Terminologie

Wipperman, B.; Galanski, Michael

doi:10.1007/978-3-642-60159-0_1

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From KMA analysis of subsampled sequences, the read count data included in the mapstat les were merged using a custom python script based on the merge script from MetaPhlAn (54). To perform ordination, data was imported into R version as a phyloseq object using a custom function (54) based on the metaphlanToPhyloseq function by Wipperman (55). Ordination of read counts mapping to ARGs for the subsampled Enterococcus, E. coli, and Klebsiella replicates was conducted using non-metric multi-dimensional scaling (NMDS) and Bray-Curtis dissimilarity.…”

Section: Amr Data Analysismentioning

confidence: 99%

Modeling the Limits of Detection for Antimicrobial Resistance Genes in Agri-Food Metagenomic Samples

Cooper,

Low,

Wong

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Despite the potential for dissemination of antimicrobial resistance (AMR) through food and food production, there are few studies of the prevalence of AMR organisms (AROs) in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance AROs within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches. Results We simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2 and Bracken indicate presence of organisms not included in the synthetic metagenomes. MetaPhlAn3 analysis of community composition was more specific but with lower sensitivity than both Kraken2 and Bracken. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 falsely reported presence of distantly related ARGs at all isolate genome coverage levels. Conclusions Overall, ARGs were accurately detected in the synthetic metagenomes (approx. 40 million paired-end reads) by all methods when the ARO reads constituted > 0.4% of the reads (approximately 5X isolate coverage). Reducing target gene coverage cutoffs allowed detection of ARGs present at lower abundance; however, this reduced cutoff may result in alternative ARG-allele detection. Background flora in metagenomes resulted in differences in detection of ARGs by KMA. Further advancements in sequencing technologies providing increased depth of coverage or longer read length may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking low-abundance AROs in agri-food samples.

show abstract

Section: Amr Data Analysismentioning

confidence: 99%