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Inference of gene-phenotype associations via protein-protein interaction and orthology.

ABSTRACT: One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing the inferred results with known gene-phenotype associations. The high Area Under the Curve values suggest a significant performance of our method. By applying our method to two human representative diseases, Type 2 Diabetes and Breast Cancer, we demonstrated that our method is able to identify related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The web server can be used to infer functions and putative phenotypes of a gene along with the candidate genes of a phenotype, and thus aids in disease candidate gene discovery. Our web server is available at http://jjwanglab.org/PhenoPPIOrth.

SUBMITTER: Wang P

PROVIDER: S-EPMC3806783 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Inference of gene-phenotype associations via protein-protein interaction and orthology.

Wang Panwen P Lai Wing-Fu WF Li Mulin Jun MJ Xu Feng F Yalamanchili Hari Krishna HK Lovell-Badge Robin R Wang Junwen J

PloS one 20131023 10

One of the fundamental goals of genetics is to understand gene functions and their associated phenotypes. To achieve this goal, in this study we developed a computational algorithm that uses orthology and protein-protein interaction information to infer gene-phenotype associations for multiple species. Furthermore, we developed a web server that provides genome-wide phenotype inference for six species: fly, human, mouse, worm, yeast, and zebrafish. We evaluated our inference method by comparing ...[more]

PMID: 24194887

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Project description:BACKGROUND: Phenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases. METHODOLOGY/PRINCIPAL FINDINGS: We proposed a technique called RWPCN (Random Walker on Protein Complex Network) for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes. CONCLUSIONS/SIGNIFICANCE: Guilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations.

Project description:BackgroundIdentification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged.ResultsBy considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E. coli K-12.ConclusionsThe accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.

Dataset Information

Inference of gene-phenotype associations via protein-protein interaction and orthology.

Publications

Inference of gene-phenotype associations via protein-protein interaction and orthology.

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