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薯蓣属叶绿体基因组特征、密码子偏好性与机器学习分子鉴别研究

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中草药 | 药材与资源 2026,57(2): 640-651

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中草药 |药材与资源 2026 , 57 (2) : 640 -651

薯蓣属叶绿体基因组特征、密码子偏好性与机器学习分子鉴别研究

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郑梦迪¹, 徐笑雨¹, 黄美琳², 高明晨¹, 赵阳¹, 宋茜¹

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作者简介:

郑梦迪: 郑梦迪（1988—），女，博士，副教授，主要从事生物资源鉴定与评价方向研究。E-mail: 670677492@qq.com

Research on chloroplast genomic characteristics, codon usage bias, and machine learning for molecular identification in Dioscorea

ZHANG Mengdi, XU Xiaoyu, HUANG Meilin, GAO Mingchen, ZHAO Yang, SONG Xi

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doi: 10.7501/j.issn.0253-2670.2026.02.023

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摘要

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目的结合叶绿体基因组、密码子偏好性与DNA条形码、机器学习技术，为薯蓣属Dioscorea系统发育、遗传多样性及物种鉴定提供理论指导。方法通过IRscope比对11种植物的叶绿体基因结构，并构建系统发育树；用CodonW、CUSP等工具分析密码子偏好性；收集薯蓣材料并扩增测序DNA条形码序列，基于成熟酶K基因（maturase K gene，matK）、光系统II蛋白D1基因—组氨酸tRNA基因间隔区（photosystem b a protein gene-transfer RNA-histidine intergenic spacer，psbA-trnH）、核酮糖-1,5-二磷酸羧化酶/加氧酶大亚基基因（ribulose-bisphosphate carboxylase/oxygenase large subunit gene，rbcL）条形码和机器学习算法进行分子鉴别。结果薯蓣属叶绿体基因组保守稳定，密码子显著A/T偏好，第3位倾向A/U结尾。自然选择是影响密码子偏差的主因，共有最优密码子为GGA和UCA。3种条形码均能成功鉴别物种，基于BLOG算法的单一条形码鉴别成功率100%，WEKA的SMO和NaïveBayes分类器鉴别率较高。结论薯蓣叶绿体基因组保守，密码子偏好模式以自然选择为主，为薯蓣属基因表达调控、物种鉴定、资源保护等提供依据和指导。

关键词

薯蓣属 / 叶绿体全基因组 / 密码子偏好性 / DNA条形码 / 机器学习 / 分子鉴别

Abstract

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Objective To provide theoretical guidance for the phylogenetics, genetic diversity, and species identification of Dioscorea by integrating chloroplast genomics, codon usage bias, DNA barcoding, and machine learning technologies. Methods The chloroplast gene structures of 11 plant species were compared via IRscope and a phylogenetic tree was constructed. Codon usage bias was analyzed using tools such as CodonW and CUSP. Dioscorea materials were collected, and DNA barcode sequences were amplified and sequenced. Molecular identification was performed based on the maturase K gene (matK), photosystem b a protein gene-transfer RNA-Histidine intergenic spacer (psbA-trnH), and ribulose-bisphosphate carboxylase/oxygenase large subunit gene (rbcL) barcodes using machine learning algorithms. Results The chloroplast genomes of Dioscorea were found to be conserved and stable. Codon usage showed a significant A/T bias, with the third codon position favoring A/U endings. Natural selection was the primary factor influencing codon bias, and the common optimal codons identified were GGA and UCA. All three barcodes successfully discriminated species. Single-barcode identification using the BLOG algorithm achieved 100% success rate, while the SMO and NaïveBayes classifiers in WEKA demonstrated high identification accuracy. Conclusion The chloroplast genome of Dioscorea is conserved, and natural selection dominates its codon usage pattern. This study provides a basis and guidance for research on gene expression regulation, species identification, and resource conservation of Dioscorea.

Key words

Dioscorea L. / chloroplast genome / codon usage bias / DNA barcoding / machine learning / molecular identification

引用本文

郑梦迪, 徐笑雨, 黄美琳, 高明晨, 赵阳, 宋茜. 薯蓣属叶绿体基因组特征、密码子偏好性与机器学习分子鉴别研究. 中草药, 2026 , 57 (2) : 640 -651 . DOI: 10.7501/j.issn.0253-2670.2026.02.023

ZHANG Mengdi, XU Xiaoyu, HUANG Meilin, GAO Mingchen, ZHAO Yang, SONG Xi. Research on chloroplast genomic characteristics, codon usage bias, and machine learning for molecular identification in Dioscorea[J]. Chinese Traditional and Herbal Drugs, 2026 , 57 (2) : 640 -651 . DOI: 10.7501/j.issn.0253-2670.2026.02.023

基金

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陕西省自然科学基础研究计划项目 (2021JQ-782); 西安医学院2024年度科研能力提升计划项目 (2024NLTS122); 西安医学院2022年度科研能力提升计划项目 (2022NLTS084)

