PPNet: Identifying Functional Association Networks by Phylogenetic Profiling of Prokaryotic Genomes.
PPNet: Identifying Functional Association Networks by Phylogenetic Profiling of Prokaryotic Genomes.
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DOI：
10.1128/spectrum.03871-22
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发表时间：
2023-02-14
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
确定微生物功能关联网络有助于解释生物现象，更好地了解致病性的分子基础，也有助于制定控制措施。在这里，我们描述PPNet，一个工具，使用基因组信息和分析系统发育概况与二进制的相似性和距离的措施，以获得大规模的细菌基因关联网络的一个单一的物种。作为一个范例，我们已经推导出一个功能关联网络在猪病原体猪链球菌使用81二进制相似性和相异性的措施，表现出优异的性能的基础上的区域下的受试者工作特性（AUROC），区域下的精度召回（AUPR），和派生的整体评分方法。通过细菌双杂交实验对选定的网络关联进行了实验验证。我们的结论是，PPNet，一个公开可用的（https：//github.com/liyangjie/PPNet），可以用来构建微生物关联网络从容易获得的基因组规模的数据。这项研究开发了PPNet，这是第一个可用于推断单个物种的大规模细菌功能关联网络的工具。PPNet包括使用平均核苷酸同一性和平均核苷酸覆盖度来分配细菌菌株的独特性的方法。PPNet收集了81个二元相似性和距离度量用于系统发育分析，然后对其进行评估并将其分为四组。PPNet可以有效地从公开的原核生物基因组中捕获与表型功能相关的基因网络，为下游分析和实验检测提供有价值的结果。
Identification of microbial functional association networks allows interpretation of biological phenomena and a greater understanding of the molecular basis of pathogenicity and also underpins the formulation of control measures. Here, we describe PPNet, a tool that uses genome information and analysis of phylogenetic profiles with binary similarity and distance measures to derive large-scale bacterial gene association networks of a single species. As an exemplar, we have derived a functional association network in the pig pathogen Streptococcus suis using 81 binary similarity and dissimilarity measures which demonstrates excellent performance based on the area under the receiver operating characteristic (AUROC), the area under the precision-recall (AUPR), and a derived overall scoring method. Selected network associations were validated experimentally by using bacterial two-hybrid experiments. We conclude that PPNet, a publicly available (https://github.com/liyangjie/PPNet), can be used to construct microbial association networks from easily acquired genome-scale data. IMPORTANCE This study developed PPNet, the first tool that can be used to infer large-scale bacterial functional association networks of a single species. PPNet includes a method for assigning the uniqueness of a bacterial strain using the average nucleotide identity and the average nucleotide coverage. PPNet collected 81 binary similarity and distance measures for phylogenetic profiling and then evaluated and divided them into four groups. PPNet can effectively capture gene networks that are functionally related to phenotype from publicly prokaryotic genomes, as well as provide valuable results for downstream analysis and experiment testing.
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影响因子：
10.7
作者：
Huerta-Cepas J;Forslund K;Coelho LP;Szklarczyk D;Jensen LJ;von Mering C;Bork P
通讯作者：
Bork P
影响因子：
3.3
作者：
Gottschalk, M;Segura, M
通讯作者：
Segura, M
影响因子：
12.3
作者：
Brynildsrud O;Bohlin J;Scheffer L;Eldholm V
通讯作者：
Eldholm V
影响因子：
12.3
作者：
Bowers PM;Pellegrini M;Thompson MJ;Fierro J;Yeates TO;Eisenberg D
通讯作者：
Eisenberg D
影响因子：
5.8
作者：
Jombart, Thibaut;Ahmed, Ismail
通讯作者：
Ahmed, Ismail