Genomes hold the complete genetic information of an organism. Examining and analyzing genomic data plays a critical role in properly understanding an organism, particularly the main characteristics, functionalities, and evolving nature of harmful viruses. However, the rapid increase in genomic data poses new challenges and demands for extracting meaningful and valuable insights from large and complex genomic datasets. In this paper, a novel Framework for Genome Data Analysis (F4GDA), is developed that offers various methods for the analysis of viral genomic data in various forms. The framework’s methods can not only analyze the changes in genomes but also various genome contents. As a case study, the genomes of five SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) VoC (variants of concern), which are divided into three types/groups on the basis of geographical locations, are analyzed using this framework to investigate (1) the nucleotides, amino acids and synonymous codon changes in the whole genomes of VoC as well as in the Spike (S) protein, (2) whether different environments affect the rate of changes in genomes, (3) the variations in nucleotide bases, amino acids, and codon base compositions in VoC genomes, and (4) to compare VoC genomes with the reference genome sequence of SARS-CoV-2.

中文翻译：

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用于基因组分析的计算机框架


基因组包含生物体的完整遗传信息。检查和分析基因组数据对于正确理解生物体起着至关重要的作用，尤其是有害病毒的主要特征、功能和进化性质。然而，基因组数据的快速增长为从大型复杂的基因组数据集中提取有意义和有价值的见解带来了新的挑战和需求。在本文中，开发了一种新的基因组数据分析框架 （F4GDA），它为分析各种形式的病毒基因组数据提供了多种方法。该框架的方法不仅可以分析基因组的变化，还可以分析各种基因组内容。作为一个案例研究，使用该框架分析了根据地理位置分为三种类型/组的五个 SARS-CoV-2（严重急性呼吸系统综合症冠状病毒 2）VoC（关注变体）的基因组，以研究 （1） VoC 全基因组以及刺突 （S） 蛋白中的核苷酸、氨基酸和同义密码子变化， （2） 不同的环境是否影响基因组的变化率，（3） VoC 基因组中核苷酸碱基、氨基酸和密码子碱基组成的变化，以及 （4） 将 VoC 基因组与 SARS-CoV-2 的参考基因组序列进行比较。