Algorithm of OMA for large-scale orthology inference

Since the publication of our article (Roth, Gonnet, and Dessimoz: BMC Bioinformatics 2008 9: 518), we have noticed several errors, which we correct in the following

A MOSAIC of methods: Improving ortholog detection through integration of algorithmic diversity

Ortholog detection (OD) is a critical step for comparative genomic analysis of protein-coding sequences. In this paper, we begin with a comprehensive comparison of four popular, methodologically diverse OD methods: MultiParanoid, Blat, Multiz, and OMA. In head-to-head comparisons, these methods are shown to significantly outperform one another 12-30% of the time. This high complementarity motivates the presentation of the first tool for integrating methodologically diverse OD methods. We term this program MOSAIC, or Multiple Orthologous Sequence Analysis and Integration by Cluster optimization. Relative to component and competing methods, we demonstrate that MOSAIC more than quintuples the number of alignments for which all species are present, while simultaneously maintaining or improving functional-, phylogenetic-, and sequence identity-based measures of ortholog quality. Further, we demonstrate that this improvement in alignment quality yields 40-280% more confidently aligned sites. Combined, these factors translate to higher estimated levels of overall conservation, while at the same time allowing for the detection of up to 180% more positively selected sites. MOSAIC is available as python package. MOSAIC alignments, source code, and full documentation are available at http://pythonhosted.org/bio-MOSAIC

