PseUI: Pseudouridine sites identification based on RNA sequence information.
PseUI: Pseudouridine sites identification based on RNA sequence information.
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PseUI：基于RNA序列信息的伪尿苷位点识别
DOI：
10.1186/s12859-018-2321-0
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发表时间：
2018-08-29
影响因子：
3
通讯作者：
Xiong Y
中科院分区：
文献类型：
--
作者：
He J;Fang T;Zhang Z;Huang B;Zhu X;Xiong Y
假尿苷酸化是所有生物体中各种稳定RNA中最普遍的转录后修饰类型，其显著影响许多受RNA调控的细胞过程。因此，准确鉴定RNA中的假尿苷（pseudouridine，PURPOSE）位点对于理解这些细胞过程将是非常有益的。由于目前可用的实验方法的低效率和高成本，因此非常期望开发用于准确和有效地检测RNA序列中的双位点的计算方法。然而，现有的计算方法的预测精度并不令人满意，仍然需要改进。在这项研究中，我们开发了一个新的模型，PseUI，用于三个物种的识别，这是H。sapiens，S. cerevisiae和M.肌肉首先，基于RNA片段生成五种不同的特征，包括核苷酸组成（NC）、二核苷酸组成（DC）、伪二核苷酸组成（pseDNC）、位置特异性核苷酸倾向（PSNP）和位置特异性二核苷酸倾向（PSDP）。然后，使用顺序向前特征选择策略，以获得一个有效的特征子集，具有紧凑的表示，但歧视性的预测能力。基于所选择的特征子集，我们建立了我们的模型，使用支持向量机（SVM）。最后，我们的模型的推广进行了验证，在基准数据集上的刀切测试和独立的验证测试。实验结果表明，该模型比以往发表的模型更准确和稳定。我们还为我们的模型提供了一个用户友好的Web服务器http：//zhulab.ahu.edu.cn/PseUI，并提供了一个简短的说明，对Web服务器。使用该指令，学术用户可以方便地得到他们想要的结果，而无需复杂的计算。在这项研究中，我们提出了一个新的预测，PseUI，检测RNA序列中的重复位点。结果表明，我们的模型优于现有的最先进的模型。预计我们的模型，PseUI，将成为一个有用的工具，用于准确识别的RNA酶切位点。本文的在线版本（10.1186/s12859-018-2321-0）包含补充材料，可供授权用户使用。
Pseudouridylation is the most prevalent type of posttranscriptional modification in various stable RNAs of all organisms, which significantly affects many cellular processes that are regulated by RNA. Thus, accurate identification of pseudouridine (Ψ) sites in RNA will be of great benefit for understanding these cellular processes. Due to the low efficiency and high cost of current available experimental methods, it is highly desirable to develop computational methods for accurately and efficiently detecting Ψ sites in RNA sequences. However, the predictive accuracy of existing computational methods is not satisfactory and still needs improvement. In this study, we developed a new model, PseUI, for Ψ sites identification in three species, which are H. sapiens, S. cerevisiae, and M. musculus. Firstly, five different kinds of features including nucleotide composition (NC), dinucleotide composition (DC), pseudo dinucleotide composition (pseDNC), position-specific nucleotide propensity (PSNP), and position-specific dinucleotide propensity (PSDP) were generated based on RNA segments. Then, a sequential forward feature selection strategy was used to gain an effective feature subset with a compact representation but discriminative prediction power. Based on the selected feature subsets, we built our model by using a support vector machine (SVM). Finally, the generalization of our model was validated by both the jackknife test and independent validation tests on the benchmark datasets. The experimental results showed that our model is more accurate and stable than the previously published models. We have also provided a user-friendly web server for our model at http://zhulab.ahu.edu.cn/PseUI, and a brief instruction for the web server is provided in this paper. By using this instruction, the academic users can conveniently get their desired results without complicated calculations. In this study, we proposed a new predictor, PseUI, to detect Ψ sites in RNA sequences. It is shown that our model outperformed the existing state-of-art models. It is expected that our model, PseUI, will become a useful tool for accurate identification of RNA Ψ sites. The online version of this article (10.1186/s12859-018-2321-0) contains supplementary material, which is available to authorized users.
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