ABSTRACT

N⁶-methyladenosine (m⁶A) plays an important role in various biological processes. Identifying m⁶A site is a key step in exploring its biological functions. One of the biggest challenges in identifying m⁶A sites is how to extract features comprising rich categorical information to distinguish m⁶A and non-m⁶A sites. To address this challenge, we propose bidirectional dinucleotide and trinucleotide position-specific propensities, respectively, in this paper. Based on this, we propose two feature-encoding algorithms: Position-Specific Propensities and Pointwise Mutual Information (PSP-PMI) and Position-Specific Propensities and Pointwise Joint Mutual Information (PSP-PJMI). PSP-PMI is based on the bidirectional dinucleotide propensity and the pointwise mutual information, while PSP-PJMI is based on the bidirectional trinucleotide position-specific propensity and the proposed pointwise joint mutual information in this paper. We introduce parameters $\alpha$ and $\beta$ in PSP-PMI and PSP-PJMI, respectively, to represent the distance from the nucleotide to its forward or backward adjacent nucleotide or dinucleotide, so as to extract features containing local and global classification information. Finally, we propose the M6A-BiNP predictor based on PSP-PMI or PSP-PJMI and SVM classifier. The 10-fold cross-validation experimental results on the benchmark datasets of non-single-base resolution and single-base resolution demonstrate that PSP-PMI and PSP-PJMI can extract features with strong capabilities to identify m⁶A and non-m⁶A sites. The M6A-BiNP predictor based on our proposed feature encoding algorithm PSP-PJMI is better than the state-of-the-art predictors, and it is so far the best model to identify m⁶A and non-m⁶A sites.

摘要

N ⁶ -甲基腺苷（m ⁶ A）在各种生物过程中发挥着重要作用。识别 m ⁶ A 位点是探索其生物学功能的关键步骤。^{识别 m 6} A 站点的最大挑战之一是如何提取包含丰富分类信息的特征以区分 m ⁶ A 和非 m ⁶一个站点。为了应对这一挑战，我们在本文中分别提出了双向二核苷酸和三核苷酸位置特异性倾向。基于此，我们提出了两种特征编码算法：位置特定倾向和逐点互信息（PSP-PMI）和位置特定倾向和逐点联合互信息（PSP-PJMI）。PSP-PMI是基于双向二核苷酸倾向和逐点互信息，而PSP-PJMI是基于双向三核苷酸位置特异性倾向和本文提出的逐点联合互信息。我们介绍参数$\alpha$和$\beta$在 PSP-PMI 和 PSP-PJMI 中，分别表示核苷酸与其前向或后向相邻核苷酸或二核苷酸的距离，从而提取包含局部和全局分类信息的特征。最后，我们提出了基于 PSP-PMI 或 PSP-PJMI 和 SVM 分类器的 M6A-BiNP 预测器。在非单碱基分辨率和单碱基分辨率基准数据集上的 10 倍交叉验证实验结果表明，PSP-PMI 和 PSP-PJMI 可以提取具有较强识别 m ⁶ A 和非 m ⁶能力的特征一个站点。基于我们提出的特征编码算法 PSP-PJMI 的 M6A-BiNP 预测器优于最先进的预测器，是迄今为止识别 m ⁶ A 和非 m的最佳模型^6个网站。