ArticleBioinformatics (Oxford, England)2024
Sigmoni: classification of nanopore signal with a compressed pangenome index.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Advanced deep learning strategies in nanopore RNA sequencing.RNA biology · 2026Review
- Hash functions in nucleotide sequence analysis.Genome research · 2026Review
- SquiDBase: a community resource of raw nanopore data from microbes.NAR genomics and bioinformatics · 2026Article
- Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.Bioinformatics (Oxford, England) · 2026Article
- Campolina: a deep neural framework for accurate segmentation of nanopore signals.Genome biology · 2026Article
- Basecalling-free resistance gene identification using a hybrid transformer in raw nanopore signals.Frontiers in microbiology · 2026Article
- Col-BWT: Pangenomic Seed Chaining with Maximal Matches Improves Read Classification.Journal of computational biology : a journal of computational molecular cell biology · 2026Article
- Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment.Nature methods · 2025Article
- The enduring advantages of the SLOW5 file format for raw nanopore sequencing data.GigaScience · 2025Article
- Improved pangenomic classification accuracy with chain statistics.bioRxiv : the preprint server for biology · 2024Article
- RawHash2: mapping raw nanopore signals using hash-based seeding and adaptive quantization.Bioinformatics (Oxford, England) · 2024Article
- Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment.bioRxiv : the preprint server for biology · 2024Article
- Faster Maximal Exact Matches with Lazy LCP Evaluation.Proceedings. Data Compression Conference · 2024Article
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5 authors.
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Abstract
summaryImprovements 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/sigmoni.
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