SPUMONI 2: Improved pangenome classification using a compressed index of minimizer digests

Genomics analyses often use a large sequence collection as a reference, like a pangenome or taxonomic database. We previously described SPUMONI, which performs binary classification of nanopore reads using pangenomic matching statistics. Here we describe SPUMONI 2, an improved version that is faster, more memory efficient, works effectively for both short and long reads, and can solve multi-class classification problems with the aid of a novel sampled document array structure. By incorporating minimizers, SPUMONI 2 reduces index size by a factor of 2 compared to SPUMONI, yielding an index more than 65 times smaller than minimap2’s for a mock community pangenome. SPUMONI 2 also achieves a speed improvement of 3-fold compared to SPUMONI and 15-fold compared to minimap2. We show SPUMONI 2 achieves an advantageous mix of accuracy and efficiency for short and long reads, including in an adaptive sampling scenario. We further demonstrate that SPUMONI 2 can detect contaminated contigs in genome assemblies, and can perform multi-class metagenomic read classification.