Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine.
Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine.
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DOI：
10.1186/1471-2105-6-310
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发表时间：
2005-12-29
影响因子：
3
通讯作者：
Zhang X
中科院分区：
文献类型：
--
作者：
Xue C;Li F;He T;Liu GP;Li Y;Zhang X
MicroRNAs(MiRNAs)是一组短小(~22nT)的非编码RNA，发挥着重要的调节作用。MiRNA前体(前miRNAs)以其发夹结构为特征。然而，大量相似的发夹可以在许多基因组中折叠。目前几乎所有用于miRNAs计算预测的方法都使用比较基因组学方法来从候选发夹中识别假定的前miRNAs。目前尚缺乏区分前miRNA和具有前miRNA发夹结构的序列片段的从头算方法。能够区分真实和假的前miRNAs对于理解miRNAs的性质和开发从头预测方法都是重要的，这些方法可以发现新的没有已知同源性的miRNAs。提出了一组新的局部连续结构-序列特征来区分真实的前miRNAs和伪前miRNAs的发夹。利用支持向量机对真实miRNAs和伪miRNAs进行分类，对人类数据的分类准确率达到90%左右。值得注意的是，基于人类数据构建的支持向量机分类器可以正确识别高达90%的前miRNAs来自其他物种，包括植物和病毒，而不需要利用任何比较基因组学信息。MiRNAs的局部结构-序列特征反映了miRNAs的区分性和保守性，而对真实和假性前miRNAs的成功从头算分类为发现新的miRNAs开辟了一条新的途径。
MicroRNAs (miRNAs) are a group of short (~22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology. A set of novel features of local contiguous structure-sequence information is proposed for distinguishing the hairpins of real pre-miRNAs and pseudo pre-miRNAs. Support vector machine (SVM) is applied on these features to classify real vs. pseudo pre-miRNAs, achieving about 90% accuracy on human data. Remarkably, the SVM classifier built on human data can correctly identify up to 90% of the pre-miRNAs from other species, including plants and virus, without utilizing any comparative genomics information. The local structure-sequence features reflect discriminative and conserved characteristics of miRNAs, and the successful ab initio classification of real and pseudo pre-miRNAs opens a new approach for discovering new miRNAs.
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