FroM Superstring to Indexing: a space-efficient index for unconstrained k-mer sets using the Masked Burrows-Wheeler Transform (MBWT)

Motivation The growing volumes and heterogeneity of genomic data call for scalable and versatile k-mer-set indexes. However, state-of-the-art indexes such as SBWT and SSHash depend on long non-branching paths in de Bruijn graphs, which limits their efficiency for small k, sampled data, or high-diversity settings.Results We introduce FMSI, a superstring-based index for arbitrary k-mer sets that supports efficient membership and compressed dictionary queries with strong theoretical guarantees. FMSI builds on recent advances in k-mer superstrings and uses the Masked Burrows-Wheeler Transform, a novel extension of the classical Burrows-Wheeler Transform that incorporates position masking. Across a range of k values and dataset types-including genomic, pangenomic, and metagenomic-FMSI consistently achieves superior query space efficiency, using up to 2-3x less memory than state-of-the-art methods, while maintaining competitive query times. Only a space-optimized version of SBWT can match the FMSI's footprint in some cases, but then FMSI is 2-3x faster. Our results establish superstring-based indexing as a robust, scalable, and versatile framework for arbitrary k-mer sets across diverse bioinformatics applications.Availability and implementation FMSI is developed in C++ and released under the MIT license, with source code provided at https://github.com/OndrejSladky/fmsi and an installable package available through Bioconda. The datasets used in the experiments are deposited at Zenodo (https://doi.org/10.5281/zenodo.14722244).