OAHG: an integrated resource for annotating human genes with multi-level ontologies
OAHG: an integrated resource for annotating human genes with multi-level ontologies
复制标题
OAHG：用多级本体注释人类基因的综合资源。
DOI：
10.1038/srep34820
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发表时间：
2016-10-05
影响因子：
4.6
通讯作者：
Zhou, Meng
中科院分区：
文献类型：
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作者：
Cheng, Liang;Sun, Jie;Zhou, Meng
OAHG是一个整合资源，旨在通过涉及基因本体（GO）、疾病本体（DO）和人类表型本体（HPO）的多层次本体，建立人类蛋白质编码基因（PCGs）、mirna和lncrna的综合功能标注资源。以往的许多研究都集中在从不同角度推断PCGs和非编码RNA基因的特性和生物学功能。在过去的几十年里，已经设计了一些数据库来分别注释PCGs、mirna和lncrna的功能。这些数据库中的部分功能描述被映射为标准化术语，例如GO，这可能有助于进行进一步的分析。尽管取得了这些进展，但没有全面的资源记录这三种重要基因的功能。OAHG的最新版本1.0（2016年6月发布）集成了GO、DO和HPO三个本体、6个基因功能数据库和2个交互数据库。目前，OAHG包含1,434,694个条目，涉及16,929个pcg， 637个mirna， 193个lncrna和24,894个本体术语。在性能评价中，OAHG表现出与现有基因相互作用和本体结构的一致性。例如，结构更相似的术语可能与更多相关基因相关（Pearson相关γ (2) = 0.2428, p < 2.2e-16）。
OAHG, an integrated resource, aims to establish a comprehensive functional annotation resource for human protein-coding genes (PCGs), miRNAs, and lncRNAs by multi-level ontologies involving Gene Ontology (GO), Disease Ontology (DO), and Human Phenotype Ontology (HPO). Many previous studies have focused on inferring putative properties and biological functions of PCGs and non-coding RNA genes from different perspectives. During the past several decades, a few of databases have been designed to annotate the functions of PCGs, miRNAs, and lncRNAs, respectively. A part of functional descriptions in these databases were mapped to standardize terminologies, such as GO, which could be helpful to do further analysis. Despite these developments, there is no comprehensive resource recording the function of these three important types of genes. The current version of OAHG, release 1.0 (Jun 2016), integrates three ontologies involving GO, DO, and HPO, six gene functional databases and two interaction databases. Currently, OAHG contains 1,434,694 entries involving 16,929 PCGs, 637 miRNAs, 193 lncRNAs, and 24,894 terms of ontologies. During the performance evaluation, OAHG shows the consistencies with existing gene interactions and the structure of ontology. For example, terms with more similar structure could be associated with more associated genes (Pearson correlation gamma(2) = 0.2428, p < 2.2e-16).