Difference between revisions of "Research"

From Bioinformatics Lab
Jump to: navigation, search
(Research Highlight)
(39 intermediate revisions by 2 users not shown)
Line 1: Line 1:
==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.
+
<div style="float:right;margin:5px 40px;">{{#widget:AddHtml|content=<img src="../wiki/wordcloud_2013_2017.jpg" alt="Word Cloud from Abstraction below publications"  title="Word Cloud of NBL Publications' Abstract" />}}</div>
 +
<div style="text-align:justify;">
 +
'''Network Medicine via network-augmented analysis of clinical genomics data'''
  
==Research Philosophy==
+
Recent genomic revolution opened new avenues to understanding human disease. However, it also revealed complex nature of human disease. For example, currently, more than several hundred genes are believed to be associated with human cancer. Genome-wide association study (GWAS) suggests hundreds of disease-related genes, but together explaining only 10-20% of total disease inheritance at most. Because of this overwhelming complexity of disease-causing pathway, modern disease genetics needs to be more systematic and predictive. However, the network organization of disease systems also provide big opportunity to investigate the genetic organization of complex diseases through the molecular networks. Our research group has developed co-functional gene networks for many organisms including human (HumanNet) and various network-guided methods to identify novel disease genes and modules.
'''4P Research (Play, Ponder, Pride, Provide)'''
+
.</div>
*'''Play''': Your lab must be your playground.
+
*'''Ponder''': More thinking, Less labor.
+
*'''Pride''': Be proud of yourself and your work.
+
*'''Provide''': People supported your work, thus you must return your discovery to people.
+
  
'''Good science must make scientists happy.'''
+
=='''Research Highlight'''==
 
+
*[[media:research_highlight_004.pdf|2011 Nature Reviews Genetics, Research highlight (Predicting genetic interaction)]]
'''The more learn, the less know.'''
+
*[[media:research_highlight_002.pdf|2008 Bioessay, What the papers say (WormNet)]]
 
+
*[[media:research_highlight_003.pdf|2008 Genome Biology, Minireview (WormNet)]]
==Research Highlight==
+
*[[media:research_highlight_001.pdf|2008 Nature methods, Research highlight (WormNet)]]
*[[media:research_highlight_004.pdf|2011 Nature Reviews Genetics, Research highlight: A network of interactors]]
+
*[[media:Gallery_NGcover_SmallVer.jpg|2008 Nature Genetics coverstory (WormNet): Network perturbation predicts phenotype]]
*[[media:research_highlight_002.pdf|2008 Bioessay: Predicting phenotypic effects of gene perturbations in ''C. elegans'' using an integrated network model]]
+
*[[media:research_highlight_003.pdf|2008 Wormnet: a crystal ball for ''Caenorhabditis elegans'']]
+
*[[media:research_highlight_001.pdf|2008 Nature methods, Research highlight: Networking an organism]]
+
*[[media:Gallery_NGcover_SmallVer.jpg|2008 Nature Genetics coverstory: Network perturbation predicts phenotype]]
+
 
+
==Collaborators==
+
*[http://www.marcottelab.org/index.php/Main_Page Edward Marcotte, University of Texas at Austin, USA]
+
*[http://www.fraserlab.org/ Andrew Fraser, University of Toronto, Canada]
+
*[http://www.crg.es/ben_lehner Ben Lehner, Systems Biology Unit, EMBL-CRG, Spain]
+
*[http://dpb.carnegiescience.edu/labs/rhee-lab Sue Rhee, Carnegie Institution of Science, USA]
+
*[http://indica.ucdavis.edu/ Pamela Ronald, University of California at Davis, USA]
+
*[http://www.sanger.ac.uk/research/faculty/mhurles/ Matthew Hurles, Sanger Institute, UK]
+
*[http://www.biology.duke.edu/benfeylab/index.htm Philip Benfey, Duke University, USA]
+
*Sangsun Yoon, Yonsei Medical School, Korea
+
*[http://www.chamc.co.kr/professor/drleedr/ Dongryul Lee, Cha Medical School, Korea]
+
*[http://www.bahnlab.com/ Yongsun Bahn, Yonsei University, Korea]
+
*[http://web.yonsei.ac.kr/labsi/ Sangjun Ha, Yonsei University, Korea]
+

Revision as of 17:56, 26 February 2018

Research Summary

Word Cloud from Abstraction below publications

Network Medicine via network-augmented analysis of clinical genomics data

Recent genomic revolution opened new avenues to understanding human disease. However, it also revealed complex nature of human disease. For example, currently, more than several hundred genes are believed to be associated with human cancer. Genome-wide association study (GWAS) suggests hundreds of disease-related genes, but together explaining only 10-20% of total disease inheritance at most. Because of this overwhelming complexity of disease-causing pathway, modern disease genetics needs to be more systematic and predictive. However, the network organization of disease systems also provide big opportunity to investigate the genetic organization of complex diseases through the molecular networks. Our research group has developed co-functional gene networks for many organisms including human (HumanNet) and various network-guided methods to identify novel disease genes and modules.

.

Research Highlight

Personal tools
Namespaces

Variants
Actions
Navigation
Toolbox