TargetSpy: a supervised machine learning approach for microRNA target prediction.
TargetSpy: a supervised machine learning approach for microRNA target prediction.
复制标题
DOI：
10.1186/1471-2105-11-292
复制
发表时间：
2010-05-28
影响因子：
3
通讯作者：
Frishman D
中科院分区：
文献类型：
--
作者：
Sturm M;Hackenberg M;Langenberger D;Frishman D
事实上，目前所有可用的 microRNA 靶位点预测算法都需要存在与 microRNA 5' 端匹配的（保守）种子。然而，最近的研究表明，这一要求可能过于严格，导致大量的目标位点缺失。我们开发了 TargetSpy，这是一种新颖的计算方法，用于预测目标位点，无论是否存在种子匹配。它基于机器学习和自动特征选择，使用涵盖当前生物知识的广泛组成、结构和碱基配对特征。我们的模型不依赖于进化保守性，这允许检测物种特异性相互作用，并使 TargetSpy 适合分析不保守的基因组序列。为了能够对 TargetSpy 与其他方法进行公正的比较，我们将所有算法分为三组：I) 无种子匹配要求，II) 种子匹配要求，以及 III) 保守种子匹配要求。 II 类和 III 类的 TargetSpy 预测是通过适当的后过滤生成的。在揭示五种选定 microRNA 蛋白质生产倍数变化的人类数据集上，我们的方法在所有类别中都显示出卓越的性能。在果蝇中，我们的 II 类和 III 类预测不仅与其他算法相当，而且值得注意的是 I 类（无种子）预测的准确性稍差。我们估计，TargetSpy 可以预测 26 到 112 个功能性目标位点，而每个 microRNA 都没有种子匹配，而所有其他当前可用的算法都遗漏了这些功能性目标位点。只有少数算法可以在不要求种子匹配的情况下预测目标位点，而 TargetSpy 展示了该类别中预测准确性的显着提高。此外，当需要保存和存在种子匹配时，其性能可与最先进的算法相媲美。 TargetSpy 在小鼠身上进行了训练，在人类和果蝇身上表现良好，这表明它可能适用于广泛的物种。此外，我们已经证明，机器学习技术的应用与即将推出的深度测序数据相结合，可以产生强大的 microRNA 目标位点预测工具 http://www.targetspy.org。
Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org.
登录
查看更多内容
影响因子：
64.8
作者：
Baek, Daehyun;Villen, Judit;Shin, Chanseok;Camargo, Fernando D.;Gygi, Steven P.;Bartel, David P.
通讯作者：
Bartel, David P.
影响因子：
3
作者：
Gaidatzis D;van Nimwegen E;Hausser J;Zavolan M
通讯作者：
Zavolan M
影响因子：
3
作者：
Kim SK;Nam JW;Rhee JK;Lee WJ;Zhang BT
通讯作者：
Zhang BT
影响因子：
1.8
作者：
HOFACKER, IL;FONTANA, W;SCHUSTER, P
通讯作者：
SCHUSTER, P
影响因子：
14.9
作者：
Griffiths-Jones, S
通讯作者：
Griffiths-Jones, S