Inferring Horizontal Gene Transfer
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Published version
Author(s)
Ravenhall, M
Skunca, N
Lassalle, F
Dessimoz, C
Type
Journal Article
Abstract
Horizontal or Lateral Gene Transfer (HGT or LGT) is the transmission of portions of genomic
DNA between organisms through a process decoupled from vertical inheritance. In the presence
of HGT events, different fragments of the genome are the result of different evolutionary
histories. This can therefore complicate the investigations of evolutionary relatedness of lineages
and species. Also, as HGT can bring into genomes radically different genotypes from
distant lineages, or even new genes bearing new functions, it is a major source of phenotypic
innovation and a mechanism of niche adaptation. For example, of particular relevance to
human health is the lateral transfer of antibiotic resistance and pathogenicity determinants,
leading to the emergence of pathogenic lineages [1]. Computational identification of HGT
events relies upon the investigation of sequence composition or evolutionary history of
genes. Sequence composition-based ("parametric") methods search for deviations from the
genomic average, whereas evolutionary history-based ("phylogenetic") approaches identify
genes whose evolutionary history significantly differs from that of the host species. The evaluation
and benchmarking of HGT inference methods typically rely upon simulated genomes,
for which the true history is known. On real data, different methods tend to infer different HGT
events, and as a result it can be difficult to ascertain all but simple and clear-cut HGT events
DNA between organisms through a process decoupled from vertical inheritance. In the presence
of HGT events, different fragments of the genome are the result of different evolutionary
histories. This can therefore complicate the investigations of evolutionary relatedness of lineages
and species. Also, as HGT can bring into genomes radically different genotypes from
distant lineages, or even new genes bearing new functions, it is a major source of phenotypic
innovation and a mechanism of niche adaptation. For example, of particular relevance to
human health is the lateral transfer of antibiotic resistance and pathogenicity determinants,
leading to the emergence of pathogenic lineages [1]. Computational identification of HGT
events relies upon the investigation of sequence composition or evolutionary history of
genes. Sequence composition-based ("parametric") methods search for deviations from the
genomic average, whereas evolutionary history-based ("phylogenetic") approaches identify
genes whose evolutionary history significantly differs from that of the host species. The evaluation
and benchmarking of HGT inference methods typically rely upon simulated genomes,
for which the true history is known. On real data, different methods tend to infer different HGT
events, and as a result it can be difficult to ascertain all but simple and clear-cut HGT events
Date Issued
2015-05-28
Date Acceptance
2015-05-01
Citation
PLoS Computational Biology, 2015, 11 (5)
ISSN
1553-734X
Publisher
Public Library of Science
Journal / Book Title
PLoS Computational Biology
Volume
11
Issue
5
Copyright Statement
© 2015 Ravenhall et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000356700200002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
ESCHERICHIA-COLI
TRANSFER EVENTS
BDELLOVIBRIO-BACTERIOVORUS
PATHOGENICITY ISLANDS
THERMOTOGA-MARITIMA
GENOMIC SIGNATURE
SURROGATE METHODS
DNA-SEQUENCE
EVOLUTION
INFERENCE
Base Composition
Computational Biology
Computer Simulation
DNA, Bacterial
Databases, Genetic
Drug Resistance, Bacterial
Evolution, Molecular
Gene Transfer, Horizontal
Genomics
Humans
Models, Genetic
Models, Statistical
Phylogeny
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Bioinformatics
Publication Status
Published
Article Number
ARTN e1004095