MysteryMaster: scraping the bottom of the barrel of barcoded Oxford nanopore reads.

IF 3.3 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

BMC Bioinformatics Pub Date : 2025-10-01 DOI:10.1186/s12859-025-06266-2

Abdolrahman Khezri, Sverre Branders, Anurag Basavaraj Bellankimath, Jawad Ali, Crystal Chapagain, Fatemeh Asadi, Manfred G Grabherr, Rafi Ahmad

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Abstract

Background: The high error rate associated with Oxford Nanopore sequencing technology adversely affects demultiplexing. To improve demultiplexing and reduce unclassified reads from nanopore sequencing data, we developed MysteryMaster, a demultiplexer that utilizes the optimal sequence aligner, Cola.

Results: When compared to Oxford Nanopore´s Dorado and Guppy demultiplexing tools across three datasets of 37 diverse samples with established ground truth, we found that MysteryMaster accurately identifies a similar or greater percentage of reads among the different basecalling models: Fast, HAC, and SUP. MysteryMaster performs slightly better than the other tools on data that was basecalled using the Fast basecalled model, while its performance in HAC and SUP data is similar to Dorado's. MysteryMaster has a false positive rate of just 0.41% with default settings.

Conclusions: While MysteryMaster can function as a standalone demultiplexer tool, the sequential application of Dorado and MysteryMaster produced the best overall performance.

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神秘大师：刮掉牛津纳米孔条形码的桶底。

背景：与牛津纳米孔测序技术相关的高错误率对解复用产生不利影响。为了改善解多路复用并减少纳米孔测序数据的未分类读取，我们开发了MysteryMaster，这是一种利用最优序列校准器Cola的解多路复用器。结果：当与Oxford Nanopore的Dorado和Guppy解复用工具在37个不同样本的三个数据集上进行比较时，我们发现，MysteryMaster在不同的基调用模型（Fast、HAC和SUP）中准确地识别出相似或更高百分比的读取。在使用Fast基调用模型进行基调用的数据上，MysteryMaster的表现略好于其他工具，而其在HAC和SUP数据上的表现与Dorado的相似。在默认设置下，MysteryMaster的误报率仅为0.41%。结论：虽然MysteryMaster可以作为独立的解复用器工具，但Dorado和MysteryMaster的顺序应用产生了最佳的整体性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

期刊介绍： BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.