In recent years, the role of the microbiota has proved to be extremely important in medicine as one of the most important aspects for the characterization of living beings in both healthy and pathological conditions. Moreover, the development of shotgun technology, and in particular the cheaper 16S ribosomal RNA (rRNA) gene sequencing, made possible its wide diffusion. In veterinary sciences, microbiome studies have seen applications not only in medicine in the strict sense (e.g diagnosis) but also, for example, in food inspection (quality, fraud, etc.) and in animal feed preparation itself. However, focusing on microbial profiling by 16S rRNA sequencing, there are several crucial aspects to be considered: from the experimental design definition and the sample size problem to the data analysis steps. This latter involves several layers, e.g. which 16S rRNA databases to use, which metrics for alpha and beta diversity, etc. In this work, we want to present, as a case study, a critical discussion about the large number of alpha and beta diversity metrics and their impact in the statistical comparisons among groups.
Microbiome studies in veterinary field: communities’ diversity measurements pitfalls
Ugo ALAFirst
;Angela DEL CARRO;Mario GIACOBINI;Barbara COLITTI;Ada ROTA;Luigi BERTOLOTTI
Last
2023-01-01
Abstract
In recent years, the role of the microbiota has proved to be extremely important in medicine as one of the most important aspects for the characterization of living beings in both healthy and pathological conditions. Moreover, the development of shotgun technology, and in particular the cheaper 16S ribosomal RNA (rRNA) gene sequencing, made possible its wide diffusion. In veterinary sciences, microbiome studies have seen applications not only in medicine in the strict sense (e.g diagnosis) but also, for example, in food inspection (quality, fraud, etc.) and in animal feed preparation itself. However, focusing on microbial profiling by 16S rRNA sequencing, there are several crucial aspects to be considered: from the experimental design definition and the sample size problem to the data analysis steps. This latter involves several layers, e.g. which 16S rRNA databases to use, which metrics for alpha and beta diversity, etc. In this work, we want to present, as a case study, a critical discussion about the large number of alpha and beta diversity metrics and their impact in the statistical comparisons among groups.File | Dimensione | Formato | |
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