DNA sequence and structure properties analysis reveals similarities and differences to promoters of stress responsive genes in Arabidopsis thaliana

IF 0.2 4区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Data Mining and Bioinformatics Pub Date : 2015-07-01 DOI:10.1504/IJDMB.2015.070832

P. Zhu, Yanhong Zhou, Libin Zhang, Chuang Ma

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引用次数: 0

Abstract

Understanding regulatory mechanisms of stress response in plants has important biological and agricultural significances. In this study, we firstly compiled a set of genes responsive to different stresses in Arabidopsis thaliana and then comparatively analysed their promoters at both the DNA sequence and three-dimensional structure levels. Amazingly, the comparison revealed that the profiles of several sequence and structure properties vary distinctly in different regions of promoters. Moreover, the content of nucleotide T and the profile of B-DNA twist are distinct in promoters from different stress groups, suggesting Arabidopsis genes might exploit different regulatory mechanisms in response to various stresses. Finally, we evaluated the performance of two representative promoter predictors including EP3 and PromPred. The evaluation results revealed their strengths and weakness for identifying stress-related promoters, providing valuable guidelines to accelerate the discovery of novel stress-related promoters and genes in plants.

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DNA序列和结构特性分析揭示了拟南芥逆境响应基因启动子的异同

了解植物胁迫反应的调控机制具有重要的生物学和农业意义。在本研究中，我们首先在拟南芥中编译了一组响应不同胁迫的基因，并在DNA序列和三维结构水平上对其启动子进行了比较分析。令人惊讶的是，比较结果显示，在启动子的不同区域中，一些序列和结构特性的分布有明显的差异。此外，不同胁迫组启动子中核苷酸T的含量和B-DNA扭曲谱存在差异，表明拟南芥基因可能利用不同的调控机制来应对不同的胁迫。最后，我们评估了两个具有代表性的启动子预测因子EP3和PromPred的性能。评价结果揭示了它们在鉴定胁迫相关启动子方面的优势和不足，为加快植物中新的胁迫相关启动子和基因的发现提供了有价值的指导。

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

International Journal of Data Mining and Bioinformatics 生物-数学与计算生物学

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.