Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae
Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae
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DOI：
10.1093/nar/gkh978
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发表时间：
2004-01-01
影响因子：
14.9
通讯作者：
Xu, D
中科院分区：
文献类型：
--
作者：
Chen, Y;Xu, D
当我们进入基因组后的时代时，已经开发出各种高通量实验技术来表征基因组规模的生物系统。从高通量生物学数据中发现新的生物学知识是当今生物信息学的主要挑战。为了应对这一挑战，我们与玻兹曼机器一起开发了一种贝叶斯统计方法，并通过整合各种高通量生物学数据，包括酵母菌两种杂交数据，蛋白质复合物和微阵列基因表达谱谱谱谱，模拟了酵母糖含量酿酒酵母中蛋白质功能注释的退火。 。在我们的方法中，我们量化了功能相似性和高通量数据之间的关系，并将关系编码为“功能链接图”，其中每个节点代表一种蛋白质，每个边缘的重量都以贝叶斯的功能相似性来表征两个蛋白质。我们还将进化信息和蛋白质亚细胞定位信息整合到了预测中。基于我们的方法，系统地分配了2280个未经注释的蛋白质中的1802年。
As we are moving into the post genome-sequencing era, various high-throughput experimental techniques have been developed to characterize biological systems on the genomic scale. Discovering new biological knowledge from the high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a Bayesian statistical method together with Boltzmann machine and simulated annealing for protein functional annotation in the yeast Saccharomyces cerevisiae through integrating various high-throughput biological data, including yeast two-hybrid data, protein complexes and microarray gene expression profiles. In our approach, we quantified the relationship between functional similarity and high-throughput data, and coded the relationship into 'functional linkage graph', where each node represents one protein and the weight of each edge is characterized by the Bayesian probability of function similarity between two proteins. We also integrated the evolution information and protein subcellular localization information into the prediction. Based on our method, 1802 out of 2280 unannotated proteins in yeast were assigned functions systematically.