VAVIEN: An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks
VAVIEN: An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks
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DOI：
10.1089/cmb.2011.0154
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发表时间：
2011-11-01
影响因子：
1.7
通讯作者：
Koyutuerk, Mehmet
中科院分区：
文献类型：
--
作者：
Erten, Sinan;Bebek, Gurkan;Koyutuerk, Mehmet
全基因组连接和关联研究已经证明，在确定影响健康和疾病的遗传因素方面是有希望的。一个重要的挑战是缩小这些分析所涉及的候选基因的范围。蛋白质-蛋白质相互作用(PPI)网络有助于提取已知疾病和候选基因之间的功能关系，其原理是，与类似疾病有关的基因产物可能表现出显著的连接性/邻近性。基于信息流的方法被证明在确定候选疾病基因的优先顺序方面非常有效。在这篇文章中，我们利用PPI网络的拓扑结构来推断疾病关联上下文中的功能信息。我们的方法是基于这样的假设，即PPI网络被组织成递归方案，这些递归方案是不同蛋白质之间合作机制的基础。我们假设，与类似疾病相关的蛋白质在PPI网络中将表现出类似的拓扑特征。利用蛋白质在网络中相对于其他蛋白质的位置(即蛋白质的“拓扑轮廓”)，我们开发了一种新的度量来评估蛋白质在PPI网络中的拓扑相似性。然后，我们使用该度量来根据候选疾病基因的产物和已知疾病基因的产物的拓扑相似性来对候选疾病基因进行优先排序。我们通过使用集成的人类PPI网络和在线孟德尔遗传人类(OMIM)数据库进行系统的实验研究来测试所得到的算法VAVIEN。结果显示，VAVIEN优于其他基于网络的优先排序算法，可在www.diseasegenes.org上获得。
Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow-based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the "topological profile'' of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, VAVIEN, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. VAVIEN outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.