Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition
Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition
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DOI：
10.1093/bioinformatics/bth085
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发表时间：
2004-05-22
期刊：
影响因子：
5.8
通讯作者：
Doig, AJ
中科院分区：
文献类型：
--
作者：
Cai, YD;Doig, AJ
动机：基因组学的一个关键目标是为基因分配功能，尤其是孤儿序列。结果：我们使用 BLASTP 将 SBASE 数据库中的聚类功能域与每个蛋白质序列进行了比较。蛋白质的这种表示是一个向量，其中向量中的每个非零条目表示感兴趣的序列与 SBASE 域之间的显着匹配。机器学习方法最近邻算法（NNA）和支持向量机用于根据该信息预测蛋白质功能类别。我们发现使用 SBASE-A 数据库和 NNA 可以得到最好的结果，即 79% 的覆盖率和 72% 的准确率。我们测试了基于搜索 InterPro 序列基序并采用数据集中最重要的 BLAST 匹配的分配函数。我们应用功能域组成方法来预测2018年目前未分类的酵母开放阅读框的功能类别。
Motivation: A key goal of genomics is to assign function to genes, especially for orphan sequences.Results: We compared the clustered functional domains in the SBASE database to each protein sequence using BLASTP. This representation for a protein is a vector, where each of the non-zero entries in the vector indicates a significant match between the sequence of interest and the SBASE domain. The machine learning methods nearest neighbour algorithm (NNA) and support vector machines are used for predicting protein functional classes from this information. We find that the best results are found using the SBASE-A database and the NNA, namely 72% accuracy for 79% coverage. We tested an assigning function based on searching for InterPro sequence motifs and by taking the most significant BLAST match within the dataset. We applied the functional domain composition method to predict the functional class of 2018 currently unclassified yeast open reading frames.