Function prediction of poorly-characterized protein genes found in genome sequences with high-performance supercomputers and its publication

使用批处理学习自组织映射图“BLSOM”用于二、三和四肽组合物，我们开发了一个系统，使蛋白质能够按功能分离(自组织)。通过分析以前被归类为COG(同源蛋白质簇)的蛋白质中的寡肽频率，BLSOM可以忠实地复制COG分类。这表明，由于与功能已知蛋白质缺乏显著的序列同源性而功能未知的蛋白质，可以基于寡肽组成的相似性而与功能已知蛋白质相关。BLSOM被应用于环境样品中来自混合基因组的大量蛋白质的功能预测。

Using Batch-Learning Self-Organizing Map"BLSOM"for di-, tri-and tetrapeptide compositions, we developed a system to enable separation(self-organization) of proteins by function. Analyzing oligopeptide frequencies in proteins previously classified into COGs(clusters of orthologous groups of proteins), BLSOMs could faithfully reproduce the COG classifications. This indicated that proteins, whose functions are unknown because of lack of significant sequence homology with function-known proteins, can be related to function-known proteins based on similarity in oligopeptide composition. BLSOM was applied to predict functions of vast quantities of proteins derived from mixed genomes in environmental samples.