Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX 76203, United States.
Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX 76203, United States; Department of Mathematics, University of North Texas, Denton, TX 76203, United States.
J Biotechnol. 2024 Jun 10;388:49-58. doi: 10.1016/j.jbiotec.2024.04.011. Epub 2024 Apr 17.
Mobilization of clusters of genes called genomic islands (GIs) across bacterial lineages facilitates dissemination of traits, such as, resistance against antibiotics, virulence or hypervirulence, and versatile metabolic capabilities. Robust delineation of GIs is critical to understanding bacterial evolution that has a vast impact on different life forms. Methods for identification of GIs exploit different evolutionary features or signals encoded within the genomes of bacteria, however, the current state-of-the-art in GI detection still leaves much to be desired. Here, we have taken a combinatorial approach that accounted for GI specific features such as compositional bias, aberrant phyletic pattern, and marker gene enrichment within an integrative framework to delineate GIs in bacterial genomes. Our GI prediction tool, DICEP, was assessed on simulated genomes and well-characterized bacterial genomes. DICEP compared favorably with current GI detection tools on real and synthetic datasets.
基因簇的移动称为基因组岛 (GI),跨越细菌谱系,促进了特性的传播,如对抗生素的抗性、毒力或超毒力以及多功能代谢能力。GI 的稳健描绘对于理解对不同生命形式有巨大影响的细菌进化至关重要。GI 识别方法利用细菌基因组中编码的不同进化特征或信号,然而,当前的 GI 检测技术仍有很大的改进空间。在这里,我们采用了一种组合方法,该方法考虑了 GI 特定的特征,例如组成性偏差、异常的系统发育模式和标记基因在综合框架内的富集,以描绘细菌基因组中的 GI。我们的 GI 预测工具 DICEP 在模拟基因组和特征良好的细菌基因组上进行了评估。DICEP 在真实和合成数据集上与当前的 GI 检测工具相比表现出色。