Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use metagenomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of the AMR variation that we observe. We find no evidence for cross-selection between antimicrobial classes, or for effect of air travel between sites. However, AMR gene abundance strongly correlates with socio-economic, health and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region, and that improving sanitation and health could potentially limit the global burden of AMR. We propose metagenomic analysis of sewage as an ethically acceptable and economically feasible approach for continuous global surveillance and prediction of AMR.

BackgroundAs the cost of sequencing has declined, clinical diagnostics based on next generation sequencing (NGS) have become reality. Diagnostics based on sequencing will require rapid and precise mapping against redundant databases because some of the most important determinants, such as antimicrobial resistance and core genome multilocus sequence typing (MLST) alleles, are highly similar to one another.In order to facilitate this, a novel mapping method, KMA (k-mer alignment), was designed. KMA is able to map raw reads directly against redundant databases, it also scales well for large redundant databases. KMA uses k-mer seeding to speed up mapping and the Needleman-Wunsch algorithm to accurately align extensions from k-mer seeds. Multi-mapping reads are resolved using a novel sorting scheme (ConClave scheme), ensuring an accurate selection of templates.ResultsThe functionality of KMA was compared with SRST2, MGmapper, BWA-MEM, Bowtie2, Minimap2 and Salmon, using both simulated data and a dataset of Escherichia coli mapped against resistance genes and core genome MLST alleles. KMA outperforms current methods with respect to both accuracy and speed, while using a comparable amount of memory.ConclusionWith KMA, it was possible map raw reads directly against redundant databases with high accuracy, speed and memory efficiency.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2336-6) contains supplementary material, which is available to authorized users.

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Antimicrobial resistance (AMR) is one of the most important health threats globally. The ability to accurately identify resistant bacterial isolates and the individual antimicrobial resistance genes (ARGs) is essential for understanding the evolution and emergence of AMR and to provide appropriate treatment. The rapid developments in next-generation sequencing technologies have made this technology available to researchers and microbiologists at routine laboratories around the world. However, tools available for those with limited experience with bioinformatics are lacking, especially to enable researchers and microbiologists in low- and middle-income countries (LMICs) to perform their own studies. The CGE-tools (Center for Genomic Epidemiology) including ResFinder (https://cge.cbs.dtu.dk/services/ResFinder/) was developed to provide freely available easy to use online bioinformatic tools allowing inexperienced researchers and microbiologists to perform simple bioinformatic analyses. The main purpose was and is to provide these solutions for people involved in frontline diagnosis especially in LMICs. Since its original publication in 2012, ResFinder has undergone a number of improvements including improvement of the code and databases, inclusion of point mutations for selected bacterial species and predictions of phenotypes also for selected species. As of 28 September 2021, 820 803 analyses have been performed using ResFinder from 61 776 IP-addresses in 171 countries. ResFinder clearly fulfills a need for several people around the globe and we hope to be able to continue to provide this service free of charge in the future. We also hope and expect to provide further improvements including phenotypic predictions for additional bacterial species.

Read mapping is superior to assembly-based methods and the new KmerResistance seemingly outperforms currently available methods particularly when including datasets with few reads.