利用CLASH数据改进微小RNA靶标预测
Improving miRNA Target Prediction Using CLASH Data.
作者信息
Li Xiaoman, Hu Haiyan
机构信息
Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL, USA.
Department of Computer Science, University of Central Florida, Orlando, FL, USA.
出版信息
Methods Mol Biol. 2019;1970:75-83. doi: 10.1007/978-1-4939-9207-2_6.
In this chapter, we present a computational method, TarPmiR, for miRNA target prediction. TarPmiR is based on emerging features of miRNA-target interactions learned from CLASH (crosslinking, ligation and sequencing of hybrids) data. First, we introduce miRNA target prediction, delineate existing methods for miRNA target prediction, and discuss their usage and limitations. Next, we describe available CLASH data, the learning of new miRNA binding features from CLASH data, and the usage of CLASH features in miRNA target prediction. Finally, we detail the computational pipeline of TarPmiR, discuss its performance compared with existing computational methods for miRNA target prediction, and present its installation and usage for miRNA target prediction. This chapter will facilitate the common understanding of CLASH data, new characteristics of miRNA-target interactions, and the use of the CLASH based miRNA target prediction tool TarPmiR.
在本章中，我们介绍一种用于miRNA靶标预测的计算方法TarPmiR。TarPmiR基于从CLASH（杂交体交联、连接和测序）数据中了解到的miRNA-靶标相互作用的新特征。首先，我们介绍miRNA靶标预测，阐述现有的miRNA靶标预测方法，并讨论它们的用途和局限性。接下来，我们描述可用的CLASH数据、从CLASH数据中学习新的miRNA结合特征，以及CLASH特征在miRNA靶标预测中的应用。最后，我们详细介绍TarPmiR的计算流程，讨论其与现有的miRNA靶标预测计算方法相比的性能，并介绍其用于miRNA靶标预测的安装和使用方法。本章将有助于人们对CLASH数据、miRNA-靶标相互作用的新特征以及基于CLASH的miRNA靶标预测工具TarPmiR的使用形成共同的理解。
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