Inferring Hierarchical Orthologous Groups

The reconstruction of ancestral evolutionary histories is the cornerstone of most phylogenetic analyses. Many applications are possible once the evolutionary history is unveiled, such as identifying taxonomically restricted genes (genome barcoding), predicting the function of unknown genes based on their evolutionary related genes gene ontologies, identifying gene losses and gene gains among gene families, or pinpointing the time in evolution where particular gene families emerge (sometimes referred to as “phylostratigraphy”). Typically, the reconstruction of the evolutionary histories is limited to the inference of evolutionary relationships (homology, orthology, paralogy) and basic clustering of these orthologs. In this thesis, we adopted the concept of Hierarchical Orthology Groups (HOGs), introduced a decade ago, and proposed several improvements both to improve their inference and to use them in biological analyses such as the aforementioned applications. In addition, HOGs are a powerful framework to investigate ancestral genomes since HOGs convey information regarding gene family evolution (gene losses, gene duplications or gene gains). In this thesis, an ancestral genome at a given taxonomic level denotes the last common ancestor genome for the related taxon and its hypothetical ancestral gene composition and gene order (synteny). The ancestral genes composition and ancestral synteny for a given ancestral genome provides valuable information to study the genome evolution in terms of genomic rearrangement (duplication, translocation, deletion, inversion) or of gene family evolution (variation of the gene function, accelerate gene evolution, duplication rich clade). This thesis identifies three major open challenges that composed my three research arcs. First, inferring HOGs is complex and computationally demanding meaning that robust and scalable algorithms are mandatory to generate good quality HOGs in a reasonable time. Second, benchmarking orthology clustering without knowing the true evolutionary history is a difficult task, which requires appropriate benchmark strategies. And third, the lack of tools to handle HOGs limits their applications. In the first arc of the thesis, I proposed two new algorithm refinements to improve orthology inference in order to produce orthologs less sensitive to gene fragmentations and imbalances in the rate of evolution among paralogous copies. In addition, I introduced version 2.0 of the GETHOGs 2.0 algorithm, which infers HOGs in a bottom up fashion, and which has been shown to be both faster and more accurate. In the second arc, I proposed new strategies to benchmark the reconstruction of gene families using detailed cases studies based on evidence from multiple sequence alignments along with reconstructed gene trees, and to benchmark orthology using a simulation framework that provides full control of the evolutionary genomic setup. This work highlights the main challenges in current methods. Third, I created pyHam (python HOG analysis method), iHam (interactive HOG analysis method) and GTM (Graph - Tree - Multiple sequence alignment)—a collection of tools to process, manipulate and visualise HOGs. pyHam offers an easy way to handle and work with HOGs using simple python coding. Embedded at its heart are two visualisation tools to synthesise HOG-derived information: iHam that allow interactive browsing of HOG structure and a tree based visualisation called tree profile that pinpoints evolutionary events induced by the HOGs on a species tree. In addition, I develop GTM an interactive web based visualisation tool that combine for a given gene family (or set of genes) the related sequences, gene tree and orthology graph. In this thesis, I show that HOGs are a useful framework for phylogenetics, with considerable work done to produce robust and scalable inferences. Another important aspect is that our inferences are benchmarked using manual case studies and automated verification using simulation or reference Quest for Orthologs Benchmarks. Lastly, one of the major advances was the conception and implementation of tools to manipulate and visualise HOG. Such tools have already proven useful when investigating HOGs for developmental reasons or for downstream analysis. Ultimately, the HOG framework is amenable to integration of all aspects which can reasonably be expected to have evolved along the history of genes and ancestral genome reconstruction. -- La reconstruction de l'histoire évolutive ancestrale est la pierre angulaire de la majorité des analyses phylogénétiques. Nombreuses sont les applications possibles une fois que l'histoire évolutive est révélée, comme l'identification de gènes restreints taxonomiquement (barcoding de génome), la prédiction de fonction pour les gènes inconnus en se basant sur les ontologies des gènes relatifs evolutionnairement, l'identification de la perte ou de l'apparition de gènes au sein de familles de gènes ou encore pour dater au cours de l'évolution l'apparition de famille de gènes (phylostratigraphie). Généralement, la reconstruction de l'histoire évolutive se limite à l'inférence des relations évolutives (homologie, orthologie, paralogie) ainsi qu'à la construction de groupes d’orthologues simples. Dans cette thèse, nous adoptons le concept des groupes hiérarchiques d’orthologues (HOGs en anglais pour Hierarchical Orthology Groups), introduit il y a plus de 10 ans, et proposons plusieurs améliorations tant bien au niveau de leurs inférences que de leurs utilisations dans les analyses biologiques susmentionnées. Cette thèse a pour but d'identifier les trois problématiques majeures qui composent mes trois axes de recherches. Premièrement, l'inférence des HOGs est complexe et nécessite une puissance computationnelle importante ce qui rend obligatoire la création d'algorithmes robustes et efficients dans l'espace temps afin de maintenir une génération de résultats de qualité rigoureuse dans un temps raisonnable. Deuxièmement, le contrôle de la qualité du groupement des orthologues est une tâche difficile si on ne connaît l'histoire évolutive réelle ce qui nécessite la mise en place de stratégies de contrôle de qualité adaptées. Tertio, le manque d'outils pour manipuler les HOGs limite leur utilisation ainsi que leurs applications. Dans le premier axe de ma thèse, je propose deux nouvelles améliorations de l'algorithme pour l'inférence des orthologues afin de pallier à la sensibilité de l'inférence vis à vis de la fragmentation des gènes et de l'asymétrie du taux d'évolution au sein de paralogues. De plus, j'introduis la version 2.0 de l'algorithme GETHOGs qui utilise une nouvelle approche de type 'bottom-up' afin de produire des résultats plus rapides et plus précis. Dans le second axe, je propose de nouvelles stratégies pour contrôler la qualité de la reconstruction des familles de gènes en réalisant des études de cas manuels fondés sur des preuves apportées par des alignement multiples de séquences et des reconstructions d'arbres géniques, et aussi pour contrôler la qualité de l'orthologie en simulant l'évolution de génomes afin de pouvoir contrôler totalement le matériel génétique produit. Ce travail met en avant les principales problématiques des méthodes actuelles. Dans le dernier axe, je montre pyHam, iHam et GTM - une panoplie d'outils que j’ai créée afin de faciliter la manipulation et la visualisation des HOGs en utilisant un programmation simple en python. Deux outils de visualisation sont directement intégrés au sein de pyHam afin de pouvoir synthétiser l'information véhiculée par les HOGs: iHam permet d’interactivement naviguer dans les HOGs ainsi qu’une autre visualisation appelée “tree profile” utilisant un arbre d'espèces où sont localisés les événements révolutionnaires contenus dans les HOGs. En sus, j'ai développé GTM un outil interactif web qui combine pour une famille de gènes donnée (ou un ensemble de gènes) leurs séquences alignées, leur arbre de gène ainsi que le graphe d'orthologie en relation. Dans cette thèse, je montre que le concept des HOGs est utile à la phylogénétique et qu'un travail considérable a été réalisé dans le but d'améliorer leur inférences de façon robuste et rapide. Un autre point important est que la qualité de nos inférences soit contrôlée en réalisant des études de cas manuellement ou en utilisant le Quest for Orthologs Benchmark qui est une référence dans le contrôle de la qualité de l’orthologie. Dernièrement, une des avancée majeure proposée est la conception et l'implémentation d'outils pour visualiser et manipuler les HOGs. Ces outils s'avèrent déjà utilisés tant pour l'étude des HOGs dans un but d'amélioration de leur qualité que pour leur utilisation dans des analyses biologiques. Pour conclure, on peut noter que tous les aspects qui semblent avoir évolué en relation avec l'histoire évolutive des gènes ou des génomes ancestraux peuvent être intégrés au concept des HOGs

