Ortutay Csaba, Vihinen Mauno
Institute of Medical Technology, FI-33014 University of Tampere and Tampere University Hospital, FI-33520 Tampere, Finland.
Nucleic Acids Res. 2009 Feb;37(2):622-8. doi: 10.1093/nar/gkn982. Epub 2008 Dec 10.
Disease gene identification is still a challenge despite modern high-throughput methods. Many diseases are very rare or lethal and thus cannot be investigated with traditional methods. Several in silico methods have been developed but they have some limitations. We introduce a new method that combines information about protein-interaction network properties and Gene Ontology terms. Genes with high-calculated network scores and statistically significant gene ontology terms based on known diseases are prioritized as candidate genes. The method was applied to identify novel primary immunodeficiency-related genes, 26 of which were found. The investigation uses the protein-interaction network for all essential immunome human genes available in the Immunome Knowledge Base and an analysis of their enriched gene ontology annotations. The identified disease gene candidates are mainly involved in cellular signaling including receptors, protein kinases and adaptor and binding proteins as well as enzymes. The method can be generalized for any disease group with sufficient information.
尽管有现代高通量方法，但疾病基因识别仍然是一项挑战。许多疾病非常罕见或具有致死性，因此无法用传统方法进行研究。已经开发了几种计算机模拟方法，但它们存在一些局限性。我们引入了一种新方法，该方法结合了蛋白质相互作用网络特性和基因本体术语的信息。基于已知疾病，具有高计算网络分数和统计学上显著基因本体术语的基因被优先作为候选基因。该方法被应用于识别新的原发性免疫缺陷相关基因，共发现了26个。该研究使用了免疫组知识库中所有基本免疫组人类基因的蛋白质相互作用网络，并对其丰富的基因本体注释进行了分析。所识别的疾病基因候选物主要参与细胞信号传导，包括受体、蛋白激酶、衔接蛋白和结合蛋白以及酶。该方法可以推广到任何有足够信息的疾病组。
