Memory-bound k-mer selection for large and evolutionarily diverse reference libraries.

ABSTRACT: Using k-mers to find sequence matches is increasingly used in many bioinformatic applications, including metagenomic sequence classification. The accuracy of these downstream applications relies on the density of the reference databases, which are rapidly growing. Although the increased density provides hope for improvements in accuracy, scalability is a concern. Reference k-mers are kept in the memory during the query time, and saving all k-mers of these ever-expanding databases is fast becoming impractical. Several strategies for subsampling have been proposed, including minimizers and finding taxon-specific k-mers. However, we contend that these strategies are inadequate, especially when reference sets are taxonomically imbalanced, as are most microbial libra

SUBMITTER: Sapcı AOB 

PROVIDER: S-EPMC11529837 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature