Éditeur(s) : HAL CCSD Oxford University Press (OUP)
Résumé : International audience
Motivation: Gene fusion is an important evolutionary process. It can yield valuable information to infer the interactions and functions of proteins. Fused genes have been identified as non-transitive patterns of similarity in triplets of genes. To be computationally tractable, this approach usually imposes an a priori distinction between a dataset in which fused genes are searched for, and a dataset that may have provided genetic material for fusion. This reduces the `genetic space' in which fusion can be discovered, as only a subset of triplets of genes is investigated. Moreover, this approach may have a high-false-positive rate, and it does not identify gene families descending from a common fusion event. Results: We represent similarities between sequences as a network. This leads to an efficient formulation of previous methods of fused gene identification, which we implemented in the Python program FusedTriplets. Furthermore, we propose a new characterization of families of fused genes, as clique minimal separators of the sequence similarity network. This well-studied graph topology provides a robust and fast method of detection, well suited for automatic analyses of big datasets. We implemented this method in the C++ program MosaicFinder, which additionally uses local alignments to discard false-positive candidates and indicates potential fusion points. The grouping into families will help distinguish sequencing or prediction errors from real biological fusions, and it will yield additional insight into the function and history of fused genes.
ISSN: 1367-4803

hal-01544795
https://hal.archives-ouvertes.fr/hal-01544795
DOI : 10.1093/bioinformatics/btt049

Éditeur(s) : HAL CCSD Society for Molecular Biology and Evolution
Résumé : International audience
Complex nongradual evolutionary processes such as gene remodeling are difficult to model, to visualize, and to investigate systematically. Despite these challenges, the creation of composite (or mosaic) genes by combination of genetic segments from unrelated gene families was established as an important adaptive phenomena in eukaryotic genomes. In contrast, almost no general studies have been conducted to quantify composite genes in viruses. Although viral genome mosaicism has been well-described, the extent of gene mosaicism and its rules of emergence remain largely unexplored. Applying methods from graph theory to inclusive similarity networks, and using data from more than 3,000 complete viral genomes, we provide the first demonstration that composite genes in viruses are 1) functionally biased, 2) involved in key aspects of the arm race between cells and viruses, and 3) can be classified into two distinct types of composite genes in all viral classes. Beyond the quantification of the widespread recombination of genes among different viruses of the same class, we also report a striking sharing of genetic information between viruses of different classes and with different nucleic acid types. This latter discovery provides novel evidence for the existence of a large and complex mobilome network, which appears partly bound by the sharing of genetic information and by the formation of composite genes between mobile entities with different genetic material. Considering that there are around 10E31 viruses on the planet, gene remodeling appears as a hugely significant way of generating and moving novel sequences between different kinds of organisms on Earth.
ISSN: 1759-6653

Droits : http://creativecommons.org/licenses/by/
hal-01324848
http://hal.upmc.fr/hal-01324848
http://hal.upmc.fr/hal-01324848/document
http://hal.upmc.fr/hal-01324848/file/Genome%20Biol%20Evol-2014-Jachiet-2195-205.pdf
DOI : 10.1093/gbe/evu168

