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• W4385919466 abstract "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 in- dividual 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. Sigmoni is 10-100X faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes." @default.
• W4385919466 created "2023-08-18" @default.
• W4385919466 creator A5009556658 @default.
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• W4385919466 date "2023-08-17" @default.
• W4385919466 modified "2023-10-14" @default.
• W4385919466 title "Sigmoni: classification of nanopore signal with a compressed pangenome index" @default.
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• W4385919466 doi "https://doi.org/10.1101/2023.08.15.553308" @default.
• W4385919466 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37645873" @default.
• W4385919466 hasPublicationYear "2023" @default.
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