Integrating DNA barcoding and machine learning for species identification: Comparative genomics and codon usage bias of chloroplasts in Gentiana sect. Cruciata

IF 4.8 3区生物学 Q1 PLANT SCIENCES

Journal of plant physiology Pub Date : 2026-04-01 Epub Date: 2026-02-28 DOI:10.1016/j.jplph.2026.154730

Mengdi Zheng , Mingchen Gao , Zeran Zhang , Xi Song

{"title":"Integrating DNA barcoding and machine learning for species identification: Comparative genomics and codon usage bias of chloroplasts in Gentiana sect. Cruciata","authors":"Mengdi Zheng , Mingchen Gao , Zeran Zhang , Xi Song","doi":"10.1016/j.jplph.2026.154730","DOIUrl":null,"url":null,"abstract":"<div><div>This study integrates chloroplast genome comparison, codon usage analysis, machine learning, and DNA barcoding to elucidate the phylogeny, genetic diversity, and species identification of <em>Gentiana</em> Sect. <em>Cruciata</em>. Perform chloroplast genome analysis using IRscope (boundary analysis), MISA (SSR detection), and mVISTA (variation alignment). Based on ChiPlot, CodonW, and CUSP analysis, factors influencing codon preference and usage patterns were studied. Molecular identification based on ITS2, <em>matK</em>, ITS, psbA-trnH barcode with BLOG, WEKA machine learning algorithms. Chloroplast SSRs dominated by A/T repeats; non-coding regions exhibited higher variability. Codon bias driven by natural selection, with A/U preference at the third position. ITS2 showed the highest discrimination power (<em>matK</em> > ITS > psbA-trnH). Machine learning (J48/SMO classifiers) achieved 83.33%–100% accuracy using four barcodes. This study provides theoretical foundations for conservation, medicinal quality control, and resource authentication of the <em>Gentiana</em> Sect. <em>Cruciata</em>.</div></div>","PeriodicalId":16808,"journal":{"name":"Journal of plant physiology","volume":"319 ","pages":"Article 154730"},"PeriodicalIF":4.8000,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of plant physiology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S017616172600043X","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/2/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PLANT SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

This study integrates chloroplast genome comparison, codon usage analysis, machine learning, and DNA barcoding to elucidate the phylogeny, genetic diversity, and species identification of Gentiana Sect. Cruciata. Perform chloroplast genome analysis using IRscope (boundary analysis), MISA (SSR detection), and mVISTA (variation alignment). Based on ChiPlot, CodonW, and CUSP analysis, factors influencing codon preference and usage patterns were studied. Molecular identification based on ITS2, matK, ITS, psbA-trnH barcode with BLOG, WEKA machine learning algorithms. Chloroplast SSRs dominated by A/T repeats; non-coding regions exhibited higher variability. Codon bias driven by natural selection, with A/U preference at the third position. ITS2 showed the highest discrimination power (matK > ITS > psbA-trnH). Machine learning (J48/SMO classifiers) achieved 83.33%–100% accuracy using four barcodes. This study provides theoretical foundations for conservation, medicinal quality control, and resource authentication of the Gentiana Sect. Cruciata.

查看原文本刊更多论文

整合DNA条形码和机器学习用于物种鉴定：龙胆科叶绿体的比较基因组学和密码子使用偏差。

本研究结合叶绿体基因组比较、密码子使用分析、机器学习和DNA条形码等方法，对十字形龙胆的系统发育、遗传多样性和物种鉴定进行了研究。使用IRscope（边界分析）、MISA （SSR检测）和mVISTA（变异比对）进行叶绿体基因组分析。基于ChiPlot、CodonW和CUSP分析，研究了影响密码子偏好和使用模式的因素。基于ITS2、matK、ITS、psbA-trnH条码的分子识别，结合BLOG、WEKA机器学习算法。叶绿体SSRs以A/T重复序列为主；非编码区表现出更高的变异性。密码子偏向受自然选择驱动，A/U偏好排在第三位。ITS2表现出最高的分辨能力（matK b> ITS b> psbA-trnH）。机器学习（J48/SMO分类器）使用四个条形码实现了83.33%-100%的准确率。本研究为龙胆药材的保存、药材质量控制和资源鉴定提供了理论依据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of plant physiology 生物-植物科学

CiteScore

7.20

自引率

4.70%

发文量

196

审稿时长

32 days

期刊介绍： The Journal of Plant Physiology is a broad-spectrum journal that welcomes high-quality submissions in all major areas of plant physiology, including plant biochemistry, functional biotechnology, computational and synthetic plant biology, growth and development, photosynthesis and respiration, transport and translocation, plant-microbe interactions, biotic and abiotic stress. Studies are welcome at all levels of integration ranging from molecules and cells to organisms and their environments and are expected to use state-of-the-art methodologies. Pure gene expression studies are not within the focus of our journal. To be considered for publication, papers must significantly contribute to the mechanistic understanding of physiological processes, and not be merely descriptive, or confirmatory of previous results. We encourage the submission of papers that explore the physiology of non-model as well as accepted model species and those that bridge basic and applied research. For instance, studies on agricultural plants that show new physiological mechanisms to improve agricultural efficiency are welcome. Studies performed under uncontrolled situations (e.g. field conditions) not providing mechanistic insight will not be considered for publication. The Journal of Plant Physiology publishes several types of articles: Original Research Articles, Reviews, Perspectives Articles, and Short Communications. Reviews and Perspectives will be solicited by the Editors; unsolicited reviews are also welcome but only from authors with a strong track record in the field of the review. Original research papers comprise the majority of published contributions.