AScirRNA: A novel computational approach to discover abiotic stress-responsive circular RNAs in plant genome

IF 2.6 4区 生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2024-09-06 DOI:10.1016/j.compbiolchem.2024.108205
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Abstract

In the realm of plant biology, understanding the intricate regulatory mechanisms governing stress responses stands as a pivotal pursuit. Circular RNAs (circRNAs), emerging as critical players in gene regulation, have garnered attention in recent days for their potential roles in abiotic stress adaptation. A comprehensive grasp of circRNAs' functions in stress response offers avenues for breeders to manipulating plants to develop abiotic stress resistant crop cultivars to thrive in challenging climates. This study pioneers a machine learning-based model for predicting abiotic stress-responsive circRNAs. The K-tuple nucleotide composition (KNC) and Pseudo KNC (PKNC) features were utilized to numerically represent circRNAs. Three different feature selection strategies were employed to select relevant and non-redundant features. Eight shallow and four deep learning algorithms were evaluated to build the final predictive model. Following five-fold cross-validation process, XGBoost learning algorithm demonstrated superior performance with LightGBM-chosen 260 KNC features (Accuracy: 74.55 %, auROC: 81.23 %, auPRC: 76.52 %) and 160 PKNC features (Accuracy: 74.32 %, auROC: 81.04 %, auPRC: 76.43 %), over other combinations of learning algorithms and feature selection techniques. Further, the robustness of the developed models were evaluated using an independent test dataset, where the overall accuracy, auROC and auPRC were found to be 73.13 %, 72.34 % and 72.68 % for KNC feature set and 73.52 %, 79.53 % and 73.09 % for PKNC feature set, respectively. This computational approach was also integrated into an online prediction tool, AScirRNA (https://iasri-sg.icar.gov.in/ascirna/) for easy prediction by the users. Both the proposed model and the developed tool are poised to augment ongoing efforts in identifying stress-responsive circRNAs in plants.

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AScirRNA:发现植物基因组中对非生物胁迫有反应的环状 RNA 的新型计算方法
在植物生物学领域,了解支配胁迫反应的复杂调控机制是一项关键的追求。环状 RNA(circRNA)作为基因调控的关键角色,因其在非生物胁迫适应中的潜在作用而在近期备受关注。全面掌握 circRNAs 在应激反应中的功能为育种者提供了一条途径,他们可以通过操纵植物来培育抗非生物应激的作物栽培品种,从而在充满挑战的气候条件下茁壮成长。本研究开创了一种基于机器学习的模型,用于预测非生物胁迫响应性 circRNA。该模型利用 K 元组核苷酸组成(KNC)和伪 KNC(PKNC)特征对 circRNA 进行数字表示。研究人员采用了三种不同的特征选择策略来选择相关的非冗余特征。对八种浅层学习算法和四种深度学习算法进行了评估,以建立最终的预测模型。经过五倍交叉验证过程,XGBoost 学习算法在使用 LightGBM 选择的 260 个 KNC 特征(准确率:74.55 %,auROC:81.23 %,auPRC:76.52 %)和 160 个 PKNC 特征(准确率:74.32 %,auROC:81.04 %,auPRC:76.43 %)时表现出优于其他学习算法和特征选择技术组合的性能。此外,还使用独立测试数据集对所开发模型的鲁棒性进行了评估,发现 KNC 特征集的总体准确率、auROC 和 auPRC 分别为 73.13 %、72.34 % 和 72.68 %,PKNC 特征集的总体准确率、auROC 和 auPRC 分别为 73.52 %、79.53 % 和 73.09 %。这种计算方法还被集成到在线预测工具 AScirRNA (https://iasri-sg.icar.gov.in/ascirna/) 中,方便用户进行预测。所提出的模型和所开发的工具都将为目前鉴定植物胁迫响应性 circRNA 的工作提供帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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