Molecular & Cellular Toxicology

The Korean Society of Toxicogenomics and Toxicoproteomics (대한독성유전 단백체학회)
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BINGO: Biological Interpretation Through Statistically and Graph-theoretically Navigating Gene $Ontology^{TM}$

  • Lee, Sung-Geun (Bioinformatics Division, ISTECH Inc.) ;
  • Yang, Jae-Seong (Department of Life Science, Pohang University of Science and Technology) ;
  • Chung, Il-Kyung (Department of Plant Genetic Engineering, Catholic University of Daegu) ;
  • Kim, Yang-Seok (Bioinformatics Division, ISTECH Inc.)
  • Published : 2005.12.30
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Abstract

Extraction of biologically meaningful data and their validation are very important for toxicogenomics study because it deals with huge amount of heterogeneous data. BINGO is an annotation mining tool for biological interpretation of gene groups. Several statistical modeling approaches using Gene Ontology (GO) have been employed in many programs for that purpose. The statistical methodologies are useful in investigating the most significant GO attributes in a gene group, but the coherence of the resultant GO attributes over the entire group is rarely assessed. BINGO complements the statistical methods with graph-theoretic measures using the GO directed acyclic graph (DAG) structure. In addition, BINGO visualizes the consistency of a gene group more intuitively with a group-based GO subgraph. The input group can be any interesting list of genes or gene products regardless of its generation process if the group is built under a functional congruency hypothesis such as gene clusters from DNA microarray analysis.

Keywords

References

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