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Dear Editors-In-Chief,

We are pleased to submit our manuscript entitled "kmtricks: Efficient and flexible construction of Bloom filters for large sequencing data collections" for consideration as a *Bioinformatics Advances* paper.

The drastic reduction in sequencing costs in recent years has led to ambitious projects generating terabytes of sequencing data. These projects open the door to myriads of pangenomics applications, which often require the joint analysis of the entire sequencing dataset. Several preprocessing tools exist to index samples, using k-mers and Bloom filters as core data structures. Yet, the construction of these data structures is nontrivial and consumes enormous computational resources. It typically consists in two operations; 1/ multi-sample k-mer counting, to approximate the desired k-mer set (& filter it); 2/ Bloom filter construction from the k-mer set. These are seen as distinct steps and often performed by different tools involving performance penalties.

In this context, we propose an efficient method as well as a flexible tool to build Bloom filters for terabase-sized sequencing data collections while improving non-erroneous k-mers filtering. Our main contributions are 1/ An efficient method for k-mer matrices and Bloom filters construction using a joint counting technique; 2/ a novel technique to preserve non-erroneous but low-abundant k-mers which take advantages of our matrix representation.

Moreover and although the paper focuses on the Bloom filters construction, our tool is a generic toolbox for k-mer matrices processing, making it applicable in various fields. We hope that our work could benefit to the whole community working on large k-mer-based multi-sample analysis and indexing.

Sincerely,
