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Background <p>Long-read sequencing has revolutionised metagenomics through improved metagenome assembly, taxonomic classification and functional characterisation. Automation can enhance the throughput, reproducibility, and accuracy of library preparation. However, the validation of automated library preparation protocols remains undetermined for metagenomic workflows, which are particularly sensitive to methodological perturbation. Here, we compare long-read metagenomic sequencing of environmental samples through parallel manual and automated protocols.</p> Results <p>Although automated library preparation led to minor reduction in read and contig lengths, taxonomic classification rate and alpha diversity was slightly higher than manual libraries, including the detection of more rare taxa. Despite this, no significant difference in microbial community structure was identified between manual and automated libraries.</p> Conclusions <p>Despite minor differences in sequencing and classification metrics, automated and manual library preparation resulted in comparable characterization of environmental community metagenomes. These findings demonstrate the suitability of automation for high-throughput long-read metagenomics, with broad applicability to automated long-read sequencing for improved efficiency and reproducibility.</p>

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Automated environmental metagenomics using Oxford nanopore sequencing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

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  • Harry T. Child,
  • Lucy Wierzbicki,
  • Gabrielle R. Joslin,
  • Katherine Roper,
  • Qiellor Haxhiraj,
  • Richard K. Tennant

摘要

Background

Long-read sequencing has revolutionised metagenomics through improved metagenome assembly, taxonomic classification and functional characterisation. Automation can enhance the throughput, reproducibility, and accuracy of library preparation. However, the validation of automated library preparation protocols remains undetermined for metagenomic workflows, which are particularly sensitive to methodological perturbation. Here, we compare long-read metagenomic sequencing of environmental samples through parallel manual and automated protocols.

Results

Although automated library preparation led to minor reduction in read and contig lengths, taxonomic classification rate and alpha diversity was slightly higher than manual libraries, including the detection of more rare taxa. Despite this, no significant difference in microbial community structure was identified between manual and automated libraries.

Conclusions

Despite minor differences in sequencing and classification metrics, automated and manual library preparation resulted in comparable characterization of environmental community metagenomes. These findings demonstrate the suitability of automation for high-throughput long-read metagenomics, with broad applicability to automated long-read sequencing for improved efficiency and reproducibility.