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AMRColab - 一个用户友好的抗菌药物耐药性检测和可视化工具。

AMRColab - a user-friendly antimicrobial resistance detection and visualization tool.

机构信息

Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia.

Center for Global Health Research (CGHR), Saveetha Medical College, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, India.

出版信息

Microb Genom. 2024 Oct;10(10). doi: 10.1099/mgen.0.001308.

DOI:10.1099/mgen.0.001308
PMID:39432417
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11493187/
Abstract

Antimicrobial resistance (AMR) poses a significant threat to global public health, with the potential to cause millions of deaths annually by 2050. Effective surveillance of AMR pathogens is crucial for monitoring and predicting their behaviour in response to antibiotics. However, many public health professionals lack the necessary bioinformatics skills and resources to analyse pathogen genomes effectively. To address this challenge, we developed AMRColab, an open-access bioinformatics analysis suite hosted on Google Colaboratory. AMRColab enables users with limited or no bioinformatics training to detect and visualize AMR determinants in pathogen genomes using a 'plug-and-play' approach. The platform integrates established bioinformatics tools such as AMRFinderPlus and hAMRonization, allowing users to analyse, compare and visualize trends in AMR pathogens easily. A trial run using methicillin-resistant (MRSA) strains demonstrated AMRColab's effectiveness in identifying AMR determinants and facilitating comparative analysis across strains. A workshop was conducted and feedback from participants indicated high confidence in using AMRColab and a willingness to incorporate it into their research. AMRColab's user-friendly interface and modular design make it accessible to a diverse audience, including medical laboratory technologists, medical doctors and public health scientists, regardless of their bioinformatics expertise. Future improvements to AMRColab will include enhanced visualization tools, multilingual support and the establishment of an online community platform. AMRColab represents a significant step towards democratizing AMR surveillance and empowering public health professionals to combat AMR effectively.

摘要

抗菌药物耐药性(AMR)对全球公共卫生构成重大威胁,到 2050 年,每年可能导致数百万人死亡。对抗菌药物耐药性病原体进行有效监测对于监测和预测其对抗生素的反应行为至关重要。然而,许多公共卫生专业人员缺乏必要的生物信息学技能和资源,无法有效地分析病原体基因组。为了解决这一挑战,我们开发了 AMRColab,这是一个在 Google Colaboratory 上托管的开放获取生物信息学分析套件。AMRColab 使具有有限或没有生物信息学培训的用户能够使用“即插即用”方法在病原体基因组中检测和可视化抗菌药物耐药性决定因素。该平台集成了经过验证的生物信息学工具,如 AMRFinderPlus 和 hAMRonization,使用户能够轻松地分析、比较和可视化抗菌药物耐药性病原体的趋势。使用耐甲氧西林金黄色葡萄球菌(MRSA)菌株进行的试用表明,AMRColab 能够有效地识别抗菌药物耐药性决定因素,并促进菌株间的比较分析。进行了一次研讨会,参与者的反馈表明,他们对使用 AMRColab 充满信心,并愿意将其纳入他们的研究中。AMRColab 用户友好的界面和模块化设计使其易于访问,包括医学实验室技术人员、医生和公共卫生科学家,无论他们的生物信息学专业知识如何。AMRColab 的未来改进将包括增强的可视化工具、多语言支持和建立在线社区平台。AMRColab 代表着在民主化抗菌药物耐药性监测方面迈出了重要一步,使公共卫生专业人员能够有效地对抗抗菌药物耐药性。

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Awareness and Predictors of the Use of Bioinformatics in Genome Research in Saudi Arabia.沙特阿拉伯基因组研究中生物信息学使用情况的认知与预测因素
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Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective.机器学习在抗菌药物耐药性预测中的应用:现状、局限性和临床视角。
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AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence.AMRFinderPlus 和参考基因目录有助于研究抗生素耐药性、应激反应和毒力之间的基因组联系。
Sci Rep. 2021 Jun 16;11(1):12728. doi: 10.1038/s41598-021-91456-0.
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A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch.病原体观察站:用于预测抗微生物药物耐药性和监测伤寒沙门氏菌的全球资源
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Microb Genom. 2021 Mar;7(3). doi: 10.1099/mgen.0.000510. Epub 2021 Feb 18.
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Genetic changes associated with tigecycline resistance in Staphylococcus aureus in vitro-selected mutants belonging to different lineages.金黄色葡萄球菌体外选择突变株中与替加环素耐药相关的遗传变化,这些突变株属于不同的谱系。
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