pediatric diarrhea specimens in order to give the first ever look at the diarrhea virome. Metagenomic sequencing and subsequent analysis revealed a spectrum of viruses in these specimens including known and highly divergent viruses. This metagenomic survey serves as a basis for future investigations about the possible role of these viruses in disease. The second tool I developed is a novel algorithm for diagnostic microarray analysis called VIPR: Viral Identification with a PRobabilistic algorithm). The main advantage of VIPR relative to other published methods for diagnostic microarray analysis is that it relies on a training set of empirical hybridizations of known viruses to guide future predictions. VIPR uses a Bayesian statistical framework in order to accomplish this. A set of hemorrhagic fever viruses and their relatives were hybridized to a total of 110 microarrays in order to test the performance of VIPR. VIPR achieved an accuracy of 94% and outperformed existing approaches for this dataset. The third tool I developed for pathogen detection is called VIPR HMM. VIPR HMM expands upon VIPR\u27s previous implementation by incorporating a hidden Markov model: HMM) in order to detect recombinant viruses. VIPR HMM correctly identified 95% of inter-species breakpoints for a set of recombinant alphaviruses and flaviviruses Mass sequencing and diagnostic microarrays require robust computational tools in order to make predictions regarding the presence of microbes in specimens of interest. High-throughput diagnostic assays coupled with powerful analysis tools have the potential to increase the efficacy with which we detect pathogens and treat disease as these technologies play more prominent roles in clinical laboratories

Bayesian machine learning methods for predicting protein-peptide interactions and detecting mosaic structures in DNA sequences alignments

Short well-defined domains known as peptide recognition modules (PRMs) regulate many important protein-protein interactions involved in the formation of macromolecular complexes and biochemical pathways. High-throughput experiments like yeast two-hybrid and phage display are expensive and intrinsically noisy, therefore it would be desirable to target informative interactions and pursue in silico approaches. We propose a probabilistic discriminative approach for predicting PRM-mediated protein-protein interactions from sequence data. The model suffered from over-fitting, so Laplacian regularisation was found to be important in achieving a reasonable generalisation performance. A hybrid approach yielded the best performance, where the binding site motifs were initialised with the predictions of a generative model. We also propose another discriminative model which can be applied to all sequences present in the organism at a significantly lower computational cost. This is due to its additional assumption that the underlying binding sites tend to be similar.It is difficult to distinguish between the binding site motifs of the PRM due to the small number of instances of each binding site motif. However, closely related species are expected to share similar binding sites, which would be expected to be highly conserved. We investigated rate variation along DNA sequence alignments, modelling confounding effects such as recombination. Traditional approaches to phylogenetic inference assume that a single phylogenetic tree can represent the relationships and divergences between the taxa. However, taxa sequences exhibit varying levels of conservation, e.g. due to regulatory elements and active binding sites, and certain bacteria and viruses undergo interspecific recombination. We propose a phylogenetic factorial hidden Markov model to infer recombination and rate variation. We examined the performance of our model and inference scheme on various synthetic alignments, and compared it to state of the art breakpoint models. We investigated three DNA sequence alignments: one of maize actin genes, one bacterial (Neisseria), and the other of HIV-1. Inference is carried out in the Bayesian framework, using Reversible Jump Markov Chain Monte Carlo

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

Evaluating the contributions of methylation and transcription to male-biased evolution

Male biased mutation is thought to be a consequence of mutations introduced during DNA replication. Studies of male bias have generally been restricted to an examination of bias in the total substitution rate rather than the substitution process. In this analysis, the potential contributions of germline sex differences in methylation and transcription to male biased mutation are examined via their effects on the substitution process. It is first shown that one of the post popular methods for modeling the effects of sequence context on nucleotide substitution rates detects an effect of context when none exists, which has important consequences particularly for models that aim to detect natural selection. Transitions involving CpG dinucleotides, which characteristically arise from methylation, are found to make a large contribution to male bias because of CpG frequency differences between the X chromosome and the autosomes. Germline transcription is also found to contribute to male bias, and may completely account for the bias observed in the chimpanzee lineage. These observations indicate that male bias is caused by multiple processes, and the contribution of replication errors is smaller than previously believed

Arvutuslikud ja statistilised meetodid DNA sekveneerimisandmete analüüsimiseks ja rakendused TÜ Eesti Geenivaramu andmetel

