MEGARes and AMR++, v3.0: an updated comprehensive database of antimicrobial resistance determinants and an improved software pipeline for classification using high-throughput sequencing

Abstract

Antimicrobial resistance (AMR) is considered a critical threat to public health, and genomic/metagenomic investigations featuring high-throughput analysis of sequence data are increasingly common and important. We previously introduced MEGARes, a comprehensive AMR database with an acyclic hierarchical annotation structure that facilitates high-throughput computational analysis, as well as AMR++, a customized bioinformatic pipeline specifically designed to use MEGARes in high-throughput analysis for characterizing AMR genes (ARGs) in metagenomic sequence data. Here, we present MEGARes v3.0, a comprehensive database of published ARG sequences for antimicrobial drugs, biocides, and metals, and AMR++ v3.0, an update to our customized bioinformatic pipeline for high-throughput analysis of metagenomic data (available at MEGLab.org). Database annotations have been expanded to include information regarding specific genomic locations for single-nucleotide polymorphisms (SNPs) and insertions and/or deletions (indels) when required by specific ARGs for resistance expression, and the updated AMR++ pipeline uses this information to check for presence of resistance-conferring genetic variants in metagenomic sequenced reads. This new information encompasses 337 ARGs, whose resistance-conferring variants could not previously be confirmed in such a manner. In MEGARes 3.0, the nodes of the acyclic hierarchical ontology include 4 antimicrobial compound types, 59 resistance classes, 233 mechanisms and 1448 gene groups that classify the 8733 accessions.

The K-mer antibiotic resistance gene variant analyzer (KARGVA)

“…For instance, MegaRES has been recently updated to v.3.0. with a more advanced characterization of ARGVs ( Bonin et al, 2022 ), including SNP confirmation. The new MegaRES is now being deduplicated and merged with the other sources by the KARGVA developers for future release.…”

Comparative genomics reveals extensive intra-species genetic divergence of the prevalent gut commensal Ruminococcus gnavus

“…Genomic islands with antimicrobial-resistance (AMR) genes were predicted using ABRicate (version 0.8.7) (https://github.com/tseemann/abricate/) searches by the default parameters referring to the Antibiotic Resistance Gene-Annotation (ARG-ANNOT) database (version: July 8 2017) [35], the NCBI β-lactamase database, the Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca/) (version: March 17 2017) and the ResFinder database (version: July 8 2017) [36]. In order to remove false positives, we applied a cut-off of the best identity of >75 % (as default) [37]; and to confirm the validity of the result, we applied >80 % length of the query coverage as in AMR++ (version 3.0) [38] to present as the final result of the determination, and results visualized accordingly. Virulence factors were screened referring to the Virulence Factor Database (VFDB) (version: March 17 2017) [37], and the plasmids were predicted referring to the PlasmidFinder database using ABRicate (version 0.8.7) as well , and results were visualized using the Pheatmap package [30].…”

“…[36]. In order to remove false positives, we applied a cut-off of the best identity of >75 % (as default) [37]; and to confirm the validity of the result, we applied >80 % length of the query coverage as in AMR++ (version 3.0) [38] to present as the final result of the determination, and results visualized accordingly. Virulence factors were screened referring to the Virulence Factor Database (VFDB) (version: March 17 2017) [37], and the plasmids were predicted referring to the PlasmidFinder database using ABRicate (version 0.8.7) as well, and results were visualized using the Pheatmap package [30].…”

Exploring Microbial Diversity and Functional Potential along the Bay of Bengal Coastline in Bangladesh: Insights from Amplicon Sequencing and Shotgun Metagenomics

“…For antimicrobial resistance profiling, two different pipelines were used. The first is the AMR++ pipeline with the Microbial Ecology Group (MEG) antimicrobial resistance database (MEGARes v3.0.0) [73][74][75] . The short reads were aligned to the MEGARes database using Burrows-Wheeler Aligner (BWA) 76 , with the gene fraction (the percentage of genes that were matched to by at least one sequencing read) set to ≥80%.…”

“…Finally, for each gene found, depth and coverage were calculated by using Samtool's mpileup script. We used the same pipelines mentioned above to find virulence factor-associated genes (VFGs) from the Virulence Factors of Pathogenic Bacteria (VFDB) database 74,[77][78][79] .…”