The DOI matches its Crossref record; 104 of the 104 references it cites were found in a registry or an index.
Inferring Horizontal Gene Transfer
Open accessAbstract
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. 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.
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Cites
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Cited by
Widen, S. A., Bes, I. C., Koreshova, A., Pliota, P., Krogull, D., & Burga, A. (2023) · Science · doi:10.1126/science.ade0705 · Cited by 78
Phosphonate production by marine microbes: exploring new sources and potential function
Acker, M., Hogle, S. L., Berube, P. M., Hackl, T., Coe, A., Stepanauskas, R., Chisholm, S. W., & Repeta, D. J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113386119 · Cited by 76
Dmitrijeva, M., Tackmann, J., Matias Rodrigues, J. F., Huerta-Cepas, J., Coelho, L. P., & von Mering, C. (2024) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02357-0 · Cited by 72
Integrated Metabolomic–Genomic Workflows Accelerate Microbial Natural Product Discovery
Avalon, N. E., Murray, A. E., & Baker, B. J. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.2c02245 · Cited by 47
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Metadata
- Authors
- Ravenhall, M., Škunca, N., Lassalle, F., & Dessimoz, C.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-05-28
- Volume
- 11
- Issue
- 5
- Pages
- e1004095
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W1599377382
- Cited by
- 296
- References
- 104
- FWCI
- 9.26
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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