Sigmoni: classification of nanopore signal with a compressed pangenome index.

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

SUBMITTER: Shivakumar VS 

PROVIDER: S-EPMC10462034 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature