Predicting horizontal gene transfers with perfect transfer networks

Abstract Background Horizontal gene transfer inference approaches are usually based on gene sequences: parametric methods search for patterns that deviate from a particular genomic signature, while phylogenetic methods use sequences to reconstruct the gene and species trees. However, it is well-know...

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Main Authors: Alitzel López Sánchez, Manuel Lafond
Format: Article
Language:English
Published: BMC 2024-02-01
Series:Algorithms for Molecular Biology
Subjects:
Online Access:https://doi.org/10.1186/s13015-023-00242-2
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author Alitzel López Sánchez
Manuel Lafond
author_facet Alitzel López Sánchez
Manuel Lafond
author_sort Alitzel López Sánchez
collection DOAJ
description Abstract Background Horizontal gene transfer inference approaches are usually based on gene sequences: parametric methods search for patterns that deviate from a particular genomic signature, while phylogenetic methods use sequences to reconstruct the gene and species trees. However, it is well-known that sequences have difficulty identifying ancient transfers since mutations have enough time to erase all evidence of such events. In this work, we ask whether character-based methods can predict gene transfers. Their advantage over sequences is that homologous genes can have low DNA similarity, but still have retained enough important common motifs that allow them to have common character traits, for instance the same functional or expression profile. A phylogeny that has two separate clades that acquired the same character independently might indicate the presence of a transfer even in the absence of sequence similarity. Our contributions We introduce perfect transfer networks, which are phylogenetic networks that can explain the character diversity of a set of taxa under the assumption that characters have unique births, and that once a character is gained it is rarely lost. Examples of such traits include transposable elements, biochemical markers and emergence of organelles, just to name a few. We study the differences between our model and two similar models: perfect phylogenetic networks and ancestral recombination networks. Our goals are to initiate a study on the structural and algorithmic properties of perfect transfer networks. We then show that in polynomial time, one can decide whether a given network is a valid explanation for a set of taxa, and show how, for a given tree, one can add transfer edges to it so that it explains a set of taxa. We finally provide lower and upper bounds on the number of transfers required to explain a set of taxa, in the worst case.
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spelling doaj.art-7b57b69e6c1e45348bd704c8433f9cfc2024-03-05T17:41:30ZengBMCAlgorithms for Molecular Biology1748-71882024-02-0119111910.1186/s13015-023-00242-2Predicting horizontal gene transfers with perfect transfer networksAlitzel López Sánchez0Manuel Lafond1Department of Computer Science, Université de SherbrookeDepartment of Computer Science, Université de SherbrookeAbstract Background Horizontal gene transfer inference approaches are usually based on gene sequences: parametric methods search for patterns that deviate from a particular genomic signature, while phylogenetic methods use sequences to reconstruct the gene and species trees. However, it is well-known that sequences have difficulty identifying ancient transfers since mutations have enough time to erase all evidence of such events. In this work, we ask whether character-based methods can predict gene transfers. Their advantage over sequences is that homologous genes can have low DNA similarity, but still have retained enough important common motifs that allow them to have common character traits, for instance the same functional or expression profile. A phylogeny that has two separate clades that acquired the same character independently might indicate the presence of a transfer even in the absence of sequence similarity. Our contributions We introduce perfect transfer networks, which are phylogenetic networks that can explain the character diversity of a set of taxa under the assumption that characters have unique births, and that once a character is gained it is rarely lost. Examples of such traits include transposable elements, biochemical markers and emergence of organelles, just to name a few. We study the differences between our model and two similar models: perfect phylogenetic networks and ancestral recombination networks. Our goals are to initiate a study on the structural and algorithmic properties of perfect transfer networks. We then show that in polynomial time, one can decide whether a given network is a valid explanation for a set of taxa, and show how, for a given tree, one can add transfer edges to it so that it explains a set of taxa. We finally provide lower and upper bounds on the number of transfers required to explain a set of taxa, in the worst case.https://doi.org/10.1186/s13015-023-00242-2Horizontal gene transferTree-based networksPerfect phylogeniesCharacter-basedGene-expressionIndirect phylogenetic methods
spellingShingle Alitzel López Sánchez
Manuel Lafond
Predicting horizontal gene transfers with perfect transfer networks
Algorithms for Molecular Biology
Horizontal gene transfer
Tree-based networks
Perfect phylogenies
Character-based
Gene-expression
Indirect phylogenetic methods
title Predicting horizontal gene transfers with perfect transfer networks
title_full Predicting horizontal gene transfers with perfect transfer networks
title_fullStr Predicting horizontal gene transfers with perfect transfer networks
title_full_unstemmed Predicting horizontal gene transfers with perfect transfer networks
title_short Predicting horizontal gene transfers with perfect transfer networks
title_sort predicting horizontal gene transfers with perfect transfer networks
topic Horizontal gene transfer
Tree-based networks
Perfect phylogenies
Character-based
Gene-expression
Indirect phylogenetic methods
url https://doi.org/10.1186/s13015-023-00242-2
work_keys_str_mv AT alitzellopezsanchez predictinghorizontalgenetransferswithperfecttransfernetworks
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