Killing Two Birds with One Stone: The Concurrent Development of the Novel Alignment Free Tree Building Method, Scrawkov-Phy, and the Extensible Phyloinformatics Utility, EMU-Phy.

Many components of phylogenetic inference belong to the most computationally challenging and complex domain of problems. To further escalate the challenge, the genomics revolution has exponentially increased the amount of data available for analysis. This, combined with the foundational nature of phylogenetic analysis, has prompted the development of novel methods for managing and analyzing phylogenomic data, as well as improving or intelligently utilizing current ones. In this study, a novel alignment tree building algorithm using Quasi-Hidden Markov Models (QHMMs), Scrawkov-Phy, is introduced. Additionally, exploratory work in the design and implementation of an extensible phyloinformatics tool, EMU-Phy, is described. Lastly, features of the best-practice tools are inspected and provisionally incorporated into Scrawkov-Phy to evaluate the algorithm’s suitability for said features. This study shows that Scrawkov-Phy, as utilized through EMU-Phy, captures phylogenetic signal and reconstructs reasonable phylogenies without the need for multiple-sequence alignment or high-order statistical models. There are numerous additions to both Scrawkov-Phy and EMU-Phy which would improve their efficacy and the results of the provisional study shows that such additions are compatible

Inferring phylogenetic trees under the general Markov model via a minimum spanning tree backbone

Phylogenetic trees are models of the evolutionary relationships among species, with species typically placed at the leaves of trees. We address the following problems regarding the calculation of phylogenetic trees. (1) Leaf-labeled phylogenetic trees may not be appropriate models of evolutionary relationships among rapidly evolving pathogens which may contain ancestor-descendant pairs. (2) The models of gene evolution that are widely used unrealistically assume that the base composition of DNA sequences does not evolve. Regarding problem (1) we present a method for inferring generally labeled phylogenetic trees that allow sampled species to be placed at non-leaf nodes of the tree. Regarding problem (2), we present a structural expectation maximization method (SEM-GM) for inferring leaf-labeled phylogenetic trees under the general Markov model (GM) which is the most complex model of DNA substitution that allows the evolution of base composition. In order to improve the scalability of SEM-GM we present a minimum spanning tree (MST) framework called MST-backbone. MST-backbone scales linearly with the number of leaves. However, the unrealistic location of the root as inferred on empirical data suggests that the GM model may be overtrained. MST-backbone was inspired by the topological relationship between MSTs and phylogenetic trees that was introduced by Choi et al. (2011). We discovered that the topological relationship does not necessarily hold if there is no unique MST. We propose so-called vertex-order based MSTs (VMSTs) that guarantee a topological relationship with phylogenetic trees.Phylogenetische Bäume modellieren evolutionäre Beziehungen zwischen Spezies, wobei die Spezies typischerweise an den Blättern der Bäume sitzen. Wir befassen uns mit den folgenden Problemen bei der Berechnung von phylogenetischen Bäumen. (1) Blattmarkierte phylogenetische Bäume sind möglicherweise keine geeigneten Modelle der evolutionären Beziehungen zwischen sich schnell entwickelnden Krankheitserregern, die Vorfahren-Nachfahren-Paare enthalten können. (2) Die weit verbreiteten Modelle der Genevolution gehen unrealistischerweise davon aus, dass sich die Basenzusammensetzung von DNA-Sequenzen nicht ändert. Bezüglich Problem (1) stellen wir eine Methode zur Ableitung von allgemein markierten phylogenetischen Bäumen vor, die es erlaubt, Spezies, für die Proben vorliegen, an inneren des Baumes zu platzieren. Bezüglich Problem (2) stellen wir eine strukturelle Expectation-Maximization-Methode (SEM-GM) zur Ableitung von blattmarkierten phylogenetischen Bäumen unter dem allgemeinen Markov-Modell (GM) vor, das das komplexeste Modell von DNA-Substitution ist und das die Evolution von Basenzusammensetzung erlaubt. Um die Skalierbarkeit von SEM-GM zu verbessern, stellen wir ein Minimale Spannbaum (MST)-Methode vor, die als MST-Backbone bezeichnet wird. MST-Backbone skaliert linear mit der Anzahl der Blätter. Die Tatsache, dass die Lage der Wurzel aus empirischen Daten nicht immer realistisch abgeleitet warden kann, legt jedoch nahe, dass das GM-Modell möglicherweise übertrainiert ist. MST-backbone wurde von einer topologischen Beziehung zwischen minimalen Spannbäumen und phylogenetischen Bäumen inspiriert, die von Choi et al. 2011 eingeführt wurde. Wir entdeckten, dass die topologische Beziehung nicht unbedingt Bestand hat, wenn es keinen eindeutigen minimalen Spannbaum gibt. Wir schlagen so genannte vertex-order-based MSTs (VMSTs) vor, die eine topologische Beziehung zu phylogenetischen Bäumen garantieren

