Literature DB >> 29032640

Deciphering Diversity Indices for a Better Understanding of Microbial Communities.

Bo-Ra Kim1, Jiwon Shin1, Robin Guevarra1, Jun Hyung Lee1, Doo Wan Kim2, Kuk-Hwan Seol2, Ju-Hoon Lee3, Hyeun Bum Kim1, Richard Isaacson4.   

Abstract

The past decades have been a golden era during which great tasks were accomplished in the field of microbiology, including food microbiology. In the past, culture-dependent methods have been the primary choice to investigate bacterial diversity. However, using cultureindependent high-throughput sequencing of 16S rRNA genes has greatly facilitated studies exploring the microbial compositions and dynamics associated with health and diseases. These culture-independent DNA-based studies generate large-scale data sets that describe the microbial composition of a certain niche. Consequently, understanding microbial diversity becomes of greater importance when investigating the composition, function, and dynamics of the microbiota associated with health and diseases. Even though there is no general agreement on which diversity index is the best to use, diversity indices have been used to compare the diversity among samples and between treatments with controls. Tools such as the Shannon- Weaver index and Simpson index can be used to describe population diversity in samples. The purpose of this review is to explain the principles of diversity indices, such as Shannon- Weaver and Simpson, to aid general microbiologists in better understanding bacterial communities. In this review, important questions concerning microbial diversity are addressed. Information from this review should facilitate evidence-based strategies to explore microbial communities.

Keywords:  Microbiota; diversity index; microbial diversity; microbial ecology

Mesh:

Substances:

Year:  2017        PMID: 29032640     DOI: 10.4014/jmb.1709.09027

Source DB:  PubMed          Journal:  J Microbiol Biotechnol        ISSN: 1017-7825            Impact factor:   2.351


  141 in total

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Journal:  Gut Microbes       Date:  2020-05-23

8.  Gut microbiome, body weight, and mammographic breast density in healthy postmenopausal women.

Authors:  Lusine Yaghjyan; Volker Mai; Xuefeng Wang; Maria Ukhanova; Maximiliano Tagliamonte; Yessica C Martinez; Shannan N Rich; Kathleen M Egan
Journal:  Cancer Causes Control       Date:  2021-03-27       Impact factor: 2.506

9.  The Effect of Inoculation of a Diazotrophic Bacterial Consortium on the Indigenous Bacterial Community Structure of Sugarcane Apoplast Fluid.

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Journal:  Curr Microbiol       Date:  2021-06-25       Impact factor: 2.188

10.  Sex effects in the association between airway microbiome and asthma.

Authors:  Renjin Chen; Leran Wang; Tammy Koch; Vanessa Curtis; Huiqing Yin-DeClue; Scott A Handley; Liang Shan; Michael J Holtzman; Mario Castro; Leyao Wang
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