RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.
RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.
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RPiRLS：RNA 与任何已知序列蛋白质相互作用的定量预测
DOI：
10.3390/molecules23030540
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发表时间：
2018-02-28
期刊：
影响因子：
--
通讯作者：
Xu J
中科院分区：
文献类型：
--
作者：
Shen WJ;Cui W;Chen D;Zhang J;Xu J
RNA-蛋白质相互作用(RPI)在转录后基因调控、病毒组装、细胞防御和蛋白质合成等许多基本生物学过程中发挥着重要作用。由于高通量测序方法的出现，现有的RNA-蛋白质结合实验数据迅速增加，现在通过计算方法来测量和理解RNA-蛋白质相互作用成为可能。在本研究中，我们将基于序列的派生核与正则化最小二乘相结合来进行预测。派生的核利用了氨基酸或核酸周围的上下文信息以及重复的保守基序信息。我们提出了一种新的机器学习方法，称为RPiRLS，用于预测已知序列中任何RNA和蛋白质之间的相互作用。对于RPiRLS分类器，每个蛋白质序列包括多达20个不同的氨基酸，但对于RPiRLS-7G分类器，每个蛋白质序列根据其理化性质使用7个字母的简化字母来表示。我们在大量的基准数据集上对这两种方法进行了评估，并将它们的性能与两种新开发的最先进的方法RPI-PRED和IPMiner进行了比较。在从PRIDB提取的非冗余基准测试集上，RPiRLS方法在准确性、特异性和敏感度方面优于RPI-Pred和IPMiner。此外，RPiRLS对lncRNA-蛋白质相互作用的预测准确率达到92%。该方法还可以扩展到构建RNA-蛋白质相互作用网络。RPiRLS Web服务器可以在http://bmc.med.stu.edu.cn/RPiRLS.上免费获得
RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at http://bmc.med.stu.edu.cn/RPiRLS.
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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：
2.5
作者：
GOLUB, GH;HEATH, M;WAHBA, G
通讯作者：
WAHBA, G
影响因子：
2.9
作者：
Ellis, Jonathan J.;Broom, Mark;Jones, Susan
通讯作者：
Jones, Susan
DOI：
10.1038/nrg2673
发表时间：
2010-01
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
通讯作者：
--
影响因子：
4.5
作者：
Han, LY;Cai, CZ;Chen, YZ
通讯作者：
Chen, YZ