Étude de l’évolution des génomes par duplications, pertes et réarrangements

La duplication est un des évènements évolutifs les plus importants, car elle peut mener à la création de nouvelles fonctions géniques. Durant leur évolution, les génomes sont aussi affectés par des inversions, des translocations (incluant des fusions et fissions de chromosomes), des transpositions et des délétions. L'étude de l'évolution des génomes est importante, notamment pour mieux comprendre les mécanismes biologiques impliqués, les types d'évènements qui sont les plus fréquents et quels étaient les contenus en gènes des espèces ancestrales. Afin d'analyser ces différents aspects de l'évolution des génomes, des algorithmes efficaces doivent être créés pour inférer des génomes ancestraux, des histoires évolutives, des relations d'homologies et pour calculer les distances entre les génomes. Dans cette thèse, quatre projets reliés à l'étude et à l'analyse de l'évolution des génomes sont présentés : 1) Nous proposons deux algorithmes pour résoudre des problèmes reliés à la duplication de génome entier : un qui généralise le problème du genome halving aux pertes de gènes et un qui permet de calculer la double distance avec pertes. 2) Nous présentons une nouvelle méthode pour l'inférence d'histoires évolutives de groupes de gènes orthologues répétés en tandem. 3) Nous proposons une nouvelle approche basée sur la théorie des graphes pour inférer des gènes in-paralogues qui considère simultanément l'information provenant de différentes espèces afin de faire de meilleures prédictions. 4) Nous présentons une étude de l'histoire évolutive des gènes d'ARN de transfert chez 50 souches de Bacillus.Gene duplication is one of the most important types of events affecting genomes during their evolution because it can create novel gene function. During the evolution process, genomes are also affected by inversions, translocations (including chromosome fusions and fissions), transpositions and deletions. Studying the evolution of genomes is important to get a better understanding of the biological mechanisms involved, which types of events are more frequent than others and what was the gene content in the ancestral species just to name a few. In order to analyze these different aspects of genome evolution, efficient algorithms need to be developed to infer ancestral genomes, evolutionary histories, homology relationships between genes and to compute distances between genomes. In this thesis, four different projects related to the study and analysis of genome evolution are presented: 1) We developed two algorithms to solve problems related to whole genome duplication: one that generalizes the genome halving problem to gene losses, and one that allows to compute the double distance with losses. 2) We developed a new method to infer evolutionary histories of orthologous tandemly arrayed gene clusters. 3) We proposed a new graph-theoretic approach to infer inparalogs that simultaneously considers the information given by multiple species in order to make better inferences of inparalogous gene pairs. 4) We studied the evolutionary history of the tRNA genes of 50 Bacillus strains

Bayesian network learning and applications in Bioinformatics

Abstract A Bayesian network (BN) is a compact graphic representation of the probabilistic re- lationships among a set of random variables. The advantages of the BN formalism include its rigorous mathematical basis, the characteristics of locality both in knowl- edge representation and during inference, and the innate way to deal with uncertainty. Over the past decades, BNs have gained increasing interests in many areas, including bioinformatics which studies the mathematical and computing approaches to under- stand biological processes. In this thesis, I develop new methods for BN structure learning with applications to bi- ological network reconstruction and assessment. The first application is to reconstruct the genetic regulatory network (GRN), where each gene is modeled as a node and an edge indicates a regulatory relationship between two genes. In this task, we are given time-series microarray gene expression measurements for tens of thousands of genes, which can be modeled as true gene expressions mixed with noise in data generation, variability of the underlying biological systems etc. We develop a novel BN structure learning algorithm for reconstructing GRNs. The second application is to develop a BN method for protein-protein interaction (PPI) assessment. PPIs are the foundation of most biological mechanisms, and the knowl- edge on PPI provides one of the most valuable resources from which annotations of genes and proteins can be discovered. Experimentally, recently-developed high- throughput technologies have been carried out to reveal protein interactions in many organisms. However, high-throughput interaction data often contain a large number of iv spurious interactions. In this thesis, I develop a novel in silico model for PPI assess- ment. Our model is based on a BN that integrates heterogeneous data sources from different organisms. The main contributions are: 1. A new concept to depict the dynamic dependence relationships among random variables, which widely exist in biological processes, such as the relationships among genes and genes' products in regulatory networks and signaling pathways. This con- cept leads to a novel algorithm for dynamic Bayesian network learning. We apply it to time-series microarray gene expression data, and discover some missing links in a well-known regulatory pathway. Those new causal relationships between genes have been found supportive evidences in literature. 2. Discovery and theoretical proof of an asymptotic property of K2 algorithm ( a well-known efficient BN structure learning approach). This property has been used to identify Markov blankets (MB) in a Bayesian network, and further recover the BN structure. This hybrid algorithm is evaluated on a benchmark regulatory pathway, and obtains better results than some state-of-art Bayesian learning approaches. 3. A Bayesian network based integrative method which incorporates heterogeneous data sources from different organisms to predict protein-protein interactions (PPI) in a target organism. The framework is employed in human PPI prediction and in as- sessment of high-throughput PPI data. Furthermore, our experiments reveal some interesting biological results. 4. We introduce the learning of a TAN (Tree Augmented Naïve Bayes) based net- work, which has the computational simplicity and robustness to high-throughput PPI assessment. The empirical results show that our method outperforms naïve Bayes and a manual constructed Bayesian Network, additionally demonstrate sufficient informa- tion from model organisms can achieve high accuracy in PPI prediction

