Phylogeny and Ancestral Genome Reconstruction from Gene Order Using Maximum Likelihood and Binary Encoding

Over the long history of genome evolution, genes get rearranged under events such as rearrangements, losses, insertions and duplications, which in all change the ordering and content along the genome. Recent progress in genome-scale sequencing renews the challenges in the reconstructions of phylogeny and ancestral genomes with gene-order data. Such problems have been proved so interesting that a large number of algorithms have been developed rigorously over the past few years in attempts to tackle these problems following various principles. However, difficulties and limitations in performance and scalability largely prevent us from analyzing emerging modern whole-genome data, our study presented in this dissertation focuses on developing appropriate evolutionary models and robust algorithms for solving the phylogenetic and ancestral inference problems using gene-order data under the whole-genome evolution, along with their applications. To reconstruct phylogenies from gene-order data, we developed a collection of closely-related methods following the principle of likelihood maximization. To the best of our knowledge, it was the first successful attempt to apply maximum likelihood optimization technique into the analysis of gene-order phylogenetic problem. Later we proposed MLWD (in collaboration with Lin and Moret) in which we described an effective transition model to account for the transitions between presence and absence states of an gene adjacency. Besides genome rearrangements, other evolutionary events modify gene contents such as gene duplications and gene insertion/deletion (indels) can be naturally processed as well. We present our results from extensive testing on simulated data showing that our approach returns very accurate results very quickly. With a known phylogeny, a subsequent problem is to reconstruct the gene-order of ancestral genomes from their living descendants. To solve this problem, we adopted an adjacency-based probabilistic framework, and developed a method called PMAG. PMAG decomposes gene orderings into a set of gene adjacencies and then infers the probability of observing each adjacency in the ancestral genome. We conducted extensive simulation experiments and compared PMAG with InferCarsPro, GASTS, GapAdj and SCJ. According to the results, PMAG demonstrated great performance in terms of the true positive rate of gene adjacency. PMAG also achieved comparable running time to the other methods, even when the traveling sales man problem (TSP) were exactly solved. Although PMAG can give good performance, it is strongly restricted from analyzing datasets underwent only rearrangements. To infer ancestral genomes under a more general model of evolution with an arbitrary rate of indels , we proposed an enhanced method PMAG+ based on PMAG. PMAG+ includes a novel approach to infer ancestral gene contents and a detail description to reduce the adjacency assembly problem to an instance of TSP. We designed a series of experiments to validate PMAG+ and compared the results with the most recent and comparable method GapAdj. According to the results, ancestral gene contents predicted by PMAG+ coincided highly with the actual contents with error rates less than 1%. Under various degrees of indels, PMAG+ consistently achieved more accurate prediction of ancestral gene orders and at the same time, produced contigs very close to the actual chromosomes

Chloroplast DNA rearrangements in Campanulaceae: phylogenetic utility of highly rearranged genomes

BACKGROUND: The Campanulaceae (the "hare bell" or "bellflower" family) is a derived angiosperm family comprised of about 600 species treated in 35 to 55 genera. Taxonomic treatments vary widely and little phylogenetic work has been done in the family. Gene order in the chloroplast genome usually varies little among vascular plants. However, chloroplast genomes of Campanulaceae represent an exception and phylogenetic analyses solely based on chloroplast rearrangement characters support a reasonably well-resolved tree. RESULTS: Chloroplast DNA physical maps were constructed for eighteen representatives of the family. So many gene order changes have occurred among the genomes that characterizing individual mutational events was not always possible. Therefore, we examined different, novel scoring methods to prepare data matrices for cladistic analysis. These approaches yielded largely congruent results but varied in amounts of resolution and homoplasy. The strongly supported nodes were common to all gene order analyses as well as to parallel analyses based on ITS and rbcL sequence data. The results suggest some interesting and unexpected intrafamilial relationships. For example fifteen of the taxa form a derived clade; whereas the remaining three taxa – Platycodon, Codonopsis, and Cyananthus – form the basal clade. This major subdivision of the family corresponds to the distribution of pollen morphology characteristics but is not compatible with previous taxonomic treatments. CONCLUSIONS: Our use of gene order data in the Campanulaceae provides the most highly resolved phylogeny as yet developed for a plant family using only cpDNA rearrangements. The gene order data showed markedly less homoplasy than sequence data for the same taxa but did not resolve quite as many nodes. The rearrangement characters, though relatively few in number, support robust and meaningful phylogenetic hypotheses and provide new insights into evolutionary relationships within the Campanulaceae

