Difference between revisions of "Research"
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==Research Summary== | ==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. | 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 Philosophy== | ||
| + | '''*4P Research''' | ||
| + | *Play: Your lab must be your playground. | ||
| + | *Ponder: More thinking, Less labor. | ||
| + | *Pride: Be proud of yourself and your work. | ||
| + | *Provide: Public has been support your work, thus return your discovery to them. | ||
| + | |||
| + | ''*If science is good, scientists must be happy whether they are successful or not.'' | ||
| + | ''*The more learn, the less know.'' | ||
==Collaborators== | ==Collaborators== | ||
Revision as of 17:15, 10 April 2011
Contents |
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 Philosophy
*4P Research
- Play: Your lab must be your playground.
- Ponder: More thinking, Less labor.
- Pride: Be proud of yourself and your work.
- Provide: Public has been support your work, thus return your discovery to them.
*If science is good, scientists must be happy whether they are successful or not. *The more learn, the less know.
Collaborators
- Edward Marcotte, University of Texas at Austin, USA
- Andrew Fraser, University of Toronto, Canada
- Ben Lehner, Systems Biology Unit, EMBL-CRG, Spain
- Rhee, Carnegie Institution of Science, USA
- Pamela Ronald, University of California at Davis, USA
- Matthew Hurles, Sanger Institute, UK
- Philip Benfey, Duke University, USA
- Sangsun Yoon, Yonsei Medical School, Korea
- Dongryul Lee, Cha Medical School, Korea
- Yongsun Bahn, Yonsei University, Korea
- Sangjun Ha, Yonsei University, Korea