Väitekirja elektrooniline versioon ei sisalda publikatsiooneTänapäeval võimaldavad teise põlvkonna sekveneerimisel (next-generation sequencing, NGS) põhinevad meetodid määrata inimese genoomi järjestusi suurtes kohortides. Seejuures toodetakse väga suuri andmemahtusid, mis tekitavad mitmeid väljakutseid nii informaatika kui statistika valdkonnas. TÜ Eesti Geenivaramu (TÜ EGV) on aastatel 2002-2011 kogunud enam kui 50 000 inimese geeniproovi ja käesoleval aastal lisandub veel 100 000. Praeguseks hetkeks on üle 5 500 geenidoonori DNA-d analüüsitud erinevate NGS meetoditega. Käesolevas doktoritöös on pakutud üldine raamistik TÜ EGV-s toodetud NGS-andmete töötluseks ning lisaks on uuritud, kuidas võimalikult hästi arvestada Eesti päritolu isikute geneetilist eripära. Üheks levinud NGS meetodiks on eksoomi ehk kõigi valku kodeerivate geenipiirkondade sekveneerimine, mis võimaldab efektiivselt leida harvu ja de novo geenivariante ja leiab seetõttu rakendust meditsiinigeneetikas mendeliaarsete haiguste geenimutatsioonide tuvastamisel. Doktoritöö esimeses osas on analüüsitud kolme Eesti perekonna andmeid ja kõigil kolmel juhul kindlaks tehtud potentsiaalne patogeenne mutatsioon, mis lubab tulevikus välja töötada paremaid ravimeetodeid. Samuti on läbi viidud genoomi sekveneerimisandmete analüüs kliinilise vere näitajatega. See analüüs tõi välja populatsioonipõhise biopanga eelised, mis lisaks rikkalikele genoomiandmetele sisaldab ka väärtuslikku informatsiooni erinevate haiguste ja tunnuste kohta. Uuringus tuvastati olulisi seoseid CEBPA geenivariantide ja basofiilide arvu vahel, kusjuures viimasel on roll mitmete autoimmuunhaiguste sümptomaatikas. Ülegenoomsete assotsiatsiooniuuringute võimsuse suurendamiseks kasutatakse puuduvate geenivariantide ennustamist ehk imputeerimist. Muutmaks just Eesti päritolu isikute andmeanalüüsi tõhusamaks, on kasutatud genoomi sekveneerimisandmeid eestlaste-spetsiifilise imputatsioonipaneeli loomiseks. Seejärel on imputeeritud puuduvaid geenivariante kolmel moel – kasutades nii eestlaste-spetsiifilist kui ka kahte multi-etnilist paneeli. Võrdlustulemused näitasid, et eestlaste-spetsiifilise paneeli kasutamisel õnnestub määrata rohkem parema kvaliteediga geenivariante ning loodud paneeli eelis tuleb eriti esile harvaesinevate variantide puhul.Next-generation sequencing (NGS) technology enables large-scale, routine sequencing in large cohorts. This thesis demonstrated that the analysis of NGS data has a huge potential in several fields, but also requires a massive computational power. Also, with the increase of data volumes, there is an incessant need for the development of computational and statistical methods. Covering the whole spectrum of protein-coding regions in a cost-effective way, exome sequencing opens new opportunities for quick and exact large-scale screenings. In the first part of the thesis we analysed three Estonian families with Mendelian diseases and detected potentially causative gene variants for each case. These projects highlighted that a tight collaboration between data scientists and medical geneticists can lead to findings with considerable impact in the research of rare genetic disorders and have the potential to lead to successful therapies in the future. Population-based biobanks provide numerous opportunities for expanding phenotypic datasets. We used additional blood cell measurements from the electronic medical records and our genome-wide scan detected previously undiscovered association with basophil counts near CEBPA gene, and highlighted their role in the autoimmune regulation. This example opens new dimensions for scanning underlying genetic basis for a variety of traits and diseases. To increase the resolution of genome-wide scans, imputation is routinely implemented to incorporate variants that are not directly genotyped. We had an opportunity to construct an imputation reference panel to Estonians based on genome sequencing data. We showed that the utilization of a population-specific reference panel provided significantly higher imputation confidence for rare variants compared to larger, multi-ethnic panels. In the downstream analysis, we observed a huge gain in gene-based rare variant testing. As one of the main results of this thesis, the Estonian-specific imputation reference panel is created, tested and ready to serve for a long time. This includes data processing in the framework of the ongoing initiative to invite 100,000 Estonians to join the Biobank cohort, with the purpose to develop efficient disease prevention and treatment guides for the implementation of personalized medicine

