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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
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Swarm: robust and fast clustering method for amplicon-based studies.

Frédéric Mahé1, Torbjørn Rognes2, Christopher Quince3

  • 1CNRS, UMR 7144, EPEP - Évolution des Protistes et des Écosystèmes Pélagiques, Station Biologique de Roscoff , Roscoff , France ; Sorbonne Universités, UPMC Univ Paris 06, UMR 7144, Station Biologique de Roscoff , Roscoff , France ; Department of Ecology, University of Kaiserslautern , Kaiserslautern , Germany.

Peerj
|October 3, 2014
PubMed
Summary
This summary is machine-generated.

Swarm software addresses flaws in amplicon clustering by using local thresholds and abundance data. This creates robust operational taxonomic units, improving de novo amplicon analysis.

Keywords:
BarcodingEnvironmental diversityMolecular operational taxonomic units

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Existing de novo amplicon clustering methods have limitations.
  • Arbitrary global thresholds and input-order dependency affect clustering accuracy.
  • Centroid selection in current methods introduces bias.

Purpose of the Study:

  • To introduce Swarm, a novel amplicon clustering algorithm.
  • To overcome the limitations of existing clustering approaches.
  • To provide a robust and scalable solution for operational taxonomic unit generation.

Main Methods:

  • Swarm employs iterative clustering with local thresholds.
  • It refines clusters using internal structure and amplicon abundances.
  • The algorithm is designed to be input-order independent.

Main Results:

  • Swarm effectively reduces the influence of clustering parameters.
  • The method demonstrates speed and scalability.
  • It produces robust operational taxonomic units.

Conclusions:

  • Swarm offers an improved approach to de novo amplicon clustering.
  • The algorithm enhances the reliability of taxonomic unit generation.
  • Swarm is a valuable tool for large-scale amplicon sequence analysis.