Finding functional associations between prokaryotic virus orthologous groups: a proof of concept.
Finding functional associations between prokaryotic virus orthologous groups: a proof of concept.
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DOI：
10.1186/s12859-021-04343-w
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发表时间：
2021-09-15
影响因子：
3
通讯作者：
Dutilh BE
中科院分区：
文献类型：
--
作者：
Pappas N;Dutilh BE
病毒学领域极大地受益于元基因组学的最新发展，主要集中在病毒的发现上。然而，对数量不断增加的病毒基因组的功能注释却滞后了。这一点在原核病毒同源基团(PVOGs)数据库中蛋白质簇的注释程度上得到了强调，其目前的9518个pVOG中有83%具有未知功能。在这项研究中，我们描述了一种机器学习方法来探索pVOG之间潜在的功能关联。我们测量了七个基因组特征，并将它们用作随机森林分类器的输入，以预测pVOG对之间的蛋白质-蛋白质相互作用。在对10个不同的数据集进行了系统的性能评估后，我们得到了一个平均精度为0.77，接收操作特性下面积(AUROC)得分为0.83的预测因子。将其应用于一组2,133,027个pVOG-pVOG相互作用，使我们能够预测267,265个假定的相互作用，报告的概率大于0.65。在0.27的预期错误发现率下，我们通过预测它们与功能注释的pVOG的交互作用，将95.6%的先前未注释的pVOG放在功能上下文中。我们相信，这种概念验证方法，包裹在一个可重复和自动化的工作流程中，可以代表着朝着获得更完整的噬菌体生物学图景迈出的重要一步。网上版载有补充材料，可在10.1186/s12859-021-04343-w查阅。
The field of viromics has greatly benefited from recent developments in metagenomics, with significant efforts focusing on viral discovery. However, functional annotation of the increasing number of viral genomes is lagging behind. This is highlighted by the degree of annotation of the protein clusters in the prokaryotic Virus Orthologous Groups (pVOGs) database, with 83% of its current 9518 pVOGs having an unknown function. In this study we describe a machine learning approach to explore potential functional associations between pVOGs. We measure seven genomic features and use them as input to a Random Forest classifier to predict protein–protein interactions between pairs of pVOGs. After systematic evaluation of the model’s performance on 10 different datasets, we obtained a predictor with a mean accuracy of 0.77 and Area Under Receiving Operation Characteristic (AUROC) score of 0.83. Its application to a set of 2,133,027 pVOG-pVOG interactions allowed us to predict 267,265 putative interactions with a reported probability greater than 0.65. At an expected false discovery rate of 0.27, we placed 95.6% of the previously unannotated pVOGs in a functional context, by predicting their interaction with a pVOG that is functionally annotated. We believe that this proof-of-concept methodology, wrapped in a reproducible and automated workflow, can represent a significant step towards obtaining a more complete picture of bacteriophage biology. The online version contains supplementary material available at 10.1186/s12859-021-04343-w.
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影响因子：
28.3
作者：
Mavrich TN;Hatfull GF
通讯作者：
Hatfull GF
影响因子：
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Kyrpides NC
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28.3
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Banfield, Jillian F.
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Orchard S;Ammari M;Aranda B;Breuza L;Briganti L;Broackes-Carter F;Campbell NH;Chavali G;Chen C;del-Toro N;Duesbury M;Dumousseau M;Galeota E;Hinz U;Iannuccelli M;Jagannathan S;Jimenez R;Khadake J;Lagreid A;Licata L;Lovering RC;Meldal B;Melidoni AN;Milagros M;Peluso D;Perfetto L;Porras P;Raghunath A;Ricard-Blum S;Roechert B;Stutz A;Tognolli M;van Roey K;Cesareni G;Hermjakob H
通讯作者：
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影响因子：
5.1
作者：
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通讯作者：
Lavigne, Rob