Morphological and Genomic Characterization of Filobasidiella depauperata: A Homothallic Sibling Species of the Pathogenic Cryptococcus Species Complex

The fungal species Cryptococcus neoformans and Cryptococcus gattii cause respiratory and neurological disease in animals and humans following inhalation of basidiospores or desiccated yeast cells from the environment. Sexual reproduction in C. neoformans and C. gattii is controlled by a bipolar system in which a single mating type locus (MAT) specifies compatibility. These two species are dimorphic, growing as yeast in the asexual stage, and producing hyphae, basidia, and basidiospores during the sexual stage. In contrast, Filobasidiella depauperata, one of the closest related species, grows exclusively as hyphae and it is found in association with decaying insects. Examination of two available strains of F. depauperata showed that the life cycle of this fungal species shares features associated with the unisexual or same-sex mating cycle in C. neoformans. Therefore, F. depauperata may represent a homothallic and possibly an obligately sexual fungal species. RAPD genotyping of 39 randomly isolated progeny from isolate CBS7855 revealed a new genotype pattern in one of the isolated basidiospores progeny, therefore suggesting that the homothallic cycle in F. depauperata could lead to the emergence of new genotypes. Phylogenetic analyses of genes linked to MAT in C. neoformans indicated that two of these genes in F. depauperata, MYO2 and STE20, appear to form a monophyletic clade with the MATa alleles of C. neoformans and C. gattii, and thus these genes may have been recruited to the MAT locus before F. depauperata diverged. Furthermore, the ancestral MATa locus may have undergone accelerated evolution prior to the divergence of the pathogenic Cryptococcus species since several of the genes linked to the MATa locus appear to have a higher number of changes and substitutions than their MATα counterparts. Synteny analyses between C. neoformans and F. depauperata showed that genomic regions on other chromosomes displayed conserved gene order. In contrast, the genes linked to the MAT locus of C. neoformans showed a higher number of chromosomal translocations in the genome of F. depauperata. We therefore propose that chromosomal rearrangements appear to be a major force driving speciation and sexual divergence in these closely related pathogenic and saprobic species

MPI-PHYLIP: Parallelizing Computationally Intensive Phylogenetic Analysis Routines for the Analysis of Large Protein Families

Background: Phylogenetic study of protein sequences provides unique and valuable insights into the molecular and genetic basis of important medical and epidemiological problems as well as insights about the origins and development of physiological features in present day organisms. Consensus phylogenies based on the bootstrap and other resampling methods play a crucial part in analyzing the robustness of the trees produced for these analyses. Methodology: Our focus was to increase the number of bootstrap replications that can be performed on large protein datasets using the maximum parsimony, distance matrix, and maximum likelihood methods. We have modified the PHYLIP package using MPI to enable large-scale phylogenetic study of protein sequences, using a statistically robust number of bootstrapped datasets, to be performed in a moderate amount of time. This paper discusses the methodology used to parallelize the PHYLIP programs and reports the performance of the parallel PHYLIP programs that are relevant to the study of protein evolution on several protein datasets. Conclusions: Calculations that currently take a few days on a state of the art desktop workstation are reduced to calculations that can be performed over lunchtime on a modern parallel computer. Of the three protein methods tested, the maximum likelihood method scales the best, followed by the distance method, and then the maximum parsimony method. However, the maximum likelihood method requires significant memory resources, which limits its application to mor

