Prediction of guide strand of microRNAs from its sequence and secondary structure.
Prediction of guide strand of microRNAs from its sequence and secondary structure.
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从其序列和二级结构中预测microRNA的指导链。
DOI：
10.1186/1471-2105-10-105
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发表时间：
2009-04-09
影响因子：
3
通讯作者：
Raghava GP
中科院分区：
文献类型：
--
作者：
Ahmed F;Ansari HR;Raghava GP
MicroRNAs(MiRNAs)是由RNaseIII酶DROSHA和DICER对长发夹状RNA转录本进行顺序处理而产生的，形成短暂的小RNA双链。双链的一条被结合到RNA诱导的沉默复合体(RISC)中，并沉默基因的表达，称为导链，或miRNA；而双链的另一链被降解，称为客体链，或miRNA*。预测miRNA的引导链对于更好地了解RNA干扰途径是很重要的。本文描述了为预测miRNAs的引导链而开发的支持向量机(SVM)模型。所有模型都在由329对miRNA和329对miRNA*组成的数据集上使用五重交叉验证技术进行训练和测试。首先，利用miRNA链的单核苷酸、二核苷酸和三核苷酸组成建立模型，模型的准确率最高，分别为0.588、0.638和0.596。其次，建立了单核苷酸、二核苷酸和三核苷酸的单核苷酸、二核苷酸和三核苷酸组合模型，其最高精度分别为0.553、0.641和0.602。第三，采用二值模式建立模型，获得了0.708的最高准确率。此外，将二级结构特征与二值模式相结合，准确率达到0.719。最后，通过组合各种特征来开发混合模型，获得了最高0.799的准确率，灵敏度为0.781，特异度为0.818。此外，在一个独立的数据集上测试了该模型的性能，该数据集达到了0.80的准确率。此外，我们还在miRNA和siRNA数据集上将我们的方法与各种siRNA设计方法的性能进行了比较。在这项研究中，首次发展了一种预测miRNA双链引导miRNA链的方法。本研究证明，可以根据miRNA前体的核苷酸序列和二级结构来区分引导链和客体链。该方法将有助于理解microRNA的加工过程，并可应用于RNA沉默技术，以提高生物学和临床研究的水平。基于本研究中描述的支持向量机模型开发了一个Web服务器。
MicroRNAs (miRNAs) are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC) and silences the gene expression is called guide strand, or miRNA; while the other strand of duplex is degraded and called the passenger strand, or miRNA*. Predicting the guide strand of miRNA is important for better understanding the RNA interference pathways. This paper describes support vector machine (SVM) models developed for predicting the guide strands of miRNAs. All models were trained and tested on a dataset consisting of 329 miRNA and 329 miRNA* pairs using five fold cross validation technique. Firstly, models were developed using mono-, di-, and tri-nucleotide composition of miRNA strands and achieved the highest accuracies of 0.588, 0.638 and 0.596 respectively. Secondly, models were developed using split nucleotide composition and achieved maximum accuracies of 0.553, 0.641 and 0.602 for mono-, di-, and tri-nucleotide respectively. Thirdly, models were developed using binary pattern and achieved the highest accuracy of 0.708. Furthermore, when integrating the secondary structure features with binary pattern, an accuracy of 0.719 was seen. Finally, hybrid models were developed by combining various features and achieved maximum accuracy of 0.799 with sensitivity 0.781 and specificity 0.818. Moreover, the performance of this model was tested on an independent dataset that achieved an accuracy of 0.80. In addition, we also compared the performance of our method with various siRNA-designing methods on miRNA and siRNA datasets. In this study, first time a method has been developed to predict guide miRNA strands, of miRNA duplex. This study demonstrates that guide and passenger strand of miRNA precursors can be distinguished using their nucleotide sequence and secondary structure. This method will be useful in understanding microRNA processing and can be implemented in RNA silencing technology to improve the biological and clinical research. A web server has been developed based on SVM models described in this study .
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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