Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken, Germany.
Bioinformatics. 2012 Jan 15;28(2):269-76. doi: 10.1093/bioinformatics/btr631. Epub 2011 Dec 16.
Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account.
We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.
有许多注释可用于从分子过程、细胞定位、组织表达、蛋白质结构域组成、蛋白质相互作用、疾病关联和其他特性等方面对基因和蛋白质进行功能描述。搜索这些不断增长的信息可以发现基因和蛋白质之间新的生物学关系。为了便于搜索，需要能够衡量基因和蛋白质注释相似性的方法。然而，目前大多数相似性方法仅关注来自基因本体论（GO）的注释，而不考虑其他注释来源。
我们引入了新的方法 BioSim，该方法结合了多种注释来源，以量化基因和蛋白质的功能相似性。我们将我们的方法与另外四个适用于使用多种注释来源的知名方法进行了比较。我们通过仅使用基于 GO 的注释或我们的大型数据仓库 BioMyn 进行注释来搜索已知的功能关系，以此来评估这些方法。该仓库整合了人类基因和蛋白质的许多不同注释来源。我们观察到，当包含多种注释来源时，几乎所有方法的搜索性能都有了显著提高。特别是，我们的方法在召回率和平均精度方面优于其他方法。
