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Background <p>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 <i>MysteryMaster</i>, a demultiplexer that utilizes the optimal sequence aligner, Cola.</p> Results <p>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 <i>MysteryMaster</i> accurately identifies a similar or greater percentage of reads among the different basecalling models: Fast, HAC, and SUP. <i>MysteryMaster</i> 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. <i>MysteryMaster</i> has a false positive rate of just 0.41% with default settings.</p> Conclusions <p>While <i>MysteryMaster</i> can function as a standalone demultiplexer tool, the sequential application of Dorado and <i>MysteryMaster</i> produced the best overall performance.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月1日

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

摘要

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.