DA DA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

Erten, Sinan; Bebek, Gürkan; Ewing, Rob M.; Koyutürk, Mehmet

doi:10.1186/1756-0381-4-19

Cited by 155 publications

(139 citation statements)

References 34 publications

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“…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.…”

Section: Discussionmentioning

confidence: 81%

“…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.…”

mentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Gene prioritization for livestock diseases by data integration

Jiang¹,

Sørensen²,

Thomsen³

et al. 2012

Physiological Genomics

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Identifying causal genes that underlie complex traits such as susceptibility to disease is a primary aim of genetic and biomedical studies. Genetic mapping of quantitative trait loci (QTL) and gene expression profiling based on high-throughput technologies are common first approaches toward identifying associations between genes and traits; however, it is often difficult to assess whether the biological function of a putative candidate gene is consistent with a particular phenotype. Here, we have implemented a network-based disease gene prioritization approach for ranking genes associated with quantitative traits and diseases in livestock species. The approach uses ortholog mapping and integrates information on disease or trait phenotypes, gene-associated phenotypes, and protein-protein interactions. It was used for ranking all known genes present in the cattle genome for their potential roles in bovine mastitis. Gene-associated phenome profile and transcriptome profile in response to Escherichia coli infection in the mammary gland were integrated to make a global inference of bovine genes involved in mastitis. The top ranked genes were highly enriched for pathways and biological processes underlying inflammation and immune responses, which supports the validity of our approach for identifying genes that are relevant to animal health and disease. These gene-associated phenotypes were used for a local prioritization of candidate genes located in a QTL affecting the susceptibility to mastitis. Our study provides a general framework for prioritizing genes associated with various complex traits in different species. To our knowledge this is the first time that gene expression, ortholog mapping, protein interactions, and biomedical text data have been integrated systematically for ranking candidate genes in any livestock species.

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Section: Discussionmentioning

confidence: 81%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Gene prioritization for livestock diseases by data integration

Jiang¹,

Sørensen²,

Thomsen³

et al. 2012

Physiological Genomics

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show abstract

“…This comparison is done via a degreeapproximating node/gene randomisation [21,22], which permutates gene labels but preserves node degrees. Similar to the robustness assessment above, for a permutated list of genes, a survival network is identified with the same/similar size as the target network.…”

Section: Robustness Of the Patient-survival Gene Network Under Data Rmentioning

confidence: 99%

The `dnet¿ approach promotes emerging research on cancer patient survival

Fang¹,

Gough²

2014

Genome Medicine

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We present the 'dnet' package and apply it to the 'TCGA' mutation and clinical data of >3,000 patients. We uncover the existence of an underlying gene network that at least partially controls cancer 'survivalness', with mutations that are significantly correlated with patient survival, yet independent of tumour origin and type. The survivalness network has natural community structure corresponding to tumour hallmarks, and contains genes that are potentially druggable in the clinic. This network has evolutionary roots in Deuterostomia identifying PTK2 and VAV1 as under-valued relative to more studied genes from that era. The 'dnet' R package is available at http://cran.r-project.org/package=dnet.

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“…While software implementing network propagation (Prince) (Vanunu et al, 2010) and statistical correction (DaDa) (Erten et al, 2011) are available, we here report results based on our implementation of these two algorithms. We run all algorithms using identical settings for data integration and incorporation of disease similarity scores, differing from each other only in how network information is utilized in computing disease association scores.…”

Section: Figmentioning

confidence: 99%

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

Erten

Bebek

Koyutürk

2011

Journal of Computational Biology

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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.

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DA DA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

Cited by 155 publications

References 34 publications

Gene prioritization for livestock diseases by data integration

Gene prioritization for livestock diseases by data integration

The `dnet¿ approach promotes emerging research on cancer patient survival

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

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