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Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing
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DMSC: A Dynamic Multi-Seeds Method for Clustering 16S rRNA Sequences Into OTUs.

Ze-Gang Wei1,2, Shao-Wu Zhang1

  • 1Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, China.

Frontiers in Microbiology
|March 28, 2019
PubMed
Summary

A new dynamic multi-seeds clustering method (DMSC) improves operational taxonomic unit (OTU) clustering in 16S rRNA sequencing. DMSC reduces overestimation and enhances accuracy, offering a robust alternative for microbiome analysis.

Keywords:
16S rRNAclusteringdynamic updatemulti-seedsoperational taxonomic units

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS)-based 16S rRNA sequencing is crucial for microbiome and environmental sample analysis.
  • Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is a fundamental step in downstream analyses.
  • Traditional heuristic OTU clustering methods often suffer from overestimation and sensitivity to sequencing errors due to single-seed selection.

Purpose of the Study:

  • To introduce a novel dynamic multi-seeds clustering method (DMSC) for more accurate OTU picking.
  • To address the limitations of single-seed heuristic clustering in 16S rRNA sequence analysis.
  • To improve the quality and reliability of OTU clustering in microbiome research.

Main Methods:

  • DMSC employs a dynamic multi-seeds approach, selecting multiple core sequences (MCS) for each cluster.
  • Cluster assignment is based on average distance to MCS and distance standard deviation within MCS.
  • MCS are dynamically updated as new sequences are added to a cluster.

Main Results:

  • DMSC demonstrated superior performance compared to traditional methods (CD-HIT, UCLUST, DBH) on simulated and real datasets.
  • The method effectively reduces OTU overestimation and exhibits robustness against sequencing errors.
  • DMSC achieved higher quality clusters with low memory usage, as indicated by NMI and MCC metrics.

Conclusions:

  • DMSC offers a significant advancement in OTU clustering for 16S rRNA sequencing data.
  • The method provides a more accurate and reliable approach to microbiome and environmental sample analysis.
  • DMSC software is available for free download, facilitating its adoption in research.