Nicolas Wieseke

A parameter-adaptive dynamic programming approach for inferring cophylogenies

BackgroundCoevolutionary systems like hosts and their parasites are commonly used model systems for evolutionary studies. Inferring the coevolutionary history based on given phylogenies of both groups is often done by employing a set of possible types of events that happened during coevolution. Costs are assigned to the different types of events and a reconstruction of the common history with a minimal sum of event costs is sought.ResultsThis paper introduces a new algorithm and a corresponding tool called CoRe-PA, that can be used to infer the common history of coevolutionary systems. The proposed method utilizes an event-based concept for reconciliation analyses where the possible events are cospeciations, sortings, duplications, and (host) switches. All known event-based approaches so far assign costs to each type of cophylogenetic events in order to find a cost-minimal reconstruction. CoRe-PA uses a new parameter-adaptive approach, i.e., no costs have to be assigned to the coevolutionary events in advance. Several biological coevolutionary systems that have already been studied intensely in literature are used to show the performance of CoRe-PA.ConclusionFrom a biological point of view reasonable cost values for event-based reconciliations can often be estimated only very roughly. CoRe-PA is very useful when it is difficult or impossible to assign exact cost values to different types of coevolutionary events in advance.

Orthology detection combining clustering and synteny for very large datasets.

The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets.

Orthology relations, symbolic ultrametrics, and cographs.

Orthology detection is an important problem in comparative and evolutionary genomics and, consequently, a variety of orthology detection methods have been devised in recent years. Although many of these methods are dependent on generating gene and/or species trees, it has been shown that orthology can be estimated at acceptable levels of accuracy without having to infer gene trees and/or reconciling gene trees with species trees. Thus, it is of interest to understand how much information about the gene tree, the species tree, and their reconciliation is already contained in the orthology relation on the underlying set of genes. Here we shall show that a result by Böcker and Dress concerning symbolic ultrametrics, and subsequent algorithmic results by Semple and Steel for processing these structures can throw a considerable amount of light on this problem. More specifically, building upon these authors’ results, we present some new characterizations for symbolic ultrametrics and new algorithms for recovering the associated trees, with an emphasis on how these algorithms could be potentially extended to deal with arbitrary orthology relations. In so doing we shall also show that, somewhat surprisingly, symbolic ultrametrics are very closely related to cographs, graphs that do not contain an induced path on any subset of four vertices. We conclude with a discussion on how our results might be applied in practice to orthology detection.

Phylogenomics with paralogs

Significance We demonstrate that the distribution of paralogs in large gene families contains in itself sufficient phylogenetic signal to infer fully resolved species phylogenies. This source of phylogenetic information is independent of information contained in orthologous sequences and is resilient against horizontal gene transfer. An important consequence is that phylogenomics data sets need not be restricted to 1:1 orthologs. Phylogenomics heavily relies on well-curated sequence data sets that comprise, for each gene, exclusively 1:1 orthologos. Paralogs are treated as a dangerous nuisance that has to be detected and removed. We show here that this severe restriction of the data sets is not necessary. Building upon recent advances in mathematical phylogenetics, we demonstrate that gene duplications convey meaningful phylogenetic information and allow the inference of plausible phylogenetic trees, provided orthologs and paralogs can be distinguished with a degree of certainty. Starting from tree-free estimates of orthology, cograph editing can sufficiently reduce the noise to find correct event-annotated gene trees. The information of gene trees can then directly be translated into constraints on the species trees. Although the resolution is very poor for individual gene families, we show that genome-wide data sets are sufficient to generate fully resolved phylogenetic trees, even in the presence of horizontal gene transfer.

Genetic characterization of Tribec virus and Kemerovo virus, two tick-transmitted human-pathogenic Orbiviruses

We determined the complete genome sequences of Tribeč virus (TRBV) and Kemerovo virus (KEMV), two tick-transmitted Orbiviruses that can cause diseases of the central nervous system and that are currently classified into the Great Island virus serogroup. VP2 proteins of TRBV and KEMV show very low sequence similarity to the homologous VP4 protein of tick-transmitted Great Island virus (GIV). The new sequence data support previous serological classification of these Orbiviruses into the Kemerovo serogroup, which is different from the Great Island virus serogroup. Genome segment 9 of TRBV and KEMV encodes several overlapping ORFs in the +1 reading frame relative to VP6(Hel). A co-phylogenetic analysis indicates a host switch from insect-borne Orbiviruses toward Ixodes species, which is in disagreement with previously published data.

