一种新的方法来分析SARS-CoV-2的演变,基于对大型遗传数据的可视化和集群,这些数据在操作记忆中被紧地表示
A Yu Palyanov1, N V Palyanova2
1A.P. Ershov Institute of Informatics Systems of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia Research Institute of Virology, Federal Research Center of Fundamental and Translational Medicine, Novosibirsk, Russia Novosibirsk State University, Novosibirsk, Russia.
Vavilovskii zhurnal genetiki i selektsii
|February 13, 2025
概括
一种新的数据压缩方法有效地存储了数百万个SARS-CoV-2基因组,从而能够详细分析随着时间的推移病毒演变和变体特征.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 数以百万计的SARS-CoV-2基因组的快速收集是一个重要的数据存储和分析挑战.
- 现有的数据格式和压缩方法不足以处理庞大的基因组数据.
研究的目的:
- 开发一种高效的数据表示和组织方法,以在普通个人计算机上无损存储SARS-CoV-2基因组.
- 为了使病毒进化,突变模式和变异特征的详细分析,使用大规模的基因组数据集.
主要方法:
- 实现了一个用于数据表示的C++程序,将每个基因组存储为与参考序列相对应的突变,删除和插入列表.
- 实现了1:1500的压缩比,显著超过了标准的压缩工具.
- 从NCBI GenBank (2019-2024) 分析了数百万个SARS-CoV-2基因组,以研究基因组特性和进化趋势.
主要成果:
- 开发了一种数据结构,可以在约330Mb内无损存储1680万个SARS-CoV-2基因组.
- 根据大小和持续时间计算未确定核酸的分布,独特的基因组和基因组集群.
- 使用突变类型的3D表面图,在4.5年内可视化病毒进化,揭示了详细的进化动态.
结论:
- 拟议的数据表示方法为大型基因组数据集提供了前所未有的压缩和快速访问.
- 这种方法提供了一个更详细和更全面的SARS-CoV-2演变的视图,与传统的遗传学方法相比.
- 这些发现有助于通过对数百万个基因组的分析更深入地了解病毒动态和适应.
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