Rec-DCM-Eigen: Reconstructing a Less Parsimonious but More Accurate Tree in Shorter Time

Maximum parsimony (MP) methods aim to reconstruct the phylogeny of extant species by finding the most parsimonious evolutionary scenario using the species' genome data. MP methods are considered to be accurate, but they are also computationally expensive especially for a large number of species. Several disk-covering methods (DCMs), which decompose the input species to multiple overlapping subgroups (or disks), have been proposed to solve the problem in a divide-and-conquer way

Joint assembly and genetic mapping of the Atlantic horseshoe crab genome reveals ancient whole genome duplication

Horseshoe crabs are marine arthropods with a fossil record extending back approximately 450 million years. They exhibit remarkable morphological stability over their long evolutionary history, retaining a number of ancestral arthropod traits, and are often cited as examples of "living fossils." As arthropods, they belong to the Ecdysozoa}, an ancient super-phylum whose sequenced genomes (including insects and nematodes) have thus far shown more divergence from the ancestral pattern of eumetazoan genome organization than cnidarians, deuterostomes, and lophotrochozoans. However, much of ecdysozoan diversity remains unrepresented in comparative genomic analyses. Here we use a new strategy of combined de novo assembly and genetic mapping to examine the chromosome-scale genome organization of the Atlantic horseshoe crab Limulus polyphemus. We constructed a genetic linkage map of this 2.7 Gbp genome by sequencing the nuclear DNA of 34 wild-collected, full-sibling embryos and their parents at a mean redundancy of 1.1x per sample. The map includes 84,307 sequence markers and 5,775 candidate conserved protein coding genes. Comparison to other metazoan genomes shows that the L. polyphemus genome preserves ancestral bilaterian linkage groups, and that a common ancestor of modern horseshoe crabs underwent one or more ancient whole genome duplications (WGDs) ~ 300 MYA, followed by extensive chromosome fusion

STATISTICS IN THE BILLERA-HOLMES-VOGTMANN TREESPACE

This dissertation is an effort to adapt two classical non-parametric statistical techniques, kernel density estimation (KDE) and principal components analysis (PCA), to the Billera-Holmes-Vogtmann (BHV) metric space for phylogenetic trees. This adaption gives a more general framework for developing and testing various hypotheses about apparent differences or similarities between sets of phylogenetic trees than currently exists. For example, while the majority of gene histories found in a clade of organisms are expected to be generated by a common evolutionary process, numerous other coexisting processes (e.g. horizontal gene transfers, gene duplication and subsequent neofunctionalization) will cause some genes to exhibit a history quite distinct from the histories of the majority of genes. Such “outlying” gene trees are considered to be biologically interesting and identifying these genes has become an important problem in phylogenetics. The R sofware package kdetrees, developed in Chapter 2, contains an implementation of the kernel density estimation method. The primary theoretical difficulty involved in this adaptation concerns the normalizion of the kernel functions in the BHV metric space. This problem is addressed in Chapter 3. In both chapters, the software package is applied to both simulated and empirical datasets to demonstrate the properties of the method. A few first theoretical steps in adaption of principal components analysis to the BHV space are presented in Chapter 4. It becomes necessary to generalize the notion of a set of perpendicular vectors in Euclidean space to the BHV metric space, but there some ambiguity about how to best proceed. We show that convex hulls are one reasonable approach to the problem. The Nye-PCA- algorithm provides a method of projecting onto arbitrary convex hulls in BHV space, providing the core of a modified PCA-type method