INTEGRATING PHYLOGENOMICS AND CHROMOSOME MAPPING TO STUDY THE EVOLUTIONARY RELATIONSHIPS AMONG EUKARYOTES AND THE EVOLUTION OF THEIR GENOMES

Our knowledge about the evolution of eukaryotes and their genomes is very limited because it has largely been based on studies of plants, animals and fungi, which are not a significant representation of the diversity across the eukaryotic tree of life. Advances in sequencing technologies are helping to expand our knowledge by including underrepresented clades and revealing that eukaryotic genomes are much more complex and dynamic than originally thought. In response to the need to explore such levels of complexity in eukaryotic genomes and the earliest events of eukaryotic evolution, this dissertation focuses on the development of bioinformatic and phylogenomic tools to study karyotype evolution and answering deep evolutionary questions. The first chapter covers the development of a phylogenomic chromosome mapper, PhyloChromoMap, and its use to study karyotype evolution in the malaria parasite Plasmodium falciparum. In addition to providing a very flexible and powerful tool to map the phylogenetic history of genes across karyotypes, this chapter reveals very distinctive patterns of evolution between subtelomeric and internal regions of the chromosomes of P. falciparum. The second chapter focuses on the development of PhyloToL, a taxon- and gene-rich phylogenomic pipeline. This chapter presents examples of how to use PhyloToL for phylogenomic studies and studies of gene family evolution, and presents a series of benchmark studies comparing PhyloToL against other popular phylogenomic pipelines. Finally, the third chapter focuses on using PhyloToL to explore one of the most critical questions in field of evolution, the root of the eukaryotic tree of life. The results in this chapter suggest that the root should be placed between Opisthokonta and all other eukaryotes. Overall this dissertation contributes insights of the earliest events of evolution in eukaryotes and provides novel approaches to study this topic. The results of this dissertation are important for comparative biology as it allows to understand the timing and mode of evolution of eukaryotic features across the eukaryotic tree of lif

