A graph-theoretic modeling on GO space for biological interpretation of gene clusters
A graph-theoretic modeling on GO space for biological interpretation of gene clusters
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DOI：
10.1093/bioinformatics/btg420
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发表时间：
2004-02-12
期刊：
影响因子：
5.8
通讯作者：
Kim, YS
中科院分区：
文献类型：
--
作者：
Lee, SG;Hur, JU;Kim, YS
动机：随着 DNA 微阵列技术的出现，全基因组转录的并行定量成为系统地了解复杂生物现象的绝佳机会。在对复杂基因表达数据的热情研究中，聚类方法已成为揭示这些数据中隐藏的有意义模式的有用工具。然而，完全基于数值表达数据的数学技术并没有显示聚类结果的生物学相关信息。结果：我们提出了一种基因簇生物学解释的新方法。我们的图论算法通过称为 GO 树的基因本体 (GO) 的修改结构来提取感兴趣的簇或组内基因的共同生物学属性。在用 GO 术语对基因进行注释后，利用 GO 术语的层次性质来查找基因簇的代表性生物学意义。此外，可以通过定义 GO 树上的距离函数来定量评估基因簇的生物学意义。我们的方法对许多统计聚类技术具有补充意义；我们可以利用生物本体论从不同的角度看待聚类问题。我们将该算法应用于众所周知的数据集，并通过GO Biological Process对聚类质量进行定量生物学评估，成功获得了基因簇的生物学特征。
Motivation: With the advent of DNA microarray technologies, the parallel quantification of genome-wide transcriptions has been a great opportunity to systematically understand the complicated biological phenomena. Amidst the enthusiastic investigations into the intricate gene expression data, clustering methods have been the useful tools to uncover the meaningful patterns hidden in those data. The mathematical techniques, however, entirely based on the numerical expression data, do not show biologically relevant information on the clustering results.Results: We present a novel methodology for biological interpretation of gene clusters. Our graph theoretic algorithm extracts common biological attributes of the genes within a cluster or a group of interest through the modified structure of gene ontology (GO) called GO tree. After genes are annotated with GO terms, the hierarchical nature of GO terms is used to find the representative biological meanings of the gene clusters. In addition, the biological significance of gene clusters can be assessed quantitatively by defining a distance function on the GO tree. Our approach has a complementary meaning to many statistical clustering techniques; we can see clustering problems from a different viewpoint by use of biological ontology. We applied this algorithm to the well-known data set and successfully obtained the biological features of the gene clusters with the quantitative biological assessment of clustering quality through GO Biological Process.