Éditeur(s) : HAL CCSD
Résumé : The recent accumulation of genomic sequence data has shown that gene evolution is not strictly tree-Like. Many evolutionary processes, like exon shuffling, gene fusion or nonhomologous recombination remodel genes by creating composite structures that are made from parts with different evolutionary histories. The development of sequence similarity networks provides an analytical framework to study the impact of these processes on molecular evolution, by structuring the resemblance relationships between sequences and by formalizing, in terms of graph theory, the detection of composite genes (intransitive triplets) and gene families (clique minimal separators). The size of current data sets, typically several million sequences, has also required the development of new tools and methods: sequence comparison parallelization, large networks visualization with Louvain communities and large cycles identification. When applied to eukaryotic and viral genome data sets, these methods have shown that composite genes are found throughout cellular organisms and mobile genetic elements. Proportionally, composite genes are more numerous in eukaryotic genomes; in absolute number, they are more numerous in viruses. In the latter, composite genes functional distribution is biased (enrichment of genes families that are essential for the perpetuation of the viral cycle), and the various parts of composite genes sometimes even originate from the genetic material of different viral classes. More generally, the extent of combinatorial processes, by unravelling other evolutionary bonds than homology bonds in the strictest sense, legitimates a pluralistic study of similarity relationships between sequences.
L’accumulation récente de données de séquences génomiques a montré que l’évolution des gènes n’est pas strictement arborescente. De nombreux processus évolutifs, comme l’exon shuffling, la fusion de gènes ou la recombinaison illégitime remodèlent les gènes, créant des structures composites, formées de parties dont les histoires évolutives sont différentes. Le développement de réseaux de similarité de séquences fournit un cadre analytique permettant d’étudier l’impact de ces processus sur l’évolution moléculaire, en structurant les relations de ressemblance entre séquences et en formalisant en termes de graphes la détection de gènes (triplets intransitifs) et de familles de gènes (cliques minimales séparatrices) composites. La taille des jeux de données actuels, de l’ordre de plusieurs millions de séquences, a également requis le développement de nouveaux outils et méthodes : parallélisation des comparaisons de séquences, visualisation de très grands réseaux par simplification en communautés de Louvain et identification de grands cycles. Appliquées à des jeux de données de génomes eucaryotes et viraux, ces méthodes ont démontré la présence de gènes composites dans tout le vivant et les éléments génétiques mobiles. En proportion, les gènes composites sont plus nombreux dans les génomes eucaryotes ; en nombre absolu, ils sont plus nombreux à être portés par des virus. Chez ces derniers, la distribution fonctionnelle des gènes composites est biaisée (enrichissement dans les familles essentielles pour la perpétuation du cycle viral), et les éléments des gènes composites trouvent même parfois leurs origines dans le matériel génétique de classes virales différentes. Plus généralement, l’étendue des processus combinatoires, en révélant des liens évolutionnaires autres que les liens d’homologie au sens fort, justifie une étude pluraliste des relations de similarité entre séquences.
https://tel.archives-ouvertes.fr/tel-01127379

NNT : 2014PA066358
tel-01127379
https://tel.archives-ouvertes.fr/tel-01127379
https://tel.archives-ouvertes.fr/tel-01127379/document
https://tel.archives-ouvertes.fr/tel-01127379/file/2014PA066358.pdf

Éditeur(s) : HAL CCSD Oxford University Press (OUP)
Résumé : International audience
Defining homologous genes is important in many evolutionary studies but raises obvious issues. Some of these issues are conceptual and stem from our assumptions of how a gene evolves, others are practical, and depend on the algorithmic decisions implemented in existing software. Therefore, to make progress in the study of homology, both ontological and epistemological questions must be considered. In particular, defining homologous genes cannot be solely addressed under the classic assumptions of strong tree thinking, according to which genes evolve in a strictly tree-like fashion of vertical descent and divergence and the problems of homology detection are primarily methodological. Gene homology could also be considered under a different perspective where genes evolve as ``public goods,'' subjected to various introgressive processes. In this latter case, defining homologous genes becomes a matter of designing models suited to the actual complexity of the data and how such complexity arises, rather than trying to fit genetic data to some a priori tree-like evolutionary model, a practice that inevitably results in the loss of much information. Here we show how important aspects of the problems raised by homology detection methods can be overcome when even more fundamental roots of these problems are addressed by analyzing public goods thinking evolutionary processes through which genes have frequently originated. This kind of thinking acknowledges distinct types of homologs, characterized by distinct patterns, in phylogenetic and nonphylogenetic unrooted or multirooted networks. In addition, we define `` family resemblances'' to include genes that are related through intermediate relatives, thereby placing notions of homology in the broader context of evolutionary relationships. We conclude by presenting some payoffs of adopting such a pluralistic account of homology and family relationship, which expands the scope of evolutionary analyses beyond the traditional, yet relatively narrow focus allowed by a strong tree-thinking view on gene evolution.
ISSN: 0737-4038

hal-01544794
https://hal.archives-ouvertes.fr/hal-01544794
DOI : 10.1093/molbev/mst228