Difference between revisions of "Research"

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	=='''Research Summary'''==		=='''Research Summary'''==
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	The ultimate goal of biological research is to manipulate traits that are important for medicine, agriculture, and bio-industry. This challenging task first requires good understanding of association between genotype and phenotype. Because of high complexity of genotype as well as phenotype, complexity of the genotype-phenotype association could be even untouchable by combinatorial explosion of the number of possible associations. Therefore, modern genetics needs to be more systematic and predictive. Recently we proposed network-guided approach for genetics of complex traits. First, we construct probabilistic functional gene networks for cells or organisms by benchmarking and integrating heterogeneous multi-omics data that are in general publicly available. Then, using guilt-by-association, and other algorithms of network propagation of known biological information, we predict gene functions, phenotypic effect of loss-of-function, and epistatic interaction. The information can contribute to reconstruction of map between genotype and phenotype. The network-guided genetics method has been effectively applied for various organisms; from simple microbe yeast, to multicellular animal C. elegans, to the reference plant Arabidopsis, to the reference crop rice, and to the human.		The ultimate goal of biological research is to manipulate traits that are important for medicine, agriculture, and bio-industry. This challenging task first requires good understanding of association between genotype and phenotype. Because of high complexity of genotype as well as phenotype, complexity of the genotype-phenotype association could be even untouchable by combinatorial explosion of the number of possible associations. Therefore, modern genetics needs to be more systematic and predictive. Recently we proposed network-guided approach for genetics of complex traits. First, we construct probabilistic functional gene networks for cells or organisms by benchmarking and integrating heterogeneous multi-omics data that are in general publicly available. Then, using guilt-by-association, and other algorithms of network propagation of known biological information, we predict gene functions, phenotypic effect of loss-of-function, and epistatic interaction. The information can contribute to reconstruction of map between genotype and phenotype. The network-guided genetics method has been effectively applied for various organisms; from simple microbe yeast, to multicellular animal C. elegans, to the reference plant Arabidopsis, to the reference crop rice, and to the human.
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	=='''Research Highlight'''==		=='''Research Highlight'''==
	*[[media:research_highlight_004.pdf|2011 Nature Reviews Genetics, Research highlight (Predicting genetic interaction)]]		*[[media:research_highlight_004.pdf|2011 Nature Reviews Genetics, Research highlight (Predicting genetic interaction)]]

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Revision as of 11:55, 1 October 2014

Research Summary

The ultimate goal of biological research is to manipulate traits that are important for medicine, agriculture, and bio-industry. This challenging task first requires good understanding of association between genotype and phenotype. Because of high complexity of genotype as well as phenotype, complexity of the genotype-phenotype association could be even untouchable by combinatorial explosion of the number of possible associations. Therefore, modern genetics needs to be more systematic and predictive. Recently we proposed network-guided approach for genetics of complex traits. First, we construct probabilistic functional gene networks for cells or organisms by benchmarking and integrating heterogeneous multi-omics data that are in general publicly available. Then, using guilt-by-association, and other algorithms of network propagation of known biological information, we predict gene functions, phenotypic effect of loss-of-function, and epistatic interaction. The information can contribute to reconstruction of map between genotype and phenotype. The network-guided genetics method has been effectively applied for various organisms; from simple microbe yeast, to multicellular animal C. elegans, to the reference plant Arabidopsis, to the reference crop rice, and to the human.

Research Highlight

• 2011 Nature Reviews Genetics, Research highlight (Predicting genetic interaction)
• 2008 Bioessay, What the papers say (WormNet)
• 2008 Genome Biology, Minireview (WormNet)
• 2008 Nature methods, Research highlight (WormNet)
• 2008 Nature Genetics coverstory (WormNet): Network perturbation predicts phenotype

Collaborators

Human/Animal Systems Biology

• Edward Marcotte, University of Texas at Austin, USA
• Ben Lehner, Systems Biology Unit, EMBL-CRG, Spain
• Jonghoon Kim, Korea University, Korea

Crop/Plant Systems Biology

• Sue Rhee, Carnegie Institution of Science, USA
• Pamela Ronald, University of California at Davis, USA
• Thomas Brutnell, Danforth Plant Science Center, USA
• Sangdong Yoo, Korea University, Korea

Microbial Systems Biology

• Yongsun Bahn, Yonsei University, Korea
• Sangsun Yoon, Yonsei Medical School, Korea