Tools for Ontology Annotation: dcGO

海量的生物数据，特别是测序的基因组及其最终产物蛋白质序列，对人类手工交叉参考文献提出了一个令人望而却步的挑战。解决方案是使用本体（受控词汇表），这样计算机系统就可以处理和连接人类也能读懂的理性关系。最常用的本体是基因本体（Gene ontology， GO），它旨在描述蛋白质的功能。GO最常用的资源是UniProt。提供蛋白质序列和功能注释。蛋白质的GO注释有两种主要类型的证据：一种来自实验或策划注释，另一种来自IEA（推断电子注释）。IEA主要来自InterPro的映射。InterPro是一个集成的数据库，它结合了来自不同来源的不同定义的蛋白质结构域（或精确的签名）。在InterPro中，域的注释是通过同源性手工管理的。最近，我们开创了一种追求全自动工具的方法：以领域为中心的基因本体（dcGO）。dcGO是基于域的GO注释的唯一可免费获得的全自动通用方法。由于其自动化的性质，dcGO比InterPro的手策展更广泛。通过比较，我们已经表明，在UniProt中蛋白质的功能预测方面，dcGO的质量至少与InterPro一样好。因此，将dcGO扩展到InterPro将对UniProt IEA的覆盖范围和质量产生相当大的下游影响，这是世界各地的科学家经常使用的主要注释来源。除了GO，我们还将使用其他生物医学本体（如描述疾病和表型的本体）提供领域注释。对于这些类型的注释，目前只有dcGO方法可以以这种直接的方式进行扩展。在以社区为基础的蛋白质功能预测关键评估CAFA中，dcGO已被证明可用于自动功能注释。在下一个CAFA中，我们将证明dcGO也是其他本体的合适基线，因此可以包含在InterPro和UniProt中。为了满足用户对整个序列进行分析的要求，我们还将提供本体丰富分析，这将允许用户了解哪些功能（和其他相关知识）在提交的序列中被过度表示。这个用户驱动的工具将以一种计算效率高的方式实现；所需的时间将以秒或分钟为单位，而不是以小时或天为单位。总之，拟议的研究将与InterProt， UniProt， CAFA和最终用户紧密联系，这些合作关系将有助于将我们以领域为中心的解决方案转化为注释和分析基因组序列的行业标准。

The massive amount of biological data, especially sequenced genomes and their end-product protein sequences, poses a prohibitive challenge for humans to manually cross-reference in literature. The solution is to use ontologies - controlled vocabularies - so that computer systems can process and connect rational relationships that are also readable by humans. The most used ontology is Gene Ontology (GO) that intends to describe protein functions.The most used resource for GO is UniProt. It provides protein sequence and functional annotation. GO annotations for proteins have two main types of evidence: one from experimental or curated annotations, the other from IEA (Inferred Electronic Annotations). IEA comes mostly from an InterPro mapping. InterPro is an integrated database that combines protein domains (or precisely signatures) from diverse sources with different definitions. In InterPro, annotations for domains are hand-curated via homology. Recently, we have pioneered a methodology in pursuit of a fully-automated tool: domain-centric Gene Ontology (dcGO). dcGO is the only freely available fully-automated general methodology for domain-based GO annotation. Thanks to its automated nature, dcGO is much more extensive than the InterPro hand curation. By comparison, we have shown that the quality of dcGO is at least as good as InterPro in terms of function predictions of proteins in UniProt. Therefore, extending dcGO to InterPro will have a considerable downstream impact on the UniProt IEA coverage and quality which is what scientists from around the world are routinely using as their primary source of annotation. In addition to GO, we will also provide domain annotations using other biomedical ontologies such as those describing diseases and phenotypes. For these kinds of annotations, currently only the dcGO approach can be extended in this way in a straightforward manner. In CAFA, a community-based critical assessment of protein function prediction, dcGO has been demonstrated for use in automated function annotation. In the next CAFA, we will prove that dcGO is also the suitable baseline for other ontologies and thus for inclusion in InterPro and UniProt. To meet the user requests for analysing sequences as a whole, we will also provide ontology enrichment analysis that will allow the users to understand which functions (and other relevant knowledge) are overrepresented in sequences submitted. This user-driven tool will be implemented in a computationally efficient way; the required will be on the order of seconds or minutes, rather than hours or days. In summary, the proposed research will be undertaken with a tight link to InterProt, UniProt, CAFA and end-users, and these collaborative connections will help translate our domain-centric solution into the industry standard for annotating and analyzing genome sequences.