Comparative study of sequence aligners for detecting antibiotic resistance in bacterial metagenomes

We aim to compare the performance of Bowtie2, bwa-mem, blastn and blastx when aligning bacterial metagenomes against the Comprehensive Antibiotic Resistance Database (CARD). Simulated reads were used to evaluate the performance of each aligner under the following four performance criteria: correctly mapped, false positives, multi-reads and partials. The optimal alignment approach was applied to samples from two wastewater treatment plants to detect antibiotic resistance genes using next generation sequencing. blastn mapped with greater accuracy among the four sequence alignment approaches considered followed by Bowtie2. blastx generated the greatest number of false positives and multi-reads when aligned against the CARD. The performance of each alignment tool was also investigated using error-free reads. Although each aligner mapped a greater number of error-free reads as compared to Illumina-error reads, in general, the introduction of sequencing errors had little effect on alignment results when aligning against the CARD. Given each performance criteria, blastn was found to be the most favourable alignment tool and was therefore used to assess resistance genes in sewage samples. Beta-lactam and aminoglycoside were found to be the most abundant classes of antibiotic resistance genes in each sample.

Antibiotic resistance genes (ARGs) are pollutants known to persist in wastewater treatment plants among other environments, thus methods for detecting these genes have become increasingly relevant. Next generation sequencing has brought about a host of sequence alignment tools that provide a comprehensive look into antimicrobial resistance in environmental samples. However, standardizing practices in ARG metagenomic studies is challenging since results produced from alignment tools can vary significantly. Our study provides sequence alignment results of synthetic, and authentic bacterial metagenomes mapped against an ARG database using multiple alignment tools, and the best practice for detecting ARGs in environmental samples.