Synthetic Modeling of Emerging SARS-CoV-2 Recombinant Lineages via Markov Chain Mutation Forecasting

This dataset contains <b>18 synthetic full-length SARS-CoV-2 genomes</b> engineered using <b>Markov chain modeling</b> of Spike gene mutations derived from Nextclade TSV data. Each sequence represents a potential new PANGO lineage candidate based on unique mutation combinations not seen in current classifications.The Spike gene sequences were generated from mutation clusters and co-evolution patterns observed in recombinant <b>XFG-like variants</b>. These were back-translated and integrated into a Wuhan-Hu-1 reference backbone to create full-genome FASTA files.Also included are supporting metadata files:Mutation summary per genomeList of novel AA substitutionsThis work demonstrates how statistical modeling of mutation transitions can be used to simulate future lineages, enabling proactive genomic surveillance strategies.

本数据集包含**18条人工合成的全长新型冠状病毒（SARS-CoV-2）基因组**，其构建基于对从Nextclade的TSV格式数据（Nextclade TSV data）中提取的刺突（Spike）基因突变所开展的马尔可夫链建模（Markov chain modeling）。每条序列均基于现有分类体系中未出现的独特突变组合，代表潜在的新型PANGO谱系（PANGO lineage）候选株。刺突基因序列由重组类XFG变异株（XFG-like variants）中观测到的突变簇与共进化模式生成，随后经反向翻译并整合至Wuhan-Hu-1参考骨架（Wuhan-Hu-1 reference backbone），最终生成全基因组FASTA格式（FASTA）文件。本数据集同时附带配套元数据文件：单基因组突变汇总表、新型氨基酸替代（AA substitutions）列表。本研究证实，通过突变转移的统计建模可模拟未来可能出现的病毒谱系，从而为主动式基因组监测策略提供支撑。