Ontology-based similarity measures and their application in bioinformatics

Genome-wide sequencing projects of many different organisms produce large numbers of sequences that are functionally characterized using experimental and bioinformatics methods. Following the development of the first bio-ontologies, knowledge of the functions of genes and proteins is increasingly made available in a standardized format. This allows for devising approaches that directly exploit functional information using semantic and functional similarity measures. This thesis addresses different aspects of the development and application of such similarity measures. First, we analyze semantic and functional similarity measures and apply them for investigating the functional space in different taxa. Second, a new software program and a new database are described, which overcome limitations of existing tools and simplify the utilization of similarity measures for different applications. Third, we delineate two applications of our functional similarity measures. We utilize them for analyzing domain and protein interaction datasets and derive thresholds for grouping predicted domain interactions into low- and high-confidence subsets. We also present the new MedSim method for prioritization of candidate disease genes, which is based on the observation that genes and proteins contributing to similar diseases are functionally related. We demonstrate that the MedSim method performs at least as well as more complex state-of-the-art methods and significantly outperforms current methods that also utilize functional annotation.Die Sequenzierung der kompletten Genome vieler verschiedener Organismen liefert eine große Anzahl an Sequenzen, die mit Hilfe experimenteller und bioinformatischer Methoden funktionell charakterisiert werden. Nach der Entwicklung der ersten Bio-Ontologien wird das Wissen über die Funktionen von Genen und Proteinen zunehmend in einem standardisierten Format zur Verfügung gestellt. Dadurch wird die Entwicklung von Verfahren ermöglicht, die funktionelle Informationen direkt mit Hilfe semantischer und funktioneller Ähnlichkeit verwenden. Diese Doktorarbeit befasst sich mit verschiedenen Aspekten der Entwicklung und Anwendung solcher Ähnlichkeitsmaße. Zuerst analysieren wir semantische und funktionelle Ähnlichkeitsmaße und verwenden sie für eine Analyse des funktionellen Raumes verschiedener Organismengruppen. Danach beschreiben wir eine neue Software und eine neue Datenbank, die Limitationen existierender Programme überwinden und den Einsatz von Ähnlichkeitsmaßen in verschiedenen Anwendungen vereinfachen. Drittens schildern wir zwei Anwendungen unserer funktionellen Ähnlichkeitsmaße. Wir verwenden sie zur Analyse von Domän- und Proteininteraktionsdatensätzen und leiten Grenzwerte ab, um die Domäninteraktionen in Teilmengen mit niedriger und hoher Konfidenz einzuteilen. Außerdem präsentieren wir die MedSim-Methode zur Priorisierung von potentiellen Krankheitsgenen. Sie beruht auf der Beobachtung, dass Gene und Proteine, die zu ähnlichen Krankheiten beitragen, funktionell verwandt sind. Wir zeigen, dass die MedSim-Methode mindestens so gut funktioniert wie komplexere moderne Methoden und die Leistung anderer aktueller Methoden signifikant übertrifft, die auch funktionelle Annotationen verwenden

