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Resolving microbial membership using Abundance and Variability In Taxonomy ('AVIT ).

Anirikh Chakrabarti1, Jay Siddharth1, Christian L Lauber1

  • 1Nestlé Institute of Health Sciences, Lausanne, Switzerland.

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|August 18, 2016
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Summary

This study introduces AVIT (Abundance and Variability In Taxonomy), a novel method for microbial ecology analysis. AVIT enhances the reliability of taxonomic abundance data, improving our understanding of microbial communities in health and disease.

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Area of Science:

  • Microbial Ecology
  • Bioinformatics
  • Genomics

Background:

  • Next-generation sequencing (NGS) has transformed microbial ecology research.
  • Accurate taxonomic abundance data is crucial for understanding host-microbe interactions in health and disease.
  • Current methods for analyzing microbial community data require further validation due to inherent variability.

Purpose of the Study:

  • To introduce AVIT (Abundance and Variability In Taxonomy), an unbiased computational method.
  • To improve the quality, quantity, and confidence of summarized taxonomic abundances.
  • To establish a framework for investigating microbial community dynamics in clinical contexts.

Main Methods:

  • AVIT utilizes inherent abundance and variability of taxa within a dataset.
  • The method determines taxa inclusion/rejection without relying on a priori thresholds.
  • Performance was benchmarked using in-vitro and in-vivo studies.

Main Results:

  • AVIT provides an unbiased approach to enrich for assigned members of microbial communities.
  • The method demonstrates robust performance in both in-vitro and in-vivo experimental settings.
  • Parameterization of AVIT offers a framework for dynamic microbial community analysis.

Conclusions:

  • AVIT offers a significant advancement in analyzing microbial community composition.
  • The method enhances the reliability of taxonomic abundance data for downstream applications.
  • AVIT facilitates a more accurate investigation of microbial dynamics in health and disease states.