MEGARes: an antimicrobial resistance database for high throughput sequencing

Abstract

Antimicrobial resistance has become an imminent concern for public health. As methods for detection and characterization of antimicrobial resistance move from targeted culture and polymerase chain reaction to high throughput metagenomics, appropriate resources for the analysis of large-scale data are required. Currently, antimicrobial resistance databases are tailored to smaller-scale, functional profiling of genes using highly descriptive annotations. Such characteristics do not facilitate the analysis of large-scale, ecological sequence datasets such as those produced with the use of metagenomics for surveillance. In order to overcome these limitations, we present MEGARes (https://megares.meglab.org), a hand-curated antimicrobial resistance database and annotation structure that provides a foundation for the development of high throughput acyclical classifiers and hierarchical statistical analysis of big data. MEGARes can be browsed as a stand-alone resource through the website or can be easily integrated into sequence analysis pipelines through download. Also via the website, we provide documentation for AmrPlusPlus, a user-friendly Galaxy pipeline for the analysis of high throughput sequencing data that is pre-packaged for use with the MEGARes database.

Shifts in the gut microbiota structure caused by Helicobacter pylori eradication therapy

“…Metagenomic taxonomic and metabolic profiling was performed using HUMAnN2 (Abubucker et al, 2012) (contains MetaPhlAn2 (Truong et al, 2015)) with default parameters (using MetaCyc (Caspi et al, 2007) database for default). Antibiotic resistance (AR) genes were identified in the metagenomes by mapping the reads to MEGARes database (Lakin et al, 2016) using Bowtie with keys -f -S -t -v 3 -k 1. Relative abundance of the group and class AR genes was searched using ResistomeAnalyzer (Lakin et al, 2016) and calculating RPKM (reads per kilobase per million mapped reads) value: num reads / ( gene length/1000 * total num reads/1 000 000 ),…”

“…Antibiotic resistance (AR) genes were identified in the metagenomes by mapping the reads to MEGARes database (Lakin et al, 2016) using Bowtie with keys -f -S -t -v 3 -k 1. Relative abundance of the group and class AR genes was searched using ResistomeAnalyzer (Lakin et al, 2016) and calculating RPKM (reads per kilobase per million mapped reads) value: num reads / ( gene length/1000 * total num reads/1 000 000 ),…”

Resistome SNP Calling via Read Colored de Bruijn Graphs

“…To perform this comparison, we simulated four datasets using BEAR in an identical manner as described above, varying the SNP rate and copy number. First, we constructed two datasets with 258,180 and 2,991,107 paired-end sequence reads with mean copy number of 15 (range [10,20]) and mean copy number of 30 (range [25,35]), respectively. Here, we kept the SNP rate constant at 0.00125, and selected 16 AMR genes to be in the set.…”

“…For simplicity we call these samples low-0.00125 and high-0.00125. Next, we simulated two additional datasets with 245,979 and 2,844,899 paired-end reads with mean coverage 15 (range [10,20]) and 30 (range [25,35]), respectively. Here, we set the SNP rate to 0.005, selected 10 AMR genes, and as in the previous experiment, included reads simulated from E coli and salmonella.…”

“…Furthermore, the potential for chimeric sequences becomes more frequent in resistome analysis due to sequence homology between AMR genes. Specifically, AMR genes within the same AMR class can share homologous regions that are typically between 60 bp and 150 bp in length [20], which is longer than the typical k-mer value and shorter than the read length. Hence, such homologous regions often correspond to several connected paths in the de Bruijn graph that are difficult to traverse unambiguously, implying that the de Bruijn graph cannot be used to reliably and correctly reconstruct the sequences corresponding to these AMR genes and their corresponding SNPs.…”