Journal ArticleSigmoni: classification of nanopore signal with a compressed pangenome indexShivakumar, Vikram S; Ahmed, Omar Y; Kovaka, Sam; Zakeri, Mohsen; Langmead, BenSummary: Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on the r-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10–100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications. Availability and implementation: Sigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoniOxford Press2024-06-2810609224Bioinformatics40Supplement_1i287 to i2961367-4803https://doi.org/10.1093/bioinformatics/btae2132029552National Science Foundation