Jiang Shuai, Ding Yunyun, Zhao Gaili, Ye Shunxing, Liu Shucan, Yin Yan, Li Zeqi, Zou Xiaoxiao, Xie Daolong, You Changqiao, Guo Xinhong
College of Biology, Hunan University, Changsha, Hunan, 410082, China.
College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha, Hunan, 410128, China.
BMC Genomics. 2025 Apr 28;26(1):409. doi: 10.1186/s12864-025-11602-0.
The influenza virus (IV) is responsible for seasonal flu epidemics. Constant mutation of the virus results in new strains and widespread reinfections across the globe, bringing great challenges to disease prevention and control. Research has demonstrated that barcoding technology efficiently and cost-effectively differentiates closely related species on a large scale. We screened and validated species-specific RNA barcode segments based on the genetic relationships of four types of IVs, facilitating their precise identification in high-throughput sequencing viral samples.
Through the analysis of single nucleotide polymorphism, population genetic characteristics, and phylogenetic relationships in the training set, 7 IVA type, 29 IVB type, 40 IVC type, and 5 IVD type barcode segments were selected. In the testing set, the nucleotide-level recall rate for all barcode segments reached 96.86%, the average nucleotide-level specificity was approximately 55.27%, the precision rate was 100%, and the false omission rate was 0%, demonstrating high accuracy, specificity, and generalization capabilities for species identification. Ultimately, all four types of IVs were visualized in a combination of one-dimensional and two-dimensional codes and stored in an online database named Influenza Virus Barcode Database (FluBarDB, http://virusbarcodedatabase.top/database/index.html ).
This study validates the effective application of RNA barcoding technology in the detection of IVs and establishes criteria and procedures for selecting species-specific molecular markers. These advancements enhance the understanding of the genetic and epidemiological characteristics of IVs and enable rapid responses to viral genetic mutations.
流感病毒(IV)引发季节性流感流行。病毒的不断变异导致新毒株出现,并在全球范围内广泛引发再次感染,给疾病预防和控制带来巨大挑战。研究表明,条形码技术能够高效且经济地在大规模范围内区分密切相关的物种。我们基于四种IV型病毒的遗传关系筛选并验证了物种特异性RNA条形码片段,便于在高通量测序病毒样本中对其进行精确识别。
通过对训练集中的单核苷酸多态性、群体遗传特征和系统发育关系进行分析,选择了7个甲型流感病毒(IVA)型、29个乙型流感病毒(IVB)型、40个丙型流感病毒(IVC)型和5个丁型流感病毒(IVD)型条形码片段。在测试集中,所有条形码片段的核苷酸水平召回率达到96.86%,平均核苷酸水平特异性约为55.27%,精确率为100%,漏检率为0%,表明在物种识别方面具有高准确性、特异性和泛化能力。最终,所有四种类型的IV通过一维码和二维码组合进行可视化,并存储在一个名为流感病毒条形码数据库(FluBarDB,http://virusbarcodedatabase.top/database/index.html )的在线数据库中。
本研究验证了RNA条形码技术在IV检测中的有效应用,并建立了选择物种特异性分子标记的标准和程序。这些进展增强了对IV遗传和流行病学特征的理解,并能够对病毒基因突变做出快速反应。