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KMID : 0545120170270122089
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Journal of Microbiology and Biotechnology
2017 Volume.27 No. 12 p.2089 ~ p.2093
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Deciphering Diversity Indices for a Better Understanding of Microbial Communities
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Kim Bo-Ra
Shin Ji-Won
Guevarra Robin B.
Lee Jun-Hyung
Kim Doo-Wan
Seol Kuk-Hwan
Lee Ju-Hoon
Kim Hyeun-Bum
Isaacson Richard E.
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Abstract
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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.
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KEYWORD
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Microbiota, microbial diversity, microbial ecology, diversity index
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