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

By Philip Thomas Lanken Conradsen Clausen, Frank Moller Aarestrup, Ole Lund. BMC Bioinformatics, 2018

763 citations recorded by the source corpus.

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

Subfields: Clinical Biochemistry, Molecular Biology, Molecular Medicine

View Paper: https://www.semanticscholar.org/paper/9512b85f01ddcafed416a119af8c84ff76cee2e7

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Citations in the corpus

Using Genomics to Track Global Antimicrobial Resistance (2019, methodology): “Multi-mapping reads are resolved using a novel sorting scheme (ConClave scheme) to ensure an accurate selection of templates (27).”
ResFinder – an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes (2022, methodology): “ResFinder software The Python code behind ResFinder is built upon the CGECore package, which runs and integrates the results of the aligners KMA [35] and blast [29] for all the CGEtools.”
In Silico Genotyping of Escherichia coli Isolates for Extraintestinal Virulence Genes by Use of Whole-Genome Sequencing Data (2020, methodology): “of strains) traT (10), papC (9), K5 kfiC (8), ompT (8), afa/draBC (5), kpsM II (2), and cvaC (2).”
The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities (2019, methodology): “It works by aligning reads against a reference gene using KMA, which uses k-mer seeding and the Needleman–Wunsch algorithm to accurately align the reads to the genes of interest (49).”