Combining biological networks to predict genetic interactions

Wong, Sunny Y.; Zhang, Lan V.; Tong, Amy; Z, Li; Goldberg, Debra S.; King, Oliver D.; Lesage, Guillaume; Vidal, Marc; Andrews, Brenda; Bussey, Howard; Boone, Charles; Roth, Frederick P.

doi:10.1073/pnas.0406614101

Cited by 229 publications

(236 citation statements)

References 47 publications

(41 reference statements)

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“…Our method extends beyond previous work that has used physical interaction networks to predict genetic interactions (Wong et al 2004;Kelley and Ideker 2005;Le Meur and Gentleman 2008) and we demonstrated that considering functional interactions offers improved predictive power. Two reasons for this are the integration of many more diverse datasets and the fact that functional couplings transcend physical interactions.…”

Section: Discussion Functional Network Provide a General Strategy Fomentioning

confidence: 52%

“…Previous work has shown that it is possible to predict genetic interactions using physical protein-protein interaction networks Wong et al 2004;Kelley and Ideker 2005;Le Meur and Gentleman 2008). We therefore asked whether using functional interactionswhich extend beyond physical interactions-improves the predictive power of a network.…”

Section: Functional Network Are More Predictive Than Current Proteinmentioning

confidence: 99%

“…These screens have highlighted the enormous extent to which mutations in one gene alter the phenotypic outcome of mutations in a second locus. Furthermore, the data from these screens show that most genetic interactions do not represent simple cases of ''redundancy'' between genes encoding similar biochemical functions Wong et al 2004;Kelley and Ideker 2005;Ulitsky and Shamir 2007).…”

mentioning

confidence: 99%

“…Analysis of genetic interaction data from model organisms has shown that genes that participate in common biological processes often show similar patterns of genetic interactions Kelley and Ideker 2005;Ye et al 2005;Ulitsky and Shamir 2007). This means that genetic interactions can, in part, be predicted using physical interaction data to identify proteins that act in common functional pathways (Wong et al 2004;Le Meur and Gentleman 2008).…”

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confidence: 99%

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Predicting genetic modifier loci using functional gene networks

et al. 2010

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Most phenotypes are genetically complex, with contributions from mutations in many different genes. Mutations in more than one gene can combine synergistically to cause phenotypic change, and systematic studies in model organisms show that these genetic interactions are pervasive. However, in human association studies such nonadditive genetic interactions are very difficult to identify because of a lack of statistical power-simply put, the number of potential interactions is too vast. One approach to resolve this is to predict candidate modifier interactions between loci, and then to specifically test these for associations with the phenotype. Here, we describe a general method for predicting genetic interactions based on the use of integrated functional gene networks. We show that in both Saccharomyces cerevisiae and Caenorhabditis elegans a single high-coverage, high-quality functional network can successfully predict genetic modifiers for the majority of genes. For C. elegans we also describe the construction of a new, improved, and expanded functional network, WormNet 2.Using this network we demonstrate how it is possible to rapidly expand the number of modifier loci known for a gene, predicting and validating new genetic interactions for each of three signal transduction genes. We propose that this approach, termed network-guided modifier screening, provides a general strategy for predicting genetic interactions. This work thus suggests that a high-quality integrated human gene network will provide a powerful resource for modifier locus discovery in many different diseases.

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Section: Discussion Functional Network Provide a General Strategy Fomentioning

confidence: 52%

Section: Functional Network Are More Predictive Than Current Proteinmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting genetic modifier loci using functional gene networks

et al. 2010

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“…Orthogonal sets of data (i.e., protein interactions, protein localization, gene expression arrays), from different cellular conditions are proving useful for network analysis [49,50]. To provide deeper functional insight into protein interaction networks, the relevant modules (domains and linear motifs) within a network can be used to decipher its observed interactions (i.e., domain-domain or motif-domain), and to predict their stability.…”

Section: Topologies and Architectures Of Modular Interaction Networkmentioning

confidence: 99%

Synthetic modular systems – reverse engineering of signal transduction

Pawson

Linding

2005

FEBS Letters

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During the last decades, biology has decomposed cellular systems into genetic, functional and molecular networks. It has become evident that these networks consist of components with specific functions (e.g., proteins and genes). This has generated a considerable amount of knowledge and hypotheses concerning cellular organization. The idea discussed here is to test the extent of this knowledge by reconstructing, or reverse engineering, new synthetic biological systems from known components. We will discuss how integration of computational methods with proteomics and engineering concepts might lead us to a deeper and more abstract understanding of signal transduction systems. Designing and successfully introducing synthetic proteins into cellular pathways would provide us with a powerful research tool with many applications, such as development of biosensors, protein drugs and rewiring of biological pathways.

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Bioinformatics Tools for Gene Function Prediction

Cui

2011

Gene Discovery for Disease Models

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Combining biological networks to predict genetic interactions

Cited by 229 publications

References 47 publications

Predicting genetic modifier loci using functional gene networks

Predicting genetic modifier loci using functional gene networks

Synthetic modular systems – reverse engineering of signal transduction

Bioinformatics Tools for Gene Function Prediction

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