An accessibility-incorporated method for accurate prediction of RNA-RNA interactions from sequence data
An accessibility-incorporated method for accurate prediction of RNA-RNA interactions from sequence data
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DOI：
10.1093/bioinformatics/btw603
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发表时间：
2017-01-15
期刊：
影响因子：
5.8
通讯作者：
Akutsu, Tatsuya
中科院分区：
文献类型：
--
作者：
Kato, Yuki;Mori, Tomoya;Akutsu, Tatsuya
动机：RNA-RNA 通过碱基配对的相互作用在基因表达的转录后调控中发挥着至关重要的作用。有效识别此类调节RNA的靶标不仅需要对正负RNA-RNA相互作用序列数据具有区分能力，还需要根据正数据准确预测相互作用位点。最近，一些研究已将交互站点可访问性纳入其预测方法中，这表明有限积极数据的预测性能得到了增强。结果：在这里，我们展示了基于可访问性的预测模型 RactIPAce 在新编译的数据集上的有效性。相互作用位点预测的第一个实验表明，与最先进的方法相比，RactIPAce 在文献中经过实验验证的相互作用的新编译数据集上实现了最佳预测性能。此外，第二个区分正相互作用和负相互作用对的实验表明，基于可及性的方法（包括我们的方法）的组合可以有效地识别真正的相互作用RNA。考虑到这些，我们的预测模型可以在筛选真正的相互作用RNA后有效地预测相互作用位点，这将促进调节RNA的功能分析。
Motivation: RNA-RNA interactions via base pairing play a vital role in the post-transcriptional regulation of gene expression. Efficient identification of targets for such regulatory RNAs needs not only discriminative power for positive and negative RNA-RNA interacting sequence data but also accurate prediction of interaction sites from positive data. Recently, a few studies have incorporated interaction site accessibility into their prediction methods, indicating the enhancement of predictive performance on limited positive data.Results: Here we show the efficacy of our accessibility-based prediction model RactIPAce on newly compiled datasets. The first experiment in interaction site prediction shows that RactIPAce achieves the best predictive performance on the newly compiled dataset of experimentally verified interactions in the literature as compared with the state-of-the-artmethods. In addition, the second experiment in discrimination between positive and negative interacting pairs reveals that the combination of accessibility-based methods including our approach can be effective to discern real interacting RNAs. Taking these into account, our prediction model can be effective to predict interaction sites after screening for real interacting RNAs, which will boost the functional analysis of regulatory RNAs.