Cross-species network and transcript transfer

Metabolic processes, signal transduction, gene regulation, as well as gene and protein expression are largely controlled by biological networks. High-throughput experiments allow the measurement of a wide range of cellular states and interactions. However, networks are often not known in detail for specific biological systems and conditions. Gene and protein annotations are often transferred from model organisms to the species of interest. Therefore, the question arises whether biological networks can be transferred between species or whether they are specific for individual contexts. In this thesis, the following aspects are investigated: (i) the conservation and (ii) the cross-species transfer of eukaryotic protein-interaction and gene regulatory (transcription factor- target) networks, as well as (iii) the conservation of alternatively spliced variants. In the simplest case, interactions can be transferred between species, based solely on the sequence similarity of the orthologous genes. However, such a transfer often results either in the transfer of only a few interactions (medium/high sequence similarity threshold) or in the transfer of many speculative interactions (low sequence similarity threshold). Thus, advanced network transfer approaches also consider the annotations of orthologous genes involved in the interaction transfer, as well as features derived from the network structure, in order to enable a reliable interaction transfer, even between phylogenetically very distant species. In this work, such an approach for the transfer of protein interactions is presented (COIN). COIN uses a sophisticated machine-learning model in order to label transferred interactions as either correctly transferred (conserved) or as incorrectly transferred (not conserved). The comparison and the cross-species transfer of regulatory networks is more difficult than the transfer of protein interaction networks, as a huge fraction of the known regulations is only described in the (not machine-readable) scientific literature. In addition, compared to protein interactions, only a few conserved regulations are known, and regulatory elements appear to be strongly context-specific. In this work, the cross-species analysis of regulatory interaction networks is enabled with software tools and databases for global (ConReg) and thousands of context-specific (CroCo) regulatory interactions that are derived and integrated from the scientific literature, binding site predictions and experimental data. Genes and their protein products are the main players in biological networks. However, to date, the aspect is neglected that a gene can encode different proteins. These alternative proteins can differ strongly from each other with respect to their molecular structure, function and their role in networks. The identification of conserved and species-specific splice variants and the integration of variants in network models will allow a more complete cross-species transfer and comparison of biological networks. With ISAR we support the cross-species transfer and comparison of alternative variants by introducing a gene-structure aware (i.e. exon-intron structure aware) multiple sequence alignment approach for variants from orthologous and paralogous genes. The methods presented here and the appropriate databases allow the cross-species transfer of biological networks, the comparison of thousands of context-specific networks, and the cross-species comparison of alternatively spliced variants. Thus, they can be used as a starting point for the understanding of regulatory and signaling mechanisms in many biological systems.In biologischen Systemen werden Stoffwechselprozesse, Signalübertragungen sowie die Regulation von Gen- und Proteinexpression maßgeblich durch biologische Netzwerke gesteuert. Hochdurchsatz-Experimente ermöglichen die Messung einer Vielzahl von zellulären Zuständen und Wechselwirkungen. Allerdings sind für die meisten Systeme und Kontexte biologische Netzwerke nach wie vor unbekannt. Gen- und Proteinannotationen werden häufig von Modellorganismen übernommen. Demnach stellt sich die Frage, ob auch biologische Netzwerke und damit die systemischen Eigenschaften ähnlich sind und übertragen werden können. In dieser Arbeit wird: (i) Die Konservierung und (ii) die artenübergreifende Übertragung von eukaryotischen Protein-Interaktions- und regulatorischen (Transkriptionsfaktor-Zielgen) Netzwerken, sowie (iii) die Konservierung von Spleißvarianten untersucht. Interaktionen können im einfachsten Fall nur auf Basis der Sequenzähnlichkeit zwischen orthologen Genen übertragen werden. Allerdings führt eine solche Übertragung oft dazu, dass nur sehr wenige Interaktionen übertragen werden können (hoher bis mittlerer Sequenzschwellwert) oder dass ein Großteil der übertragenden Interaktionen sehr spekulativ ist (niedriger Sequenzschwellwert). Verbesserte Methoden berücksichtigen deswegen zusätzlich noch die Annotationen der Orthologen, Eigenschaften der Interaktionspartner sowie die Netzwerkstruktur und können somit auch Interaktionen auf phylogenetisch weit entfernte Arten (zuverlässig) übertragen. In dieser Arbeit wird ein solcher Ansatz für die Übertragung von Protein-Interaktionen vorgestellt (COIN). COIN verwendet Verfahren des maschinellen Lernens, um Interaktionen als richtig (konserviert) oder als falsch übertragend (nicht konserviert) zu klassifizieren. Der Vergleich und die artenübergreifende Übertragung von regulatorischen Interaktionen ist im Vergleich zu Protein-Interaktionen schwieriger, da ein Großteil der bekannten Regulationen nur in der (nicht maschinenlesbaren) wissenschaftlichen Literatur beschrieben ist. Zudem sind im Vergleich zu Protein-Interaktionen nur wenige konservierte Regulationen bekannt und regulatorische Elemente scheinen stark kontextabhängig zu sein. In dieser Arbeit wird die artenübergreifende Analyse von regulatorischen Netzwerken mit Softwarewerkzeugen und Datenbanken für globale (ConReg) und kontextspezifische (CroCo) regulatorische Interaktionen ermöglicht. Regulationen wurden dafür aus Vorhersagen, experimentellen Daten und aus der wissenschaftlichen Literatur abgeleitet und integriert. Grundbaustein für viele biologische Netzwerke sind Gene und deren Proteinprodukte. Bisherige Netzwerkmodelle vernachlässigen allerdings meist den Aspekt, dass ein Gen verschiedene Proteine kodieren kann, die sich von der Funktion, der Proteinstruktur und der Rolle in Netzwerken stark voneinander unterscheiden können. Die Identifizierung von konservierten und artspezifischen Proteinprodukten und deren Integration in Netzwerkmodelle würde einen vollständigeren Übertrag und Vergleich von Netzwerken ermöglichen. In dieser Arbeit wird der artenübergreifende Vergleich von Proteinprodukten mit einem multiplen Sequenzalignmentverfahren für alternative Varianten von paralogen und orthologen Genen unterstützt, unter Berücksichtigung der bekannten Exon-Intron-Grenzen (ISAR). Die in dieser Arbeit vorgestellten Verfahren, Datenbanken und Softwarewerkzeuge ermöglichen die Übertragung von biologischen Netzwerken, den Vergleich von tausenden kontextspezifischen Netzwerken und den artenübergreifenden Vergleich von alternativen Varianten. Sie können damit die Ausgangsbasis für ein Verständnis von Kommunikations- und Regulationsmechanismen in vielen biologischen Systemen bilden