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Geptop:一种基于直系同源性和系统发育的已测序细菌基因组基因必需性预测工具。

Geptop: a gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny.

作者信息

Wei Wen, Ning Lu-Wen, Ye Yuan-Nong, Guo Feng-Biao

机构信息

Center of Bioinformatics and Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.

出版信息

PLoS One. 2013 Aug 15;8(8):e72343. doi: 10.1371/journal.pone.0072343. eCollection 2013.

Abstract

Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the Porphyromonas gingivalis, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at http://cefg.uestc.edu.cn/geptop/ free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.

摘要

在预测细菌必需基因方面得分很高的整合基因组学预测器,在大多数物种中是不可行的,因为数据源有限。我们基于直系同源性和系统发育开发了一种通用方法和名为Geptop的工具,以提供基因必需性注释。在一系列测试中,我们的Geptop方法在接收器操作曲线中产生的曲线下面积(AUC)得分高于整合方法。在随机打乱样本的十折交叉验证中,Geptop的AUC为0.918,在对19种生物体的跨生物体预测中,Geptop的AUC得分在0.569至0.959之间。对来自牙龈卟啉单胞菌(属于与上述19个细菌基因组不同门的物种)最近确定的必需基因数据集进行的测试,AUC为0.77。因此,Geptop可应用于任何基因组已测序的细菌物种。与通过致死筛选唯一确定的必需基因相比,仅由Gepop预测的必需基因与更多的蛋白质-蛋白质相互作用相关,特别是在AUC得分较低(<0.7)的三种细菌中。这在某种意义上可能进一步说明了我们方法的可靠性和可行性。Geptop的网络服务器和独立版本可在http://cefg.uestc.edu.cn/geptop/免费获取。该工具已在968个细菌基因组上运行,结果可在该网站上获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b7a/3744497/e37ea69a1b9a/pone.0072343.g001.jpg

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