PLoS GeneticsBioinformaticsPeer-reviewed journal article
KmerAperture: Retaining k-mer synteny for alignment-free extraction of core and accessory differences between bacterial genomes
Matthew MooreiD, Mirjam LaageriD, Paolo RibecaiD, Xavier DidelotiD
Abstract
By decomposing genome sequences into k-mers, it is possible to estimate genome differences without alignment. Techniques such as k-mer minimisers (MinHash), have been developed and are often accurate approximations of distances based on full k-mer sets. These and other alignment-free methods avoid the large temporal and computational expense of alignment or mapping. However, these k-mer set comparisons are not entirely accurate within-species and can be completely inaccurate within-lineage. This is due, in part, to their inability to distinguish core polymorphism from accessory differences. Here we present a new approach, KmerAperture, which uses information on the k-mer relative genomic positions to determine the type of polymorphism causing differences in k-mer presence and absence between pairs of genomes. Single SNPs are expected to result in contiguous series of relative unique k-mers of length L= k. On the other hand, series of length L > k may be caused by accessory differences of length L-k+1; when the start and end of the sequence are contiguous with homologous sequence. Alternatively, they may be caused by multiple SNPs within k bp from each other and KmerAperture can determine whether that is the case. To demonstrate use cases KmerAperture was benchmarked using datasets including a very low diversity simulated population with accessory content independent from the number of SNPs, a simulated population were SNPs are spatially dense, a moderately diverse real cluster of genomes (Escherichia coli ST1193) with a large accessory genome and a low diversity real genome cluster (Salmonella Typhimurium ST34). We show that KmerAperture can accurately distinguish both core and accessory sequence diversity without alignment, outperforming other k-mer based tools.
- organism: bacteria (other)
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
- Citations
- 6
- FWCI
- 0.81
- Field percentile
- top 33%
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