부산대학교 도서관

By decomposing genome sequences into k-mers, it is possible to estimate genome differences without alignment. Techniques such as k-mer minimisers, for example 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. 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 of k unique k-mers per genome. On the other hand, contiguous series > k may be caused by accessory differences of length S-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. Author summary: The KmerAperture algorithm provides a substantive progression in alignment-free methodologies in bacterial comparative genomics. The utility of bacterial genomes for epidemiology and uncovering the genetic basis for phenotypic diversity and adaptation lies in our ability to compare them at scale. Perhaps the most limiting feature of genetic analysis workflows is sequence alignment. A number of k-mer based alignment-free methodologies have been developed to avoid the temporal and compute cost of alignment. However, in closely related bacteria the signal of polymorphisms, as they are represented by unique k-mers, is lost to even a small amount of unshared sequence, whether real or artefactual. Here, we show that it's possible to discern SNP diversity without alignment, including when SNPs are within k of one another, a perennial problem for k-mer analyses. [ABSTRACT FROM AUTHOR]