Abstract Motivation Identifying which documents in a large database contain a query string is a fundamental problem in Information Retrieval and Computational Biology. We focus on the approximate version of this problem for genomic sequences: the result set may contain false positive matches but no false negatives. State-of-the-art solutions rely on Bloom filters to index all k-mers (substrings of fixed length k) in the documents. To answer a query, documents sharing at least a user-prescribed fraction of query k-mers (typically 75–80%) are returned. Methods and results Here, we explore an alternative index design based on k-mer minimizers and integer compression methods. We show that a careful implementation of this design outperforms previous solutions based on Bloom filters by a wide margin: the index has lower memory footprint and faster query times, while false positive matches have only a minor impact on the ranking of the documents reported. This trend is robust across genomic datasets of different complexity and query workloads. Software The software is freely available at github. com/yhhshb/kaminari under the MIT license. Reproducibility scripts are available at github. com/vicLeva/benchmarksₖaminari.
Levallois et al. (Wed,) studied this question.