Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors.
Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors.
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DOI：
10.1016/j.csbj.2020.10.032
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发表时间：
2020
影响因子：
6
通讯作者：
Dehzangi A
中科院分区：
文献类型：
--
作者：
Ahmed S;Hossain Z;Uddin M;Taherzadeh G;Sharma A;Shatabda S;Dehzangi A
RNA修饰是产生新RNA结构的重要步骤。这种修饰可能能够修饰RNA功能或其稳定性。在各种修饰中，5-羟甲基胞嘧啶（5 hmC）修饰的RNA表现出一系列生物学过程的重要潜力。了解5 hmC在RNA中的分布对于确定其生物学功能至关重要。虽然传统的测序技术可以广泛识别5 hmC，但它们既耗时又资源密集。在这项研究中，我们提出了一种新的计算工具，称为iRNA 5 hmC-PS来解决这个问题。为了构建iRNA 5 hmC-PS，我们提取了一组新的基于序列的特征，称为位置特异性缺口k-mer（PSG k-mer），以获得最大的序列信息。我们的特征分析表明，我们提出的PSG k-mer功能包含重要的信息，用于识别5 hmC网站。我们还使用了一个组明智的特征重要性计算策略，选择一个小的子集包含最大的判别信息的功能。我们的实验结果表明，iRNA 5 hmC-PS能够显着提高预测性能。iRNA 5 hmC-PS的预测性能达到78.3%，比以往研究中报道的预测性能提高了12.8%。iRNA 5 hmC-PS作为在线工具可在http://103.109.52.8:81/iRNA5hmC-PS上公开获得。其基准数据集、源代码和文档可在https://github.com/zahid6454/iRNA5hmC-PS上获得。
RNA modification is an essential step towards generation of new RNA structures. Such modification is potentially able to modify RNA function or its stability. Among different modifications, 5-Hydroxymethylcytosine (5hmC) modification of RNA exhibit significant potential for a series of biological processes. Understanding the distribution of 5hmC in RNA is essential to determine its biological functionality. Although conventional sequencing techniques allow broad identification of 5hmC, they are both time-consuming and resource-intensive. In this study, we propose a new computational tool called iRNA5hmC-PS to tackle this problem. To build iRNA5hmC-PS we extract a set of novel sequence-based features called Position-Specific Gapped k-mer (PSG k-mer) to obtain maximum sequential information. Our feature analysis shows that our proposed PSG k-mer features contain vital information for the identification of 5hmC sites. We also use a group-wise feature importance calculation strategy to select a small subset of features containing maximum discriminative information. Our experimental results demonstrate that iRNA5hmC-PS is able to enhance the prediction performance, dramatically. iRNA5hmC-PS achieves 78.3% prediction performance, which is 12.8% better than those reported in the previous studies. iRNA5hmC-PS is publicly available as an online tool at http://103.109.52.8:81/iRNA5hmC-PS. Its benchmark dataset, source codes, and documentation are available at https://github.com/zahid6454/iRNA5hmC-PS.
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