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Detecting Horizontal Gene Transfer between Closely Related Taxa

Horizontal gene transfer (HGT), the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically de...

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Autores principales: Adato, Orit, Ninyo, Noga, Gophna, Uri, Snir, Sagi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595140/
https://www.ncbi.nlm.nih.gov/pubmed/26439115
http://dx.doi.org/10.1371/journal.pcbi.1004408
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author Adato, Orit
Ninyo, Noga
Gophna, Uri
Snir, Sagi
author_facet Adato, Orit
Ninyo, Noga
Gophna, Uri
Snir, Sagi
author_sort Adato, Orit
collection PubMed
description Horizontal gene transfer (HGT), the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically derived) genes in its recipient genome. Detecting HGT between closely related species or strains is challenging, as the phylogenetic signal is usually weak and the nucleotide composition is normally nearly identical. Nevertheless, there is a great importance in detecting HGT between congeneric species or strains, especially in clinical microbiology, where understanding the emergence of new virulent and drug-resistant strains is crucial, and often time-sensitive. We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on “unusual” sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set. When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.
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spelling pubmed-45951402015-10-09 Detecting Horizontal Gene Transfer between Closely Related Taxa Adato, Orit Ninyo, Noga Gophna, Uri Snir, Sagi PLoS Comput Biol Research Article Horizontal gene transfer (HGT), the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically derived) genes in its recipient genome. Detecting HGT between closely related species or strains is challenging, as the phylogenetic signal is usually weak and the nucleotide composition is normally nearly identical. Nevertheless, there is a great importance in detecting HGT between congeneric species or strains, especially in clinical microbiology, where understanding the emergence of new virulent and drug-resistant strains is crucial, and often time-sensitive. We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on “unusual” sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set. When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain. Public Library of Science 2015-10-06 /pmc/articles/PMC4595140/ /pubmed/26439115 http://dx.doi.org/10.1371/journal.pcbi.1004408 Text en © 2015 Adato et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Adato, Orit
Ninyo, Noga
Gophna, Uri
Snir, Sagi
Detecting Horizontal Gene Transfer between Closely Related Taxa
title Detecting Horizontal Gene Transfer between Closely Related Taxa
title_full Detecting Horizontal Gene Transfer between Closely Related Taxa
title_fullStr Detecting Horizontal Gene Transfer between Closely Related Taxa
title_full_unstemmed Detecting Horizontal Gene Transfer between Closely Related Taxa
title_short Detecting Horizontal Gene Transfer between Closely Related Taxa
title_sort detecting horizontal gene transfer between closely related taxa
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595140/
https://www.ncbi.nlm.nih.gov/pubmed/26439115
http://dx.doi.org/10.1371/journal.pcbi.1004408
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