Vavien: An Algorithm for Prioritizing Candidate Disease Genes Based on Topological Similarity of Proteins in Interaction Networks

Abstract

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.

Going the Distance for Protein Function Prediction: A New Distance Metric for Protein Interaction Networks

“…Given some fixed , we define to be the expected number of times that a random walk starting at and proceeding for steps, will visit . Note that bears some resemblance to previous diffusion approaches suggested for PPI networks, see [23], [24], though we will next be building metric structure on top of in a completely novel way. For now, note that captures our intuition regarding similarity, because node pairs connected by many short paths of low-degree nodes will tend to have high values.…”

Gene prioritization for livestock diseases by data integration

“…This may in turn explain that the prioritization was more consistent in the complex-based approach compared with the gene-based approach. This is in line with complex-based disease gene prioritizations approaches used in human studies (7,9,10,18,20,(42)(43)(44)(45). Compared with human and model organisms (e.g., mouse, yeast, and worm), much fewer protein-protein interactions and gene-associated phenotypes are available in cattle.…”

“…Network-based disease gene prioritization relies on the observation that genes that are functionally linked in pathways or protein complexes are often associated with identical or similar phenotypes (7,9,10,18,20,(42)(43)(44)(45). Thus, when one or more proteins in a given protein complex are already implicated in a disease of interest, then the remaining network members immediately become strong disease gene candidates.…”

“…Thus, when one or more proteins in a given protein complex are already implicated in a disease of interest, then the remaining network members immediately become strong disease gene candidates. Networkbased gene prioritization typically integrates information from disease phenotypes, gene-associated phenotypes, and proteinprotein interactions (PPI) and has been substantially studied, validated, and implemented in several bioinformatic approaches (7,9,10,18,20,(42)(43)(44)(45). We reasoned that networkbased disease gene prioritization would also be beneficial for prioritizations of candidate genes for complex diseases in livestock species.…”

Random Walking on Functional Interaction Networks to Rank Genes Involved in Cancer

“…Different algorithms to rank genes in biomolecular networks have been proposed in the literature [8,9,10,11]. In this context networks are usually represent through an undirected graph G = (V, E), where nodes υ ∈ V correspond to genes, and edges e ∈ E are weighted according to the evidence of the functional interaction between genes [6,7].…”