In silico framework for genome analysis

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-11-12 DOI:10.1016/j.future.2024.107585

M. Saqib Nawaz , M. Zohaib Nawaz , Yongshun Gong , Philippe Fournier-Viger , Abdoulaye Baniré Diallo

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引用次数: 0

Abstract

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.

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基因组分析的硅学框架

基因组拥有生物体的全部遗传信息。检查和分析基因组数据对于正确理解生物体，特别是有害病毒的主要特征、功能和进化性质起着至关重要的作用。然而，基因组数据的快速增长对从庞大而复杂的基因组数据集中提取有意义、有价值的见解提出了新的挑战和要求。本文开发了一个新颖的基因组数据分析框架（Framework for Genome Data Analysis，F4GDA），为各种形式的病毒基因组数据分析提供了多种方法。该框架的方法不仅能分析基因组的变化，还能分析各种基因组内容。作为一项案例研究，我们利用该框架分析了五种 SARS-CoV-2（严重急性呼吸系统综合征冠状病毒 2）VoC（关注变种）的基因组，这些变种根据地理位置被分为三种类型/组别，研究内容包括：(1) 核苷酸、氨基酸和同义密码子的变化；(2) 核苷酸、氨基酸和同义密码子的变化；(3) 核苷酸、氨基酸和同义密码子的变化、(2)不同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)VoC 基因组与 SARS-CoV-2 参考基因组序列的比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.