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首页> 外文期刊>Nucleic Acids Research >A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters
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A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters

机译:启发式图比较算法及其在功能相关酶簇检测中的应用

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The availability of computerized knowledge on biochemical pathways in the KEGG database opens new opportunities for developing computational methods to characterize and understand higher level functions of complete genomes. Our approach is based on the concept of graphs; for example, the genome is a graph with genes as nodes and the pathway is another graph with gene products as nodes. We have developed a simple method for graph comparison to identify local similarities, termed correlated clusters, between two graphs, which allows gaps and mismatches of nodes and edges and is especially suitable for detecting biological features. The method was applied to a comparison of the complete genomes of 10 microorganisms and the KEGG metabolic pathways, which revealed, not surprisingly, a tendency for formation of correlated clusters called FRECs (functionally related enzyme clusters). However, this tendency varied considerably depending on the organism. The relative number of enzymes in FRECs was close to 50% for Bacillus subtilis and Escherichia coli, but was <10% for Synechocystis and Saccharomyces cerevisiae. The FRECs collection is reorganized into a collection of ortholog group tables in KEGG, which represents conserved pathway motifs with the information about gene clusters in all the completely sequenced genomes.
机译:KEGG数据库中有关生化途径的计算机化知识的可用性为开发用于表征和理解完整基因组更高功能的计算方法提供了新机会。我们的方法基于图的概念;例如,基因组是一个以基因为节点的图,而通路是另一个以基因产物为节点的图。我们已经开发出一种简单的图形比较方法,以识别两个图形之间的局部相似性(称为相关簇),该方法允许节点和边的间隙和不匹配,尤其适合于检测生物学特征。该方法用于比较10种微生物的完整基因组和KEGG代谢途径,这毫不奇怪地揭示了形成称为FRECs(功能相关的酶簇)的相关簇的趋势。但是,这种趋势根据生物体而有很大的不同。对于枯草芽孢杆菌和大肠杆菌,FRECs中酶的相对数量接近50%,而集胞囊藻和酿酒酵母的酶相对数量则小于10%。 FRECs集合在KEGG中重组为直系同源物组表的集合,该表代表了保守的途径基序,以及有关所有完全测序基因组中基因簇的信息。

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