Dataset Information

Microbial Diversity Analysis of Bupleurum latissimum Nakai from Ulleung-Do of South Korea Using Next-Generation Sequencing.

ABSTRACT: Bupleurum latissimum Nakai is a rare endemic species native to Ulleung-do in South Korea. This study aimed to report on the rhizosphere soil microbial diversity of B. latissimum. Proteobacteria, Actinobacteria, and Verrucomicrobia were identified in relative abundances of 27.77%, 21.70%, and 15.27%, respectively.

SUBMITTER: Han KM

PROVIDER: S-EPMC9022543 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Microbial Diversity Analysis of Bupleurum latissimum Nakai from Ulleung-Do of South Korea Using Next-Generation Sequencing.

Han Kyeung Min KM Choi Mi Na MN Pi Jung Hun JH Kim Sea Hyun SH Kim Yeong Su YS Na Chae Sun CS

Microbiology resource announcements 20220316 4

Bupleurum latissimum Nakai is a rare endemic species native to Ulleung-do in South Korea. This study aimed to report on the rhizosphere soil microbial diversity of B. latissimum. Proteobacteria, Actinobacteria, and Verrucomicrobia were identified in relative abundances of 27.77%, 21.70%, and 15.27%, respectively. ...[more]

PMID: 35293822

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Project description:Amplicon sequencing has revolutionized our ability to study DNA collected from environmental samples by providing a rapid and sensitive technique for microbial community analysis that eliminates the challenges associated with lab cultivation and taxonomic identification through microscopy. In water resources management, it can be especially useful to evaluate ecosystem shifts in response to natural and anthropogenic landscape disturbances to signal potential water quality concerns, such as the detection of toxic cyanobacteria or pathogenic bacteria. Amplicon sequencing data consist of discrete counts of sequence reads, the sum of which is the library size. Groups of samples typically have different library sizes that are not representative of biological variation; library size normalization is required to meaningfully compare diversity between them. Rarefaction is a widely used normalization technique that involves the random subsampling of sequences from the initial sample library to a selected normalized library size. This process is often dismissed as statistically invalid because subsampling effectively discards a portion of the observed sequences, yet it remains prevalent in practice and the suitability of rarefying, relative to many other normalization approaches, for diversity analysis has been argued. Here, repeated rarefying is proposed as a tool to normalize library sizes for diversity analyses. This enables (i) proportionate representation of all observed sequences and (ii) characterization of the random variation introduced to diversity analyses by rarefying to a smaller library size shared by all samples. While many deterministic data transformations are not tailored to produce equal library sizes, repeatedly rarefying reflects the probabilistic process by which amplicon sequencing data are obtained as a representation of the amplified source microbial community. Specifically, it evaluates which data might have been obtained if a particular sample's library size had been smaller and allows graphical representation of the effects of this library size normalization process upon diversity analysis results.

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Project description:Diversity analysis of amplicon sequencing data has mainly been limited to plug-in estimates calculated using normalized data to obtain a single value of an alpha diversity metric or a single point on a beta diversity ordination plot for each sample. As recognized for count data generated using classical microbiological methods, amplicon sequence read counts obtained from a sample are random data linked to source properties (e.g., proportional composition) by a probabilistic process. Thus, diversity analysis has focused on diversity exhibited in (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, and most probable number methods) to sample collection and processing procedures of amplicon sequencing methods to facilitate inference reflecting the probabilistic nature of such data and evaluation of uncertainty in diversity metrics. Following description of types of random error, mechanisms such as clustering of microorganisms in the source, differential analytical recovery during sample processing, and amplification are found to invalidate a multinomial relative abundance model. The zeros often abounding in amplicon sequencing data and their implications are addressed, and Bayesian analysis is applied to estimate the source Shannon index given unnormalized data (both simulated and experimental). Inference about source diversity is found to require knowledge of the exact number of unique variants in the source, which is practically unknowable due to library size limitations and the inability to differentiate zeros corresponding to variants that are actually absent in the source from zeros corresponding to variants that were merely not detected. Given these problems with estimation of diversity in the source even when the basic multinomial model is valid, diversity analysis at the level of samples with normalized library sizes is discussed.

Dataset Information

Microbial Diversity Analysis of Bupleurum latissimum Nakai from Ulleung-Do of South Korea Using Next-Generation Sequencing.

Publications

Microbial Diversity Analysis of <i>Bupleurum latissimum</i> Nakai from Ulleung-Do of South Korea Using Next-Generation Sequencing.

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