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原文連結
論文資訊
- 類型：已發表論文
- 日期：2010
摘要
Motivation: Small nucleolar RNAs are an abundant class of non-coding RNAs that guide chemical modifications of rRNAs, snRNAs and some mRNAs. In the case of many 'orphan' snoRNAs, the targeted nucleotides remain unknown, however. The box H/ACA subclass determines uridine residues that are to be converted into pseudouridines via specific complementary binding in a well-defined secondary structure configuration that is outside the scope of common RNA (co-)folding algorithms. Results: RNAsnoop implements a dynamic programming algorithm that computes 熱力學ally optimal H/ACA-RNA interactions in an efficient scanning variant. Complemented by an support vector machine (SVM)-based 機器學習 approach to distinguish true binding sites from spurious solutions and a system to evaluate comparative 資訊, it prese
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