Estimation of identification methods of gene clusters using GO term annotations from a hierarchical cluster tree

Authors:
Yoichi Yamada;Yuki Miyata;Masanori Higashihara;Kenji Satou
Affiliations:
Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa, Japan;Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa, Japan;Graduate School of Knowledge Science, Japan Advanced Institute of Science and Technology, Asahidai, Nomi, Ishikawa, Japan;Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-machi, Kanazawa, Japan
Venue:
MCBC'08 Proceedings of the 9th WSEAS International Conference on Mathematics & Computers In Biology & Chemistry
Year:
2008

Citing 3
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GO: :TermFinder---open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes

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Abstract

The hierarchical clustering algorithm has frequently been applied to grouping genes sharing a certain characteristic from a microarray data set. Identification of clusters from a hierarchical cluster tree is generally conducted by cutting the tree at a certain level. In this method, the most parent clusters are identified as mutually correlated gene groups and their child clusters are ignored. However the child clusters have a possibility to show more significant GO term annotation than their parent clusters. To overcome this problem, Toronen developed a novel algorithm based on the calculation of each GO annotation in all the clusters that satisfy a threshold of correlation coefficient. However comparison of the algorithm with the general method have not been done enough so far. Therefore we compared the general method with Toronen's proposed algorithm for identifying overrepresented GO terms, and confirmed availability of the proposed algorithm.