A Probabilistic Model for Gene Content Evolution with Duplication, Loss, and Horizontal Transfer

Csűrös, Miklós; Miklós, István

doi:10.1007/11732990_18

Cited by 40 publications

(29 citation statements)

References 51 publications

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“…By adopting techniques used in Arvestad et al [2004], this probability can be computed without such a limitation and with significantly increased computational efficiency for gene families of commonly occurring sizes. In fact, efficient algorithms for copy number patterns for the gene evolution model, augmented with an extra event corresponding to a gene having been laterally transferred into a species tree vertex, was presented in Csürös and Miklós [2006]. Our gene evolution model has several important applications.…”

Section: Discussionmentioning

confidence: 98%

The gene evolution model and computing its associated probabilities

Arvestad

Lagergren

Sennblad

2009

J. ACM

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Phylogeny is both a fundamental tool in biology and a rich source of fascinating modeling and algorithmic problems. Today's wealth of sequenced genomes makes it increasingly important to understand evolutionary events such as duplications, losses, transpositions, inversions, lateral transfers, and domain shuffling. We focus on the gene duplication event, that constitutes a major force in the creation of genes with new function [Ohno 1970;Lynch and Force 2000] and, thereby also, of biodiversity.We introduce the probabilistic gene evolution model, which describes how a gene tree evolves within a given species tree with respect to speciation, gene duplication, and gene loss. The actual relation between gene tree and species tree is captured by a reconciliation, a concept which we generalize for more expressiveness. The model is a canonical generalization of the classical linear birth-death process, obtained by replacing the interval where the process takes place by a tree.For the gene evolution model, we derive efficient algorithms for some associated probability distributions: the probability of a reconciled tree, the probability of a gene tree, the maximum probability reconciliation, the posterior probability of a reconciliation, and sampling reconciliations with respect to the posterior probability. These algorithms provides the basis for several applications, including species tree construction, reconciliation analysis, orthology analysis, biogeography, and host-parasite co-evolution. Arvestad, L. and Lagergren, J., and Sennblad, B. 2009. The gene evolution model and computing its associated probabilities. ACM Reference Format:

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Section: Discussionmentioning

confidence: 98%

The gene evolution model and computing its associated probabilities

Arvestad

Lagergren

Sennblad

2009

J. ACM

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“…The effective duplication/loss ratio (d/l e ) and transfer/loss ratio (h/l e ) for each ATGC-COG were estimated with the software COUNT (24), which optimizes the parameters of a duplication-transfer-loss model analogous to the model described above under the assumption of neutrality (81). The output of the program was postprocessed to obtain ATGC-COG-specific rates as described in ref.…”

Section: Methodsmentioning

confidence: 99%

Disentangling the effects of selection and loss bias on gene dynamics

Iranzo

Cuesta

Manrubia

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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We combine mathematical modeling of genome evolution with comparative analysis of prokaryotic genomes to estimate the relative contributions of selection and intrinsic loss bias to the evolution of different functional classes of genes and mobile genetic elements (MGE). An exact solution for the dynamics of gene family size was obtained under a linear duplication-transfer-loss model with selection. With the exception of genes involved in information processing, particularly translation, which are maintained by strong selection, the average selection coefficient for most nonparasitic genes is low albeit positive, compatible with observed positive correlation between genome size and effective population size. Free-living microbes evolve under stronger selection for gene retention than parasites. Different classes of MGE show a broad range of fitness effects, from the nearly neutral transposons to prophages, which are actively eliminated by selection. Genes involved in antiparasite defense, on average, incur a fitness cost to the host that is at least as high as the cost of plasmids. This cost is probably due to the adverse effects of autoimmunity and curtailment of horizontal gene transfer caused by the defense systems and selfish behavior of some of these systems, such as toxin-antitoxin and restriction modification modules. Transposons follow a biphasic dynamics, with bursts of gene proliferation followed by decay in the copy number that is quantitatively captured by the model. The horizontal gene transfer to loss ratio, but not duplication to loss ratio, correlates with genome size, potentially explaining increased abundance of neutral and costly elements in larger genomes.

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“…Csűrös et al [30] gave a probabilistic model of gene evolution that considered LGTs, duplications, and losses, but applied it only to gene family sizes. A rather restricted parsimony method was given in [31] where the input is a gene tree and a species tree augmented with additional edges showing where transfers have taken place.…”

Section: Few Attempts Have Been Made At Devising Methods To Explicitlmentioning

confidence: 99%

Simultaneous Identification of Duplications and Lateral Gene Transfers

Tofigh

Hallett

Lagergren

2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

161

265

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The incongruency between a gene tree and a corresponding species tree can be attributed to evolutionary events such as gene duplication and gene loss. This paper describes a combinatorial model where a so-called DTL-scenario is used to explain the differences between a gene tree and a corresponding species tree taking into account gene duplications, gene losses, and lateral gene transfers (also known as horizontal gene transfers). The reasonable biological constraint that a lateral gene transfer may only occur between contemporary species leads to the notion of acyclic DTLscenarios. Parsimony methods are introduced by defining appropriate optimization problems. We show that finding most parsimonious acyclic DTL-scenarios is NP-complete. However, by dropping the condition of acyclicity, the problem becomes tractable, and we provide a dynamic programming algorithm as well as a fixed-parameter-tractable algorithm for finding most parsimonious DTLscenarios.

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A Probabilistic Model for Gene Content Evolution with Duplication, Loss, and Horizontal Transfer

Cited by 40 publications

References 51 publications

The gene evolution model and computing its associated probabilities

The gene evolution model and computing its associated probabilities

Disentangling the effects of selection and loss bias on gene dynamics

Simultaneous Identification of Duplications and Lateral Gene Transfers

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