薇甘菊叶绿体基因组密码子偏好性分析
Codon Bias Analysis on Mikania micrantha Chloroplast Genome
何雯杰 1黄华枝 2张驰 3梁宗锁 4昌秦湘1
作者信息
- 1. 浙江理工大学 生命科学与医药学院,浙江杭州 310018;太原学院 园林科学研究所,山西太原 030012
- 2. 广东轻工职业技术大学 生态环境技术学院,广东广州 510300
- 3. 太原学院 园林科学研究所,山西太原 030012
- 4. 浙江理工大学 生命科学与医药学院,浙江杭州 310018
- 折叠
摘要
为探究薇甘菊(Mikania micrantha)叶绿体基因组密码子使用模式和影响其偏好性的主要因素,从NCBI数据库下载并筛选出53条长度大于300 bp的叶绿体蛋白编码基因,采用CodonW 1.4.2、DNASTAR和SPSS 26.0等软件,对其密码子偏好性和成因进行多方面分析.结果表明,薇甘菊叶绿体基因组密码子3个位点的GC平均含量表现为GC1(47.06%)>GC2(39.85%)>GC3(28.16%);GC3含量为17.65%～36.93%.有效密码子数目(ENC)均值远大于35.00,表明密码子偏性较弱.RSCU值大于1.00的密码子有27个,其中第3位碱基使用A或U的密码子占比88.88%,表明薇甘菊密码子第3位碱基偏好使用A或U.中性绘图、ENC-plot和PR2-plot等分析结果表明,在影响薇甘菊密码子偏好性的众多因素中,自然选择发挥主要作用.CAU、ACA和CGU为最优密码子.研究结果可为薇甘菊叶绿体基因组学研究提供参考.
Abstract
In order to explore code usage mode of Mikania micrantha chloroplast genome and main factors af-fecting codon bias,chloroplast protein coding genes were downloaded from NCBI database and 53 chloroplast protein coding genes with length more than 300 bp were selected.Codon bias and contributing factors were com-prehensively analyzed by Codon W 1.4.2,DNASTAR and SPSS 26.0 softwares.Results showed that average GC contents in three positions of chloroplast genome codons in M.micrantha followed order of GC1(47.06%)>GC2(39.85%)>GC3(28.16%).Contents of GC3 ranged from 17.65%to 36.93%.Average effective number of codons(ENC)was significantly greater than 35.00,which indicated that codon bias was weak.Among 27 codons with RSCU values greater than 1.00,88.88%of codons used A or U as the third base,which indicated that the third base of codons in M.micrantha prefered to use A or U.Results of neutral plot,ENC-plot and PR2-plot analysis indicated that natural selection played main role among many factors affecting codon bias of M.micrantha.CAU,ACA and CGU were optimal codons.Results could provide references for research on chloroplast genom-ics of M.micrantha.
关键词
叶绿体基因组/密码子偏好性/薇甘菊Key words
chloroplast genome/codon bias/Mikania micrantha引用本文复制引用
基金项目
广州市基础研究计划项目(202005000002)
2023年山西省教育厅教学改革创新项目(J20231436)
山西省基础研究计划资助项目(202303021222227)
出版年
2024
国家科技期刊平台
NSTL
万方数据