From event-labeled gene trees to species trees

BackgroundTree reconciliation problems have long been studied in phylogenetics. A particular variant of the reconciliation problem for a gene tree T and a species tree S assumes that for each interior vertex x of T it is known whether x represents a speciation or a duplication. This problem appears in the context of analyzing orthology data.ResultsWe show that S is a species tree for T if and only if S displays all rooted triples of T that have three distinct species as their leaves and are rooted in a speciation vertex. A valid reconciliation map can then be found in polynomial time. Simulated data shows that the event-labeled gene trees convey a large amount of information on underlying species trees, even for a large percentage of losses.ConclusionsThe knowledge of event labels in a gene tree strongly constrains the possible species tree and, for a given species tree, also the possible reconciliation maps. Nevertheless, many degrees of freedom remain in the space of feasible solutions. In order to disambiguate the alternative solutions additional external constraints as well as optimization criteria could be employed.

The paralog-to-contig assignment problem: high quality gene models from fragmented assemblies.

BackgroundThe accurate annotation of genes in newly sequenced genomes remains a challenge. Although sophisticated comparative pipelines are available, computationally derived gene models are often less than perfect. This is particularly true when multiple similar paralogs are present. The issue is aggravated further when genomes are assembled only at a preliminary draft level to contigs or short scaffolds. However, these genomes deliver valuable information for studying gene families. High accuracy models of protein coding genes are needed in particular for phylogenetics and for the analysis of gene family histories.ResultsWe present a pipeline, ExonMatchSolver, that is designed to help the user to produce and curate high quality models of the protein-coding part of genes. The tool in particular tackles the problem of identifying those coding exon groups that belong to the same paralogous genes in a fragmented genome assembly. This paralog-to-contig assignment problem is shown to be NP-complete. It is phrased and solved as an Integer Linear Programming problem.ConclusionsThe ExonMatchSolver-pipeline can be employed to build highly accurate models of protein coding genes even when spanning several genomic fragments. This sets the stage for a better understanding of the evolutionary history within particular gene families which possess a large number of paralogs and in which frequent gene duplication events occurred.

Malagasy bats shelter a considerable genetic diversity of pathogenic Leptospira suggesting notable host-specificity patterns

Pathogenic Leptospira are the causative agents of leptospirosis, a disease of global concern with major impact in tropical regions. Despite the importance of this zoonosis for human health, the evolutionary and ecological drivers shaping bacterial communities in host reservoirs remain poorly investigated. Here, we describe Leptospira communities hosted by Malagasy bats, composed of mostly endemic species, in order to characterize host-pathogen associations and investigate their evolutionary histories. We screened 947 individual bats (representing 31 species, 18 genera and seven families) for Leptospira infection and subsequently genotyped positive samples using three different bacterial loci. Molecular identification showed that these Leptospira are notably diverse and include several distinct lineages mostly belonging to Leptospira borgpetersenii and L. kirschneri. The exploration of the most probable host-pathogen evolutionary scenarios suggests that bacterial genetic diversity results from a combination of events related to the ecology and the evolutionary history of their hosts. Importantly, based on the data set presented herein, the notable host-specificity we have uncovered, together with a lack of geographical structuration of bacterial genetic diversity, indicates that the Leptospira community at a given site depends on the co-occurring bat species assemblage. The implications of such tight host-specificity on the epidemiology of leptospirosis are discussed.

On tree representations of relations and graphs: symbolic ultrametrics and cograph edge decompositions

Tree representations of (sets of) symmetric binary relations, or equivalently edge-colored undirected graphs, are of central interest, e.g. in phylogenomics. In this context symbolic ultrametrics play a crucial role. Symbolic ultrametrics define an edge-colored complete graph that allows to represent the topology of this graph as a vertex-colored tree. Here, we are interested in the structure and the complexity of certain combinatorial problems resulting from considerations based on symbolic ultrametrics, and on algorithms to solve them.This includes, the characterization of symbolic ultrametrics that additionally distinguishes between edges and non-edges of arbitrary edge-colored graphs G and thus, yielding a tree representation of G, by means of so-called cographs. Moreover, we address the problem of finding “closest” symbolic ultrametrics and show the NP-completeness of the three problems: symbolic ultrametric editing, completion and deletion. Finally, as not all graphs are cographs, and hence, do not have a tree representation, we ask, furthermore, what is the minimum number of cotrees needed to represent the topology of an arbitrary non-cograph G. This is equivalent to find an optimal cograph edge k-decomposition