kmtricks: Efficient and flexible construction of Bloom filters for large sequencing data collections

When indexing large collections of short-read sequencing data, a common operation that has now been implemented in several tools (Sequence Bloom Trees and variants, BIGSI, ..) is to construct a collection of Bloom filters, one per sample. Each Bloom filter is used to represent a set of k-mers which approximates the desired set of all the non-erroneous k-mers present in the sample. However, this approximation is imperfect, especially in the case of metagenomics data. Erroneous but abundant k-mers are wrongly included, and non-erroneous but low-abundant ones are wrongly discarded. We propose kmtricks, a novel approach for generating Bloom filters from terabase-sized collections of sequencing data. Our main contributions are 1/ an efficient method for jointly counting k-mers across multiple samples, including a streamlined Bloom filter construction by directly counting, partitioning and sorting hashes instead of k-mers, which is approximately four times faster than state-of-the-art tools; 2/ a novel technique that takes advantage of joint counting to preserve low-abundant k-mers present in several samples, improving the recovery of non-erroneous k-mers. Our experiments highlight that this technique preserves around 8x more k-mers than the usual yet crude filtering of low-abundance k-mers in a large metagenomics dataset. Availabilityhttps://github.com/tlemane/kmtricks FundingThe work was funded by IPL Inria Neuromarkers, ANR Inception (ANR-16-CONV-0005), ANR Prairie (ANR-19-P3IA-0001), ANR SeqDigger (ANR-19-CE45-0008).