Graph-based methods for large-scale protein classification and orthology inference

The quest for understanding how proteins evolve and function has been a prominent and costly human endeavor. With advances in genomics and use of bioinformatics tools, the diversity of proteins in present day genomes can now be studied more efficiently than ever before. This thesis describes computational methods suitable for large-scale protein classification of many proteomes of diverse species. Specifically, we focus on methods that combine unsupervised learning (clustering) techniques with the knowledge of molecular phylogenetics, particularly that of orthology. In chapter 1 we introduce the biological context of protein structure, function and evolution, review the state-of-the-art sequence-based protein classification methods, and then describe methods used to validate the predictions. Finally, we present the outline and objectives of this thesis. Evolutionary (phylogenetic) concepts are instrumental in studying subjects as diverse as the diversity of genomes, cellular networks, protein structures and functions, and functional genome annotation. In particular, the detection of orthologous proteins (genes) across genomes provides reliable means to infer biological functions and processes from one organism to another. Chapter 2 evaluates the available computational tools, such as algorithms and databases, used to infer orthologous relationships between genes from fully sequenced genomes. We discuss the main caveats of large-scale orthology detection in general as well as the merits and pitfalls of each method in particular. We argue that establishing true orthologous relationships requires a phylogenetic approach which combines both trees and graphs (networks), reliable species phylogeny, genomic data for more than two species, and an insight into the processes of molecular evolution. Also proposed is a set of guidelines to aid researchers in selecting the correct tool. Moreover, this review motivates further research in developing reliable and scalable methods for functional and phylogenetic classification of large protein collections. Chapter 3 proposes a framework in which various protein knowledge-bases are combined into unique network of mappings (links), and hence allows comparisons to be made between expert curated and fully-automated protein classifications from a single entry point. We developed an integrated annotation resource for protein orthology, ProGMap (Protein Group Mappings, http://www.bioinformatics.nl/progmap), to help researchers and database annotators who often need to assess the coherence of proposed annotations and/or group assignments, as well as users of high throughput methodologies (e.g., microarrays or proteomics) who deal with partially annotated genomic data. ProGMap is based on a non-redundant dataset of over 6.6 million protein sequences which is mapped to 240,000 protein group descriptions collected from UniProt, RefSeq, Ensembl, COG, KOG, OrthoMCL-DB, HomoloGene, TRIBES and PIRSF using a fast and fully automated sequence-based mapping approach. The ProGMap database is equipped with a web interface that enables queries to be made using synonymous sequence identifiers, gene symbols, protein functions, and amino acid or nucleotide sequences. It incorporates also services, namely BLAST similarity search and QuickMatch identity search, for finding sequences similar (or identical) to a query sequence, and tools for presenting the results in graphic form. Graphs (networks) have gained an increasing attention in contemporary biology because they have enabled complex biological systems and processes to be modeled and better understood. For example, protein similarity networks constructed of all-versus-all sequence comparisons are frequently used to delineate similarity groups, such as protein families or orthologous groups in comparative genomics studies. Chapter 4.1 presents a benchmark study of freely available graph software used for this purpose. Specifically, the computational complexity of the programs is investigated using both simulated and biological networks. We show that most available software is not suitable for large networks, such as those encountered in large-scale proteome analyzes, because of the high demands on computational resources. To address this, we developed a fast and memory-efficient graph software, netclust (http://www.bioinformatics.nl/netclust/), which can scale to large protein networks, such as those constructed of millions of proteins and sequence similarities, on a standard computer. An extended version of this program called Multi-netclust is presented in chapter 4.2. This tool that can find connected clusters of data presented by different network data sets. It uses user-defined threshold values to combine the data sets in such a way that clusters connected in all or in either of the networks can be retrieved efficiently. Automated protein sequence clustering is an important task in genome annotation projects and phylogenomic studies. During the past years, several protein clustering programs have been developed for delineating protein families or orthologous groups from large sequence collections. However, most of these programs have not been benchmarked systematically, in particular with respect to the trade-off between computational complexity and biological soundness. In chapter 5 we evaluate three best known algorithms on different protein similarity networks and validation (or 'gold' standard) data sets to find out which one can scale to hundreds of proteomes and still delineate high quality similarity groups at the minimum computational cost. For this, a reliable partition-based approach was used to assess the biological soundness of predicted groups using known protein functions, manually curated protein/domain families and orthologous groups available in expert-curated databases. Our benchmark results support the view that a simple and computationally cheap method such as netclust can perform similar to and in cases even better than more sophisticated, yet much more costly methods. Moreover, we introduce an efficient graph-based method that can delineate protein orthologs of hundreds of proteomes into hierarchical similarity groups de novo. The validity of this method is demonstrated on data obtained from 347 prokaryotic proteomes. The resulting hierarchical protein classification is not only in agreement with manually curated classifications but also provides an enriched framework in which the functional and evolutionary relationships between proteins can be studied at various levels of specificity. Finally, in chapter 6 we summarize the main findings and discuss the merits and shortcomings of the methods developed herein. We also propose directions for future research. The ever increasing flood of new sequence data makes it clear that we need improved tools to be able to handle and extract relevant (orthological) information from these protein data. This thesis summarizes these needs and how they can be addressed by the available tools, or be improved by the new tools that were developed in the course of this research. <br/

