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Prediction of Horizontally and Widely Transferred Genes in Prokaryotes

作者
Yoji Nakamura
出处
期刊:Evolutionary Bioinformatics [SAGE Publishing]
卷期号:14: 1176934318810785-1176934318810785 被引量:9
标识
DOI:10.1177/1176934318810785
摘要

Horizontal gene transfer (HGT) is the process whereby an organism acquires exogenous genes (horizontally transferred genes or HT genes) that are not inherited from the parent, but are derived from another organism. In prokaryotes, HGT has been considered as one of the important driving forces of evolution. Previously, genome-wide analyses have been conducted for estimating the proportion of HT genes in prokaryotic genomes, but the number of species examined at the time was limited, and gene annotation was relatively poor. Currently, tens of thousands of prokaryotic genomes have been published and gene annotation resources have improved. In the present study, HT gene prediction method was modified so that the estimate was robust to gene length, conducting a comprehensive search using 3017 representative prokaryotic genomes belonging to 1348 species. The result showed that an average of 13% (ranging from 0% to 30% across species) of protein-coding genes was predicted as being of horizontal origin. The proportion of the predicted HT genes per species was associated with the species' habitat, while a positive correlation between the proportion and genomic nucleotide frequency was also observed. Moreover, the functions of the predicted HT genes were inferred and compared according to two popular databases, the Clusters of Orthologous Groups and the Kyoto Encyclopedia of Genes and Genomes. As a result, both databases indicated that many of the widely transferred genes were involved in mobile genetic elements (transposons, phages, and plasmids) as expected. Notably, the present study predicted that six as-yet-uncharacterized genes were widely distributed HT genes, and therefore, will be interesting targets for evolutionary studies. Thus, this study demonstrates that a data-driven approach using massive sequence data may contribute to a broader understanding of HGT in prokaryotes.

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