文章基本信息

标题：Resolving microbial membership using Abundance and Variability In Taxonomy (‘AVIT )
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作者：Anirikh Chakrabarti ; Jay Siddharth ; Christian L. Lauber 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2016
卷号：6
期号：1
DOI：10.1038/srep31655
语种：English
出版社：Springer Nature
摘要：Development of NGS has revolutionized the analysis in microbial ecology contributing to our deeper understanding of microbiota in health and disease. However, the quality, quantity and confidence of summarized taxonomic abundances are in need of further scrutiny due to sample dependent and independent effects. In this article we introduce 'AVIT (Abundance and Variability In Taxonomy), an unbiased method to enrich for assigned members of microbial communities. As opposed to using a priori thresholds, 'AVIT uses inherent abundance and variability of taxa in a dataset to determine the inclusion or rejection of each taxa for further downstream analysis. Using in-vitro and in-vivo studies, we benchmarked performance and parameterized 'AVIT to establish a framework for investigating the dynamic range of microbial community membership in clinically relevant scenarios.