RNA SEQUENCE-ANALYSIS USING COVARIANCE-MODELS
RNA SEQUENCE-ANALYSIS USING COVARIANCE-MODELS
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DOI：
10.1093/nar/22.11.2079
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发表时间：
1994-06-11
影响因子：
14.9
通讯作者：
DURBIN, R
中科院分区：
文献类型：
--
作者：
EDDY, SR;DURBIN, R
我们描述了一个通用的方法来几个RNA序列分析问题，使用概率模型，灵活地描述了二级结构和一级序列的一致性的RNA序列家族。我们称这些模型为“协方差模型”。tRNA序列的协方差模型是在序列数据库中搜索额外的tRNA和tRNA相关序列的一种非常敏感和有区别的工具。可以从现有的序列比对自动构建模型。我们还描述了一种算法，用于学习一个模型，因此从最初未对齐的示例序列和没有先验结构信息的共识二级结构。在未对齐的tRNA样本上训练的模型可以正确预测tRNA的构象结构，并产生高质量的多重比对。该方法可以应用于任何家族的小RNA序列。
We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models 'covariance models'. A covariance model of tRNA sequences is an extremely sensitive and discriminative tool for searching for additional tRNAs and tRNA-related sequences in sequence databases. A model can be built automatically from an existing sequence alignment. We also describe an algorithm for learning a model and hence a consensus secondary structure from initially unaligned example sequences and no prior structural information. Models trained on unaligned tRNA examples correctly predict tRNA scondary structure and produce high-quality multiple alignments. The approach may be applied to any family of small RNA sequences.