STOCHASTIC CONTEXT-FREE GRAMMARS FOR TRANSFER-RNA MODELING
STOCHASTIC CONTEXT-FREE GRAMMARS FOR TRANSFER-RNA MODELING
复制标题
DOI：
10.1093/nar/22.23.5112
复制
发表时间：
1994-11-25
影响因子：
14.9
通讯作者：
HAUSSLER, D
中科院分区：
文献类型：
--
作者：
SAKAKIBARA, Y;BROWN, M;HAUSSLER, D
随机上下文无关语法（scfg）被应用于tRNA序列的折叠、对齐和建模问题。scfg捕获了序列的共同一级和二级结构，并推广了用于蛋白质和DNA相关工作的隐马尔可夫模型（hmm）。结果表明，该模型仅对来自两个tRNA亚家族（线粒体和细胞质）的20个tRNA序列进行训练后，就能从其他种类的类似长度的RNA序列中识别出一般tRNA，并能发现新的tRNA序列的二级结构，并能产生大组tRNA序列的多个比对。我们的研究结果表明，在一些线粒体trna中，D-和t -结构域的排列可能会得到改善，这些结构域不能适合典型的二级结构。
Stochastic context-free grammars (SCFGs) are applied to the problems of folding, aligning and modeling families of tRNA sequences. SCFGs capture the sequences' common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. Results show that after having been trained on as few as 20 tRNA sequences from only two tRNA subfamilies (mitochondrial and cytoplasmic), the model can discern general tRNA from similar-length RNA sequences of other kinds, can find secondary structure of new tRNA sequences, and can produce multiple alignments of large sets of tRNA sequences. Our results suggest potential improvements in the alignments of the D- and T-domains in some mitochdondrial tRNAs that cannot be fit into the canonical secondary structure.