OMA 2011: orthology inference among 1000 complete genomes

OMA (Orthologous MAtrix) is a database that identifies orthologs among publicly available, complete genomes. Initiated in 2004, the project is at its 11th release. It now includes 1000 genomes, making it one of the largest resources of its kind. Here, we describe recent developments in terms of species covered; the algorithmic pipeline—in particular regarding the treatment of alternative splicing, and new features of the web (OMA Browser) and programming interface (SOAP API). In the second part, we review the various representations provided by OMA and their typical applications. The database is publicly accessible at http://omabrowser.or

OMA 2011: orthology inference among 1000 complete genomes

OMA (Orthologous MAtrix) is a database that identifies orthologs among publicly available, complete genomes. Initiated in 2004, the project is at its 11th release. It now includes 1000 genomes, making it one of the largest resources of its kind. Here, we describe recent developments in terms of species covered; the algorithmic pipeline—in particular regarding the treatment of alternative splicing, and new features of the web (OMA Browser) and programming interface (SOAP API). In the second part, we review the various representations provided by OMA and their typical applications. The database is publicly accessible at http://omabrowser.org

Identifying orthologs with OMA: A primer [version 1; peer review: 2 approved]

The Orthologous Matrix (OMA) is a method and database that allows users to identify orthologs among many genomes. OMA provides three different types of orthologs: pairwise orthologs, OMA Groups and Hierarchical Orthologous Groups (HOGs). This Primer is organized in two parts. In the first part, we provide all the necessary background information to understand the concepts of orthology, how we infer them and the different subtypes of orthology in OMA, as well as what types of analyses they should be used for. In the second part, we describe protocols for using the OMA browser to find a specific gene and its various types of orthologs. By the end of the Primer, readers should be able to (i) understand homology and the different types of orthologs reported in OMA, (ii) understand the best type of orthologs to use for a particular analysis; (iii) find particular genes of interest in the OMA browser; and (iv) identify orthologs for a given gene.  The data can be freely accessed from the OMA browser at https://omabrowser.org

The OMA orthology database in 2015: function predictions, better plant support, synteny view and other improvements

The Orthologous Matrix (OMA) project is a method and associated database inferring evolutionary relationships amongst currently 1706 complete proteomes (i.e. the protein sequence associated for every protein-coding gene in all genomes). In this update article, we present six major new developments in OMA: (i) a new web interface; (ii) Gene Ontology function predictions as part of the OMA pipeline; (iii) better support for plant genomes and in particular homeologs in the wheat genome; (iv) a new synteny viewer providing the genomic context of orthologs; (v) statically computed hierarchical orthologous groups subsets downloadable in OrthoXML format; and (vi) possibility to export parts of the all-against-all computations and to combine them with custom data for ‘client-side' orthology prediction. OMA can be accessed through the OMA Browser and various programmatic interfaces at http://omabrowser.or

The OMA orthology database in 2018: retrieving evolutionary relationships among all domains of life through richer web and programmatic interfaces.

The Orthologous Matrix (OMA) is a leading resource to relate genes across many species from all of life. In this update paper, we review the recent algorithmic improvements in the OMA pipeline, describe increases in species coverage (particularly in plants and early-branching eukaryotes) and introduce several new features in the OMA web browser. Notable improvements include: (i) a scalable, interactive viewer for hierarchical orthologous groups; (ii) protein domain annotations and domain-based links between orthologous groups; (iii) functionality to retrieve phylogenetic marker genes for a subset of species of interest; (iv) a new synteny dot plot viewer; and (v) an overhaul of the programmatic access (REST API and semantic web), which will facilitate incorporation of OMA analyses in computational pipelines and integration with other bioinformatic resources. OMA can be freely accessed at https://omabrowser.org