HycDemux: a hybrid unsupervised approach for accurate barcoded sample demultiplexing in nanopore sequencing

Genome Biology

Method

Xiujuan Sun¹  Xin Gao²  Yang Xue³  Guojun Li³  Renmin Han³  Junhai Qi⁴  Fa Zhang⁵ 

[1] High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, 100190, Beijing, China;King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 23955, Thuwal, Saudi Arabia;Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, 266237, Qingdao, China;Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, 266237, Qingdao, China;BioMap Research, California, USA;School of Medical Technolgoy, Beijing Institute of Technology, 100085, Beijing, China;

关键词: Nanopore sequencing;    Demultiplexing;    Clustering;   

DOI  :  10.1186/s13059-023-03053-1

 received in 2022-01-09, accepted in 2023-09-08,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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