ArticleGenome biology2023
HycDemux: a hybrid unsupervised approach for accurate barcoded sample demultiplexing in nanopore sequencing.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- ReadChop: a high-performance demultiplexer for long-read sequencing data.Bioinformatics (Oxford, England) · 2026Article
- DemuxTrans: Transformer and temporal convolution network for accurate barcode demultiplexing in nanopore sequencing.Bioinformatics (Oxford, England) · 2025Article
- MysteryMaster: scraping the bottom of the barrel of barcoded Oxford nanopore reads.BMC bioinformatics · 2025Article
- DNA-Barcoding for Cultivar Identification and Intraspecific Diversity Analysis of Agricultural Crops.International journal of molecular sciences · 2025Review
- Demultiplexing and barcode-specific adaptive sampling for nanopore direct RNA sequencing.Nature communications · 2025Article
- TDFPS-Designer: an efficient toolkit for barcode design and selection in nanopore sequencing.Genome biology · 2024Article
- HycDemux: a hybrid unsupervised approach for accurate barcoded sample demultiplexing in nanopore sequencing.Genome biology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA barcodes enable Oxford Nanopore sequencing to sequence multiple barcoded DNA samples on a single flow cell. DNA sequences with the same barcode need to be grouped together through demultiplexing. As the number of samples increases, accurate demultiplexing becomes difficult. We introduce HycDemux, which incorporates a GPU-parallelized hybrid clustering algorithm that uses nanopore signals and DNA sequences for accurate data clustering, alongside a voting-based module to finalize the demultiplexing results. Comprehensive experiments demonstrate that our approach outperforms unsupervised tools in short sequence fragment clustering and performs more robustly than current state-of-the-art demultiplexing tools for complex multi-sample sequencing data.
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Registered trials
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