School of Mathematical Science, Dalian University of Technology, Dalian 116024, China; School of Information Engineering, Suihua University, Suihua 152061, China.
School of Information Engineering, Suihua University, Suihua 152061, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.
Mol Phylogenet Evol. 2022 Oct;175:107583. doi: 10.1016/j.ympev.2022.107583. Epub 2022 Jul 8.
Exploring the dynamic variations of viral genomes utilizing with a phylogenetic analysis is vital to control the pandemic and stop its waves. Genetic network can be applied to depict the complicated evolution relationships of viral genomes. However, current phylogenetic methods cannot handle the cases with deletions effectively. Therefore, the k-mer natural vector is employed to characterize the compositions and distribution features of k-mers occurring in a viral genome, and construct a one-to-one relationship between a viral genome and its k-mer natural vector. Utilizing the k-mer natural vector, we proposed a novel genetic network to investigate the variations of viral genomes in transmission among humans. With the assistance of genetic network, we identified the super-spreaders that were responsible for the pandemic outbreaks all over the world and chose the parental strains to evaluate the effectiveness of diagnostics, therapeutics, and vaccines. The obtaining results fully demonstrated that our genetic network can truly describe the relationships of viral genomes, effectively simulate virus spread tendency, and trace the transmission routes precisely. In addition, this work indicated that the k-mer natural vector has the ability to capture established hotspots of diversities existing in the viral genomes and understand how genomic contents change over time.
利用系统发育分析探索病毒基因组的动态变化对于控制大流行并阻止其波浪式传播至关重要。遗传网络可用于描述病毒基因组复杂的进化关系。然而，当前的系统发育方法无法有效地处理缺失情况。因此，采用 k-mer 自然向量来描述病毒基因组中 k-mer 的组成和分布特征，并建立病毒基因组与其 k-mer 自然向量之间的一一对应关系。利用 k-mer 自然向量，我们提出了一种新的遗传网络来研究病毒基因组在人际传播中的变异情况。借助遗传网络，我们确定了在全球范围内引发大流行的超级传播者，并选择了亲代毒株来评估诊断、治疗和疫苗的有效性。研究结果充分表明，我们的遗传网络可以真实地描述病毒基因组之间的关系，有效地模拟病毒传播趋势，并准确追踪传播途径。此外，这项工作表明，k-mer 自然向量具有捕获病毒基因组中已建立的多样性热点的能力，并能够了解基因组内容随时间的变化情况。