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文献

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参考文献引证文献

排序方式：

朱绍杰, 王宇飞, 于丹, 等. 分子生物学技术在薯蓣属植物中的应用研究进展[J]. 中草药, 2024, 55(20):7192-7202.
郭璞注. 山海经[M]. 上海:上海古籍出版社, 1989.
吴其潜. 植物名实图考[M]. 上海:商务印刷馆, 1957:24.
谢肇淛. 五杂俎[M]. 北京:京华出版社, 1989:200.
郑玉红, 夏冰, 杭悦宇, 等. 山药原植物薯蓣及其近缘种的分子鉴别和亲缘关系研究[J]. 南京农业大学学报, 2007, 30(2):55-59.
李伟, 朱明玉, 曾宇灵, 等. 5种黄精属植物叶绿体基因组从头组装及系统发育研究[J]. 世界科学技术-中医药现代化, 2025, 27(1):36-55.
马双姣, 周建国, 李滢, 等. 薯蓣和叉蕊薯蓣叶绿体基因组及特异性DNA条形码鉴定序列筛选研究[J]. 中国科学:生命科学, 2018, 48(5):571-582.
荣正普, 王杰敏, 裴林, 等. 黄芩属药用植物叶绿体基因组密码子使用偏好性分析[J]. 中草药, 2025, 56(1):269-281.
Mahima K, Sunil Kumar K N, Rakhesh K V, et al. Advancements and future prospective of DNA barcodes in the herbal drug industry[J]. Front Pharmacol, 2022, 13:947512.
何拓. 基于机器学习的黄檀属与紫檀属木材识别方法研究[D]. 北京:中国林业科学研究院, 2019.
冯婷婷. 基于机器学习的三种药用植物DNA条形码分子鉴别研究[D]. 广州:广东药科大学, 2021.
魏怡冰. 黔产薯蓣属药用植物资源调查、鉴别及亲缘关系研究[D]. 贵阳:贵阳中医学院, 2018.
Zheng M D, Zhang C, Ma R L, et al. Identification of Moslae Herba and its adulterants based on matK, ITS2 and its secondary structure[J]. Chin J Mod Appl Pharm, 2022, 39(17):2222-2228.
梁莹莹, 吴文如, 彭晓祺, 等. 苹婆叶绿体全基因组序列特征及苹婆属系统发育分析[J]. 中草药, 2024, 55(19):6721-6729.
李俊霖, 郭淑红, 张强, 等. 蒙古黄芪叶绿体全基因组特征解析及亲缘性分析[J]. 中草药, 2024, 55(7):2366-2374.
杜雨, 李效雄, 贾西贝, 等. 枸杞叶绿体基因组密码子偏好性分析[J]. 中草药, 2024, 55(4):1316-1325.
Zhang J S, Feng M. Analysis of the Codon usage bias pattern in the chloroplast genomes of Chloranthus species (Chloranthaceae)[J]. Genes, 2025, 16(2):186.
徐荣, 宋文政, 乔振升, 等. 富民枳叶绿体基因组密码子偏好性分析[J]. 中草药, 2024, 55(20):7093-7100.
Shen L W, Chen S Q, Liang M, et al. Comparative analysis of Codon usage bias in chloroplast genomes of ten medicinal species of Rutaceae[J]. BMC Plant Biol, 2024, 24(1):424.
Yang Y, Liu X Y, He L X, et al. Comparative chloroplast genomics and Codon usage bias analysis in Hevea genus[J]. Genes, 2025, 16(2):201.
Zhao Z Y, Wang X, Yu Y, et al. Complete chloroplast genome sequences of Dioscorea:Characterization, genomic resources, and phylogenetic analyses[J]. PeerJ, 2018, 6:e6032.
Chen J H, Hao Z D, Guang X M, et al. Liriodendron genome sheds light on angiosperm phylogeny and species-pair differentiation[J]. Nat Plants, 2019, 5(1):18-25.
Xia W, Zhang B, Xing D, et al. Development of high-resolution DNA barcodes for Dioscorea species discrimination and phylogenetic analysis[J]. Ecol Evol, 2019, 9(18):10843-10853.
Gong L, Ding X X, Guan W, et al. Comparative chloroplast genome analyses of Amomum:Insights into evolutionary history and species identification[J]. BMC Plant Biol, 2022, 22(1):520.
Bureau J A, Oliva M E, Dong Y M, et al. Engineering yeast for the production of plant terpenoids using synthetic biology approaches[J]. Nat Prod Rep, 2023, 40(12):1822-1848.
Pang X H, Song J Y, Zhu Y J, et al. Applying plant DNA barcodes for Rosaceae species identification[J]. Cladistics, 2011, 27(2):165-170.
Hollingsworth P M, Graham S W, Little D P. Choosing and using a plant DNA barcode[J]. PLoS One, 2011, 6(5):e19254.
Carranza-Rojas J, Goeau H, Bonnet P, et al. Going deeper in the automated identification of Herbarium specimens[J]. BMC Evol Biol, 2017, 17(1):181.

2026年第57卷第2期

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doi: 10.7501/j.issn.0253-2670.2026.02.023

接收时间：2025-10-02
首发时间：2026-09-09

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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