A scoring matrix approach to detecting miRNA target sites.

A scoring matrix approach to detecting miRNA target sites.
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DOI:
10.1186/1748-7188-3-3
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发表时间:
2008-03-31
期刊:
Algorithms for molecular biology : AMB
影响因子:
--
通讯作者:
Kim JT
Kim JT
中科院分区:
其他
文献类型:
--
作者:
Moxon S;Moulton V;Kim JT

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microRNA(miRNA)靶标的实验鉴定是一个困难且耗时的过程。因此,已经设计了几种计算预测方法,以预测后续实验验证的目标。目前的计算靶标预测方法仅使用miRNA序列作为输入。随着越来越多的实验验证的目标变得可用,利用这些额外的信息在搜索进一步的目标可能有助于提高计算方法的特异性,为目标位点预测。我们介绍了一种通用的目标预测方法,堆叠结合矩阵(SBM),使用有关的miRNA以及实验验证的目标序列在搜索候选目标序列的信息。我们证明了我们的方法的实用性,将其应用到动物和植物的数据集,并将其与米兰达,一种常用的目标预测方法进行比较。我们表明,SBM可以应用于在植物和动物的目标预测,并在灵敏度和特异性方面表现良好。实现SBM方法的开源代码以及文档和示例可从可用性和要求部分的地址免费下载。
Experimental identification of microRNA (miRNA) targets is a difficult and time consuming process. As a consequence several computational prediction methods have been devised in order to predict targets for follow up experimental validation. Current computational target prediction methods use only the miRNA sequence as input. With an increasing number of experimentally validated targets becoming available, utilising this additional information in the search for further targets may help to improve the specificity of computational methods for target site prediction. We introduce a generic target prediction method, the Stacking Binding Matrix (SBM) that uses both information about the miRNA as well as experimentally validated target sequences in the search for candidate target sequences. We demonstrate the utility of our method by applying it to both animal and plant data sets and compare it with miRanda, a commonly used target prediction method. We show that SBM can be applied to target prediction in both plants and animals and performs well in terms of sensitivity and specificity. Open source code implementing the SBM method, together with documentation and examples are freely available for download from the address in the Availability and Requirements section.
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