Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands.
Identifieur interne : 002D14 ( Main/Exploration ); précédent : 002D13; suivant : 002D15Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands.
Auteurs : Georgios S. Vernikos [Royaume-Uni] ; Julian ParkhillSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2006.
Descripteurs français
- KwdFr :
- MESH :
- génétique : ADN bactérien, Facteurs de virulence, Génome bactérien, Ilots génomiques, Salmonella, Transfert horizontal de gène, Virulence.
- pathogénicité : Salmonella.
- Algorithmes, Évolution biologique.
English descriptors
- KwdEn :
- MESH :
- chemical , genetics : DNA, Bacterial, Virulence Factors.
- genetics : Gene Transfer, Horizontal, Genome, Bacterial, Genomic Islands, Salmonella, Virulence.
- pathogenicity : Salmonella.
- Algorithms, Biological Evolution.
Abstract
There is a growing literature on the detection of Horizontal Gene Transfer (HGT) events by means of parametric, non-comparative methods. Such approaches rely only on sequence information and utilize different low and high order indices to capture compositional deviation from the genome backbone; the superiority of the latter over the former has been shown elsewhere. However even high order k-mers may be poor estimators of HGT, when insufficient information is available, e.g. in short sliding windows. Most of the current HGT prediction methods require pre-existing annotation, which may restrict their application on newly sequenced genomes.
DOI: 10.1093/bioinformatics/btl369
PubMed: 16837528
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">There is a growing literature on the detection of Horizontal Gene Transfer (HGT) events by means of parametric, non-comparative methods. Such approaches rely only on sequence information and utilize different low and high order indices to capture compositional deviation from the genome backbone; the superiority of the latter over the former has been shown elsewhere. However even high order k-mers may be poor estimators of HGT, when insufficient information is available, e.g. in short sliding windows. Most of the current HGT prediction methods require pre-existing annotation, which may restrict their application on newly sequenced genomes.</div>
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