Rapid and precise alignment of raw reads against redundant databases with KMA

• Published in: BMC bioinformatics Vol. 19; no. 1; pp. 307 - 8
• Main Authors: Clausen, Philip T. L. C., Aarestrup, Frank M., Lund, Ole
• Format: Journal Article
• Language: English
• Published: London BioMed Central 29.08.2018; Springer Nature B.V; BMC
• Subjects: Algorithms; Alignment; Alleles; Antimicrobial agents; Antimicrobial resistance; Bioinformatics; Biomedical and Life Sciences; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer Simulation; Databases, Factual; Drug resistance; E coli; Epidemiology; Gene mapping; Genome, Human; Genomes; High-Throughput Nucleotide Sequencing - methods; Humans; Life Sciences; Mapping; Methods; Microarrays; Multilocus sequence typing; Nucleotide sequence; Redundancy; Research Article; Sequence Alignment; Sequence analysis (methods); Sequence Analysis, DNA - methods; Software; Denmark; Minimap; Multi-mapping Reads; Core Genome MLST; Bowtie; Redundant Database
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2336-6

Background As 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. Results The 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. Conclusion With KMA, it was possible map raw reads directly against redundant databases with high accuracy, speed and memory efficiency.