Integration of Alignment and Phylogeny in the Whole-Genome Era

With the development of new sequencing techniques, whole genomes of many species have become available. This huge amount of data gives rise to new opportunities and challenges. These new sequences provide valuable information on relationships among species, e.g. genome recombination and conservation. One of the principal ways to investigate such information is multiple sequence alignment (MSA). Currently, there is large amount of MSA data on the internet, such as the UCSC genome database, but how to effectively use this information to solve classical and new problems is still an area lacking of exploration. In this thesis, we explored how to use this information in four problems, i.e. sequence orthology search problem, multiple alignment improvement problem, short read mapping problem, and genome rearrangement inference problem. For the first problem, we developed a EM algorithm to iteratively align a query with a multiple alignment database with the information from a phylogeny relating the query species and the species in the multiple alignment. We also infer the query\u27s location in the phylogeny. We showed that by doing alignment and phylogeny inference together, we can improve the accuracies for both problems. For the second problem, we developed an optimization algorithm to iteratively refine the multiple alignment quality. Experiment results showed our algorithm is very stable in term of resulting alignments. The results showed that our method is more accurate than existing methods, i.e. Mafft, Clustal-O, and Mavid, on test data from three sets of species from the UCSC genome database. For the third problem, we developed a model, PhyMap, to align a read to a multiple alignment allowing mismatches and indels. PhyMap computes local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyMap uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. Both theoretical computation and experiment results show that our model can differentiate between orthologous and paralogous alignments better than other popular short read mapping tools (BWA, BOWTIE and BLAST). For the fourth problem, we gave a simple genome recombination model which can express insertions, deletions, inversions, translocations and inverted translocations on aligned genome segments. We also developed an MCMC algorithm to infer the order of the query segments. We proved that using any Euclidian metrics to measure distance between two sequence orders in the tree optimization goal function will lead to a degenerated solution where the inferred order will be the order of one of the leaf nodes. We also gave a graph-based formulation of the problem which can represent the probability distribution of the order of the query sequences

A Hierarchical Framework for Phylogenetic and Ancestral Genome Reconstruction on Whole Genome Data

Gene order gets evolved under events such as rearrangements, duplications, and losses, which can change both the order and content along the genome, through the long history of genome evolution. Recently, the accumulation of genomic sequences provides researchers with the chance to handle long-standing problems about the phylogenies, or evolutionary histories, of sets of species, and ancestral genomic content and orders. Over the past few years, such problems have been proven so interesting that a large number of algorithms have been proposed in the attempt to resolve them, following different standards. The work presented in this dissertation focuses on algorithms and models for whole-genome evolution and their applications in phylogeny and ancestor inference from gene order. We developed a flexible ancestor reconstruction method (FARM) within the framework of maximum likelihood and weighted maximum matching. We designed binary code based framework to reconstruct evolutionary history for whole genome gene orders. We developed algorithms to estimate/predict missing adjacencies in ancestral reconstruction procedure to restore gene order from species, when leaf genomes are far from each other. We developed a pipeline involving maximum likelihood, weighted maximum matching and variable length binary encoding for estimation of ancestral gene content, to reconstruct ancestral genomes under the various evolutionary model, including genome rearrangements, additions, losses and duplications, with high accuracy and low time consumption. Phylogenetic analyses of whole-genome data have been limited to small collections of genomes and low-resolution data, or data without massive duplications. We designed a maximum-likelihood approach to phylogeny analysis (VLWD) based on variable length binary encoding, under maximum likelihood model, to reconstruct phylogenies from whole genome data, scaling up in accuracy and make it capable of reconstructing phylogeny from whole genome data, like triploids and tetraploids. Maximum likelihood based approaches have been applied to ancestral reconstruction but remain primitive for whole-genome data. We developed a hierarchical framework for ancestral reconstruction, using variable length binary encoding in content estimation, then adjacencies fixing and missing adjacencies predicting in adjacencies collection and finally, weighted maximum matching in gene order assembly. Therefore it extensively improves the performance of ancestral gene order reconstruction. We designed a series of experiments to validate these methods and compared the results with the most recent and comparable methods. According to the results, they are proven to be fast and accurate

Sequencing the plastid genome of giant ragweed (Ambrosia trifida, Asteraceae) from a herbarium specimen

We report the first plastome sequence of giant ragweed (Ambrosia trifida); with this new genome information, we assessed the phylogeny of Asteraceae and the transcriptional profiling against glyphosate resistance in giant ragweed. Assembly and genic features show a normal angiosperm quadripartite plastome structure with no signatures of deviation in gene directionality. Comparative analysis revealed large inversions across the plastome of giant ragweed and the previously sequenced members of the plant family. Asteraceae plastid genomes contain two inversions of 22.8 and 3.3 kb; the former is located between trnS-GCU and trnG-UCC genes, and the latter between trnE-UUC and trnT-GGU genes. The plastid genome sequences of A. trifida and the related species, Ambrosia artemisiifolia, are identical in gene content and arrangement, but they differ in length. The phylogeny is well-resolved and congruent with previous hypotheses about the phylogenetic relationship of Asteraceae. Transcriptomic analysis revealed divergence in the relative expressions at the exonic and intronic levels, providing hints toward the ecological adaptation of the genus. Giant ragweed shows various levels of glyphosate resistance, with introns displaying higher expression patterns at resistant time points after the assumed herbicide treatment.Peer reviewe