摘要
      综合抗生素耐药性数据库（CARD；CARD.mcmaster.ca）将抗生素耐药性本体论（ARO）与精心策划的AMR基因（ARG）序列和耐药性赋予突变相结合，为耐药性的注释和解释提供了信息学框架。截至3.2.4版本，CARD包含6627个本体术语、5010个参考序列、1933个突变、3004篇出版物和5057个AMR检测模型，可由附带的抗性基因识别器（RGI）软件用于注释基因组或宏基因组序列。自2020年以来，重点加强的治疗措施包括扩大β-内酰胺酶治疗，纳入结核分枝杆菌基于可能性的AMR突变，添加消毒剂和防腐剂及其相关的ARG，以及系统治疗耐药性调节剂。这一扩展的管理包括180个新的AMR基因家族、15个新的药物类别、1个新的耐药机制和两个新的本体论关系：进化变异因子和is_small_molecule_inhibitor。抗药性的计算机预测和ARG的流行统计数据已扩展到377种病原体、21079条染色体、2662个基因组岛、41828个质粒和155606个全基因组鸟枪组装体，从而整理了322710个独特的ARG等位基因序列。新功能包括CARD:社区提交的隔离耐药性数据的实时收集，以及为ARG引入标准化的15个字符的CARD短名称，以支持机器学习工作。
Abstract
The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring mutations to provide an informatics framework for annotation and interpretation of resistomes. As of version 3.2.4, CARD encompasses 6627 ontology terms, 5010 reference sequences, 1933 mutations, 3004 publications, and 5057 AMR detection models that can be used by the accompanying Resistance Gene Identifier (RGI) software to annotate genomic or metagenomic sequences. Focused curation enhancements since 2020 include expanded β-lactamase curation, incorporation of likelihood-based AMR mutations for Mycobacterium tuberculosis, addition of disinfectants and antiseptics plus their associated ARGs, and systematic curation of resistance-modifying agents. This expanded curation includes 180 new AMR gene families, 15 new drug classes, 1 new resistance mechanism, and two new ontological relationships: evolutionary_variant_of and is_small_molecule_inhibitor. In silico prediction of resistomes and prevalence statistics of ARGs has been expanded to 377 pathogens, 21,079 chromosomes, 2,662 genomic islands, 41,828 plasmids and 155,606 whole-genome shotgun assemblies, resulting in collation of 322,710 unique ARG allele sequences. New features include the CARD:Live collection of community submitted isolate resistome data and the introduction of standardized 15 character CARD Short Names for ARGs to support machine learning efforts.

https://academic.oup.com/nar/article/51/D1/D690/6764414?login=false