TarPmiR: a new approach for microRNA target site prediction.
TarPmiR: a new approach for microRNA target site prediction.
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DOI：
10.1093/bioinformatics/btw318
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发表时间：
2016-09-15
期刊：
影响因子：
--
通讯作者：
Hu H
中科院分区：
文献类型：
--
作者：
Ding J;Li X;Hu H
研究动机：microRNA （miRNA）靶点的鉴定对于研究基因调控具有重要意义。miRNA靶位预测的计算方法有几十种。尽管它们存在，但我们仍然无法可靠地识别miRNA靶点，部分原因是我们对miRNA靶点特征的了解有限。最近发表的CLASH（交联结扎和杂交体测序）数据为研究miRNA靶点特征和改进miRNA靶点预测方法提供了前所未有的机会。结果：将四种不同的机器学习方法应用于CLASH数据，我们确定了miRNA靶点的七个新特征。将这些新特征与现有miRNA靶点预测算法常用的特征相结合，我们开发了一种称为TarPmiR的miRNA靶点预测方法。在两个人类和一个小鼠非clash数据集上进行测试，我们发现TarPmiR在每个数据集中预测了超过74.2%的真实miRNA靶位。与现有的三种方法相比，我们证明了TarPmiR在查全率和查准率方面优于这些现有方法。可用性和实现：TarPmiR软件可以在http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/上免费获得。联系方式：haihu@cs.ucf.edu或xiaoman@mail.ucf.edu补充信息：补充数据可在Bioinformatics在线获取。
Motivation: The identification of microRNA (miRNA) target sites is fundamentally important for studying gene regulation. There are dozens of computational methods available for miRNA target site prediction. Despite their existence, we still cannot reliably identify miRNA target sites, partially due to our limited understanding of the characteristics of miRNA target sites. The recently published CLASH (crosslinking ligation and sequencing of hybrids) data provide an unprecedented opportunity to study the characteristics of miRNA target sites and improve miRNA target site prediction methods. Results: Applying four different machine learning approaches to the CLASH data, we identified seven new features of miRNA target sites. Combining these new features with those commonly used by existing miRNA target prediction algorithms, we developed an approach called TarPmiR for miRNA target site prediction. Testing on two human and one mouse non-CLASH datasets, we showed that TarPmiR predicted more than 74.2% of true miRNA target sites in each dataset. Compared with three existing approaches, we demonstrated that TarPmiR is superior to these existing approaches in terms of better recall and better precision. Availability and Implementation: The TarPmiR software is freely available at http://hulab.ucf.edu/research/projects/miRNA/TarPmiR/. Contacts: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
4.4
作者：
Chou CH;Lin FM;Chou MT;Hsu SD;Chang TH;Weng SL;Shrestha S;Hsiao CC;Hung JH;Huang HD
通讯作者：
Huang HD
DOI：
10.1093/bioinformatics/btu833
发表时间：
2015-05-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Ding J;Li X;Hu H
通讯作者：
Hu H
影响因子：
64.5
作者：
Hafner M;Landthaler M;Burger L;Khorshid M;Hausser J;Berninger P;Rothballer A;Ascano M Jr;Jungkamp AC;Munschauer M;Ulrich A;Wardle GS;Dewell S;Zavolan M;Tuschl T
通讯作者：
Tuschl T
影响因子：
64.5
作者：
Lewis, BP;Shih, IH;Burge, CB
通讯作者：
Burge, CB
影响因子：
64.5
作者：
Helwak A;Kudla G;Dudnakova T;Tollervey D
通讯作者：
Tollervey D