Computational Methods for Annotation Transfers from Sequence
Computational Methods for Annotation Transfers from Sequence
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DOI：
10.1007/978-1-4939-3743-1_5
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发表时间：
2017-01-01
期刊：
影响因子：
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通讯作者：
Jones, David T.
中科院分区：
文献类型：
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作者：
Cozzetto, Domenico;Jones, David T.
对公共序列资源的调查表明，实验支持的功能信息仍然完全缺失了相当一部分已知蛋白质，并且显然是不完整的，甚至更大的部分。生物信息学方法长期以来一直单独或组合使用非常多样化的数据源来预测蛋白质功能，并理解不同的数据类型有助于阐明互补的生物学作用。本章重点介绍接受氨基酸序列作为输入和生产GO长期分配直接作为输出的方法;沿着个别方法的优点和局限性，提出了相关的生物和计算概念。
Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have long made use of very diverse data sources alone or in combination to predict protein function, with the understanding that different data types help elucidate complementary biological roles. This chapter focuses on methods accepting amino acid sequences as input and producing GO term assignments directly as outputs; the relevant biological and computational concepts are presented along with the advantages and limitations of individual approaches.