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非编码RNA的类别特异性预测。

Class-specific prediction of ncRNAs.

作者信息

Stadler Peter F

机构信息

Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107, Leipzig, Germany.

出版信息

Methods Mol Biol. 2014;1097:199-213. doi: 10.1007/978-1-62703-709-9_10.

Abstract

Many RNA families, i.e., groups of homologous RNA genes, belong to RNA classes, such as tRNAs, snoRNAs, or microRNAs, that are characterized by common sequence motifs and/or common secondary structure features. The detection of new members of RNA classes, as well as the comprehensive annotation of genomes with members of RNA classes is a challenging task that goes beyond simple homology search. Computational methods addressing this problem typically use a three-tiered approach: In the first step an efficient and sensitive filter is employed. In the second step the candidate set is narrowed down using computationally expensive methods geared towards specificity. In the final step the hits are annotated with class-specific features and scored. Here we review the tools that are currently available for a diverse set of RNA classes.

摘要

许多RNA家族,即同源RNA基因的群体,属于RNA类别,如tRNA、snoRNA或微小RNA,它们以共同的序列基序和/或共同的二级结构特征为特征。检测RNA类别的新成员,以及用RNA类别的成员对基因组进行全面注释,是一项具有挑战性的任务,超出了简单的同源性搜索范围。解决这个问题的计算方法通常采用三层方法:第一步,使用高效且灵敏的过滤器。第二步,使用针对特异性的计算成本高昂的方法缩小候选集。在最后一步,用类别特异性特征对命中结果进行注释并评分。在这里,我们综述了目前可用于多种RNA类别的工具。

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