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Related Experiment Video

Updated: Nov 5, 2025

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
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Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring

Verena Dully1, Thomas A Wilding2, Timo Mühlhaus3

  • 1Technische Universität Kaiserslautern, Ecology, D-67663 Kaiserslautern, Germany.

Computational and Structural Biotechnology Journal
|May 17, 2021
PubMed
Summary

Environmental DNA (eDNA) metabarcoding uses random forest (RF) machine learning for biomonitoring. Moderate rarefaction of sequence data sufficiently maintains RF accuracy, informing cost-effective sampling designs.

Keywords:
16S rRNAAMBI, AZTI's marine biotic indexASV, Amplicon Sequence VariantsAZE, allowable zone of effect, intermediate impact zoneBI, biotic indexBallWa, ballast water datasetBasCo, Basque coast datasetBiomonitoringCE, cage edgeCV, Coefficient of VarianceDADA2, Divisive Amplicon Denoising AlgorithmEQ, environmental qualityEnvironmental DNAFM, full modelMDS, multidimensional scalingMachine learningMarineNEB, New England BiolabsNW, north westNorSa, Norway salmon datasetOOB-error, out-of-bag error estimatePCR, polymerase chain reactionREF, reference siteRF, random forest algorithmSML, supervised machine learningScoSa, Scottish salmon farm datasetV3-V4, hypervariable gene regions of the 16s rRNAbp, base pairseDNA, environmental deoxyribonucleic acidmicrogAMBI, AZTI's marine biotic index based on microbial genesmtry, numbers of variables tried at each splitn, numberrRNA, small subunit prokaryotic ribosomal ribonucleic acid

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Area of Science:

  • Environmental DNA (eDNA) metabarcoding
  • Bioinformatics
  • Machine Learning in Ecology

Background:

  • eDNA metabarcoding is crucial for biomonitoring and impact assessments.
  • Sequencing depth in eDNA data varies significantly, posing challenges for analysis.
  • Rarefaction is used to normalize eDNA data for machine learning, but it reduces information.

Purpose of the Study:

  • To investigate the relationship between sequencing depth and random forest (RF) classification accuracy in eDNA data.
  • To guide future eDNA sampling designs for improved efficiency and cost-effectiveness.
  • To determine optimal rarefaction levels for reliable eDNA biomonitoring.

Main Methods:

  • Analysis of four bacterial amplicon datasets (three published, one new).
  • Application of the random forest (RF) machine learning algorithm.
  • Evaluation of RF classification performance across progressively rarefied datasets, starting from maximal data (min. 30,000 reads/sample).

Main Results:

  • Moderate rarefaction (50-5000 sequences/sample) achieved prediction performance comparable to full data.
  • Classification success was not significantly associated with the number of classes, data balance, or total sequences/samples.
  • The adequacy of training data to characterize classes was the most critical factor for predictive accuracy.

Conclusions:

  • Significant cost and computation time reductions are possible in eDNA biomonitoring.
  • Future sampling designs can be optimized by understanding the balance between data depth and classification accuracy.
  • Focusing on representative training data is key for successful eDNA-based biomonitoring.