Whole-genome sequence analysis for pathogen detection and diagnostics

This dissertation focuses on computational methods for improving the accuracy of commonly used nucleic acid tests for pathogen detection and diagnostics. Three specific biomolecular techniques are addressed: polymerase chain reaction, microarray comparative genomic hybridization, and whole-genome sequencing. These methods are potentially the future of diagnostics, but each requires sophisticated computational design or analysis to operate effectively. This dissertation presents novel computational methods that unlock the potential of these diagnostics by efficiently analyzing whole-genome DNA sequences. Improvements in the accuracy and resolution of each of these diagnostic tests promises more effective diagnosis of illness and rapid detection of pathogens in the environment. For designing real-time detection assays, an efficient data structure and search algorithm are presented to identify the most distinguishing sequences of a pathogen that are absent from all other sequenced genomes. Results are presented that show these "signature" sequences can be used to detect pathogens in complex samples and differentiate them from their non-pathogenic, phylogenetic near neighbors. For microarray, novel pan-genomic design and analysis methods are presented for the characterization of unknown microbial isolates. To demonstrate the effectiveness of these methods, pan-genomic arrays are applied to the study of multiple strains of the foodborne pathogen, Listeria monocytogenes, revealing new insights into the diversity and evolution of the species. Finally, multiple methods are presented for the validation of whole-genome sequence assemblies, which are capable of identifying assembly errors in even finished genomes. These validated assemblies provide the ultimate nucleic acid diagnostic, revealing the entire sequence of a genome

The impact and pattern of gene and genome duplication in the history of increasing organismal complexity

The increase in phenotypic or morphological complexity in organisms may stem from a corresponding increase in the complexity of the underlying genetic architecture, driven by the process of gene duplication. Gene duplication is a mutational mechanism that can impact the genome through the gradual birth and death of individual genes or clusters of genes, and through infrequent episodic events of whole genome duplication. Functional and pleiotropic differences among genes may impact the probability of fixation of duplicated genes and the facility of gene families to record duplication events across deep history. Phylogenetic inference of the relationship among genes in multigene families has been used to reconstruct the history of duplication and subsequently to test hypotheses about the tempo and mode of these mutational mechanisms. We were unable to refute the hypothesis that one or two rounds of tetraploid evolution occurred subsequent to the origin of the lower deuterostomes and immediately preceding the origin of chordates. Our results suggest that the evolutionary history of gene families is defined by the nature of selection on individual genes. Genes embedded in highly constrained pleiotropic networks appear to have different patterns of diversification than genes subject to lesser (or different) selective constraints