Sequence analysis Prediction of both conserved and nonconserved microRNA targets in animals
Sequence analysis Prediction of both conserved and nonconserved microRNA targets in animals
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发表时间：
2007
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通讯作者：
Xiaowei Wang;I. E. Naqa
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作者：
Xiaowei Wang;I. E. Naqa
动机：microRNAs(MiRNAs)参与许多不同的生物过程，它们可能潜在地调节数千个基因的功能。然而，miRNA研究中的一个主要问题是缺乏准确预测miRNA靶标的生物信息学程序。动物miRNAs与其基因靶标的序列互补性有限，这使得构建高特异性的靶标预测模型具有挑战性。结果：我们提出了一种新的基于支持向量机(SVMs)和大型微阵列训练数据集的miRNA靶标预测程序。通过系统地分析公共微阵列数据，我们已经确定了对靶向下调调控很重要的统计显著特征。为了训练我们的目标预测模型MirTarget2，在支持向量机机器学习框架中非线性地集成了异质预测特征。在人类预测的miRNA靶点中，大约有一半在其他生物中不保守。我们的预测算法已经用独立的实验数据进行了验证，它在预测大量miRNA下调的基因靶点时具有更好的性能。可获得性：所有预测目标都被导入到一个在线数据库miRDB中，该数据库可在http://mirdb.org.上免费访问联系方式：xwang@radonc.wustl.edu补充信息：补充数据可在BioInformation Online上获得。
Motivation: MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potentially regulate the functions of thousands of genes. However, one major issue in miRNA studies is the lack of bioinformatics programs to accurately predict miRNA targets. Animal miRNAs have limited sequence complementarity to their gene targets, which makes it challenging to build target prediction models with high specificity. Results: Here we present a new miRNA target prediction program based on support vector machines (SVMs) and a large microarray training dataset. By systematically analyzing public microarray data, we have identified statistically significant features that are important to target downregulation. Heterogeneous prediction features have been non-linearly integrated in an SVM machine learning framework for the training of our target prediction model, MirTarget2. About half of the predicted miRNA target sites in human are not conserved in other organisms. Our prediction algorithm has been validated with independent experimental data for its improved performance on predicting a large number of miRNA down-regulated gene targets. Availability: All the predicted targets were imported into an online database miRDB, which is freely accessible at http://mirdb.org. Contact: xwang@radonc.wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online.