ArticlePLoS computational biology2012
Multiple genetic interaction experiments provide complementary information useful for gene function prediction.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Integrative structure determination of histones H3 and H4 using genetic interactions.The FEBS journal · 2023Article
- Miro, a Rho GTPase genetically interacts with Alzheimer's disease-associated genes (Biology open · 2020Article
- Predicting synthetic lethal interactions using conserved patterns in protein interaction networks.PLoS computational biology · 2019Article
- A robust platform for chemical genomics in bacterial systems.Molecular biology of the cell · 2016Article
- Predicting protein function and other biomedical characteristics with heterogeneous ensembles.Methods (San Diego, Calif.) · 2016Article
- Data Imputation in Epistatic MAPs by Network-Guided Matrix Completion.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Prediction of Genetic Interactions Using Machine Learning and Network Properties.Frontiers in bioengineering and biotechnology · 2015Review
- Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli.PLoS genetics · 2014Article
- Network Assessor: an automated method for quantitative assessment of a network's potential for gene function prediction.Frontiers in genetics · 2014Article
- Inferring gene function and network organization in Drosophila signaling by combined analysis of pleiotropy and epistasis.G3 (Bethesda, Md.) · 2013Article
- Quantitative genetic-interaction mapping in mammalian cells.Nature methods · 2013Article
- Genetic variants and their interactions in disease risk prediction - machine learning and network perspectives.BioData mining · 2013Article
Corrections and comments
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2 authors.
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Abstract
Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.
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