MMG4: Recognition of G4-Forming Sequences Based on Markov Model.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS Journal of Computational Biology Pub Date : 2024-10-17 DOI:10.1089/cmb.2024.0523
Boyuan Yu, Hao Zhang, Cong Pian, Yuanyuan Chen
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Abstract

G-quadruplexes (G4s) are special nucleic acid structures with various important biological functions. Existing tools and technologies for G4-forming sequences recognition are limited to time-consuming and costly methods such as circular dichroism and nuclear magnetic resonance. Developing a fast and accurate model for G4-forming sequences recognition has far-reaching significance. In this study, MMG4, a novel model to recognize G4-forming sequences based on Markov model (MM), was developed and the phenomenon of high recognition accuracy in the central region of the sequence and low accuracy in the two end regions was discovered. It was further found that the differences in base transfer probabilities, ratio distribution, and G4-motif structural content in different regions may be the causes of this phenomenon. The study also explored the impact of sequence length on recognition accuracy and found the optimal recognition interval to be [910-1049], with the highest recognition accuracy reaching 85.95%. By extracting sequence features, the study constructed three types of machine learning models: random forest (RF), support vector machine, and back-propagation neural network. It was found that recognition performance of MM was significantly better than that of the other three machine learning models, proving that the recognition method based on MM can effectively capture the correlation information between adjacent nucleotides of G4. By combining MM with the three machine learning models, the predictive performance of MMG4 improved. Among them, the RF model combined with MM has the best performance, achieving an area under the receiver operating characteristic curve value of 0.93 and an area under the precision-recall curve value of 0.9. Finally, the study validated the model robustness and generalization ability through independent testing dataset.

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MMG4:基于马尔可夫模型的 G4 形成序列识别。
G-quadruplexes (G4s) 是一种特殊的核酸结构,具有各种重要的生物学功能。现有的 G4 形成序列识别工具和技术仅限于耗时耗钱的圆二色性和核磁共振等方法。开发一种快速准确的 G4 形成序列识别模型具有深远的意义。本研究建立了基于马尔可夫模型(MM)的新型 G4 形成序列识别模型 MMG4,发现了序列中心区识别准确率高而两端区识别准确率低的现象。研究进一步发现,不同区域的碱基转移概率、比例分布和 G4-motif结构内容的差异可能是造成这一现象的原因。研究还探讨了序列长度对识别准确率的影响,发现最佳识别区间为[910-1049],最高识别准确率达到 85.95%。通过提取序列特征,研究构建了三种机器学习模型:随机森林(RF)、支持向量机和反向传播神经网络。结果发现,MM的识别性能明显优于其他三种机器学习模型,证明基于MM的识别方法能有效捕捉G4相邻核苷酸之间的相关信息。通过将 MM 与三种机器学习模型相结合,MMG4 的预测性能得到了提高。其中,结合 MM 的 RF 模型性能最佳,接收者工作特征曲线下面积值达到 0.93,精确度-召回曲线下面积值达到 0.9。最后,研究通过独立测试数据集验证了模型的鲁棒性和泛化能力。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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