基于基因组的抗菌药物耐药性研究的综合生物信息学资源指南。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Debyani Samantray, Ankit Singh Tanwar, Thokur Sreepathy Murali, Angela Brand, Kapaettu Satyamoorthy, Bobby Paul
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引用次数: 0

摘要

高通量测序技术和生物信息学工具的使用极大地改变了微生物基因组研究。在复杂计算工具的帮助下,进行全基因组组装、基于基因组识别和比较不同物种,以及预测蛋白质、抗微生物耐药性和毒素基因的存在变得更加容易。这些生物信息学资源的质量可能会不断提高,在分析多个基因组数据时变得更加方便用户,能够有效地生成信息并将其转化为有意义的知识,并增强我们对AMR遗传机制的理解。在这份手稿中,我们为选择微生物研究的热门资源提供了重要指南,如基因组组装和注释、抗生素耐药性基因图谱、毒力因子鉴定和药物相互作用研究。此外,我们还讨论了面向计算机的微生物基因组研究的最佳实践、微生物基因组数据分析的新趋势、多组学数据的集成、机器学习算法的适当使用以及用于基因组数据分析中的开源生物信息学资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies.

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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