A novel alignment-free method for detection of lateral genetic transfer based on TF-IDF
Identifieur interne : 001402 ( Main/Exploration ); précédent : 001401; suivant : 001403A novel alignment-free method for detection of lateral genetic transfer based on TF-IDF
Auteurs : Yingnan Cong [Australie] ; Yao-Ban Chan [Australie] ; Mark A. Ragan [Australie]Source :
- Scientific Reports [ 2045-2322 ] ; 2016.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- genetics : Gene Transfer, Horizontal.
- methods : Computational Biology, Sequence Analysis, DNA, Sequence Analysis, Protein.
- Evolution, Molecular, Genome, Phylogeny.
Abstract
Lateral genetic transfer (LGT) plays an important role in the evolution of microbes. Existing computational methods for detecting genomic regions of putative lateral origin scale poorly to large data. Here, we propose a novel method based on TF-IDF (Term Frequency-Inverse Document Frequency) statistics to detect not only regions of lateral origin, but also their origin and direction of transfer, in sets of hierarchically structured nucleotide or protein sequences. This approach is based on the frequency distributions of
Url:
DOI: 10.1038/srep30308
PubMed: 27453035
PubMed Central: 4958984
Affiliations:
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Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A novel alignment-free method for detection of lateral genetic transfer based on TF-IDF</title>
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<author><name sortKey="Chan, Yao Ban" sort="Chan, Yao Ban" uniqKey="Chan Y" first="Yao-Ban" last="Chan">Yao-Ban Chan</name>
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<term>Gene Transfer, Horizontal (genetics)</term>
<term>Genome</term>
<term>Phylogeny</term>
<term>Sequence Analysis, DNA (methods)</term>
<term>Sequence Analysis, Protein (methods)</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de séquence d'ADN ()</term>
<term>Analyse de séquence de protéine ()</term>
<term>Biologie informatique ()</term>
<term>Génome</term>
<term>Phylogénie</term>
<term>Transfert horizontal de gène (génétique)</term>
<term>Évolution moléculaire</term>
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<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Gene Transfer, Horizontal</term>
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<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Transfert horizontal de gène</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term>
<term>Sequence Analysis, DNA</term>
<term>Sequence Analysis, Protein</term>
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<keywords scheme="MESH" xml:lang="en"><term>Evolution, Molecular</term>
<term>Genome</term>
<term>Phylogeny</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Analyse de séquence d'ADN</term>
<term>Analyse de séquence de protéine</term>
<term>Biologie informatique</term>
<term>Génome</term>
<term>Phylogénie</term>
<term>Évolution moléculaire</term>
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<front><div type="abstract" xml:lang="en"><p>Lateral genetic transfer (LGT) plays an important role in the evolution of microbes. Existing computational methods for detecting genomic regions of putative lateral origin scale poorly to large data. Here, we propose a novel method based on TF-IDF (Term Frequency-Inverse Document Frequency) statistics to detect not only regions of lateral origin, but also their origin and direction of transfer, in sets of hierarchically structured nucleotide or protein sequences. This approach is based on the frequency distributions of <italic>k</italic>
-mers in the sequences. If a set of contiguous <italic>k</italic>
-mers appears sufficiently more frequently in another phyletic group than in its own, we infer that they have been transferred from the first group to the second. We performed rigorous tests of TF-IDF using simulated and empirical datasets. With the simulated data, we tested our method under different parameter settings for sequence length, substitution rate between and within groups and post-LGT, deletion rate, length of transferred region and <italic>k</italic>
size, and found that we can detect LGT events with high precision and recall. Our method performs better than an established method, ALFY, which has high recall but low precision. Our method is efficient, with runtime increasing approximately linearly with sequence length.</p>
</div>
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<author><name sortKey="Elsner, J B" uniqKey="Elsner J">J. B. Elsner</name>
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<author><name sortKey="Tsonis, P A" uniqKey="Tsonis P">P. A. Tsonis</name>
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<author><name sortKey="Gonnet, G H" uniqKey="Gonnet G">G. H. Gonnet</name>
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<affiliations><list><country><li>Australie</li>
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<name sortKey="Chan, Yao Ban" sort="Chan, Yao Ban" uniqKey="Chan Y" first="Yao-Ban" last="Chan">Yao-Ban Chan</name>
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