SeLOX—a locus of recombination site search tool for the detection and directed evolution of site-specific recombination systems

Site-specific recombinases have become a resourceful tool for genome engineering, allowing sophisticated in vivo DNA modifications and rearrangements, including the precise removal of integrated retroviruses from host genomes. In a recent study, a mutant form of Cre recombinase has been used to excise the provirus of a specific HIV-1 strain from the human genome. To achieve provirus excision, the Cre recombinase had to be evolved to recombine an asymmetric locus of recombination (lox)-like sequence present in the long terminal repeat (LTR) regions of a HIV-1 strain. One pre-requisite for this type of work is the identification of degenerate lox-like sites in genomic sequences. Given their nature—two inverted repeats flanking a spacer of variable length—existing search tools like BLAST or RepeatMasker perform poorly. To address this lack of available algorithms, we have developed the web-server SeLOX, which can identify degenerate lox-like sites within genomic sequences. SeLOX calculates a position weight matrix based on lox-like sequences, which is used to search genomic sequences. For computational efficiency, we transform sequences into binary space, which allows us to use a bit-wise AND Boolean operator for comparisons. Next to finding lox-like sites for Cre type recombinases in HIV LTR sequences, we have used SeLOX to identify lox-like sites in HIV LTRs for six yeast recombinases. We finally demonstrate the general usefulness of SeLOX in identifying lox-like sequences in large genomes by searching Cre type recombination sites in the entire human genome. SeLOX is freely available at http://selox.mpi-cbg.de/cgi-bin/selox/index

Complex chromosome 17p rearrangements associated with low-copy repeats in two patients with congenital anomalies

Recent molecular cytogenetic data have shown that the constitution of complex chromosome rearrangements (CCRs) may be more complicated than previously thought. The complicated nature of these rearrangements challenges the accurate delineation of the chromosomal breakpoints and mechanisms involved. Here, we report a molecular cytogenetic analysis of two patients with congenital anomalies and unbalanced de novo CCRs involving chromosome 17p using high-resolution array-based comparative genomic hybridization (array CGH) and fluorescent in situ hybridization (FISH). In the first patient, a 4-month-old boy with developmental delay, hypotonia, growth retardation, coronal synostosis, mild hypertelorism, and bilateral club feet, we found a duplication of the Charcot-Marie–Tooth disease type 1A and Smith-Magenis syndrome (SMS) chromosome regions, inverted insertion of the Miller-Dieker lissencephaly syndrome region into the SMS region, and two microdeletions including a terminal deletion of 17p. The latter, together with a duplication of 21q22.3-qter detected by array CGH, are likely the unbalanced product of a translocation t(17;21)(p13.3;q22.3). In the second patient, an 8-year-old girl with mental retardation, short stature, microcephaly and mild dysmorphic features, we identified four submicroscopic interspersed 17p duplications. All 17 breakpoints were examined in detail by FISH analysis. We found that four of the breakpoints mapped within known low-copy repeats (LCRs), including LCR17pA, middle SMS-REP/LCR17pB block, and LCR17pC. Our findings suggest that the LCR burden in proximal 17p may have stimulated the formation of these CCRs and, thus, that genome architectural features such as LCRs may have been instrumental in the generation of these CCRs

Adaptive Evolution in Zinc Finger Transcription Factors

The majority of human genes are conserved among mammals, but some gene families have undergone extensive expansion in particular lineages. Here, we present an evolutionary analysis of one such gene family, the poly–zinc-finger (poly-ZF) genes. The human genome encodes approximately 700 members of the poly-ZF family of putative transcriptional repressors, many of which have associated KRAB, SCAN, or BTB domains. Analysis of the gene family across the tree of life indicates that the gene family arose from a small ancestral group of eukaryotic zinc-finger transcription factors through many repeated gene duplications accompanied by functional divergence. The ancestral gene family has probably expanded independently in several lineages, including mammals and some fishes. Investigation of adaptive evolution among recent paralogs using dN/dS analysis indicates that a major component of the selective pressure acting on these genes has been positive selection to change their DNA-binding specificity. These results suggest that the poly-ZF genes are a major source of new transcriptional repression activity in humans and other primates