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Related Concept Videos

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...

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Updated: Jun 8, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Advanced computational algorithms for microbial community analysis using massive 16S rRNA sequence data.

Yijun Sun1, Yunpeng Cai, Volker Mai

  • 1Interdisciplinary Center for Biotechnology Research, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32610-3622, USA. sunyijun@biotech.ufl.edu

Nucleic Acids Research
|October 9, 2010
PubMed
Summary
This summary is machine-generated.

Researchers developed new bioinformatics tools for analyzing microbial community data from next-generation sequencing. These methods offer deeper insights into microbial structures and disease associations beyond simple diversity estimates.

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Related Experiment Videos

Last Updated: Jun 8, 2026

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing
08:05

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Published on: March 19, 2018

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons
10:24

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Area of Science:

  • Bioinformatics
  • Microbial Ecology
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) generates massive microbial sequence data.
  • Extracting meaningful information from large nucleotide datasets is a key bioinformatics challenge.
  • Current methods often focus on basic microbial diversity estimation.

Purpose of the Study:

  • To introduce a novel analytical strategy for deep investigation of microbial communities.
  • To enable quantitative identification of disease-associated microbial signatures.
  • To provide advanced statistical tools for sequence-based ecological studies.

Main Methods:

  • Development of a novel analytical strategy incorporating discriminant and topology analyses.
  • Application of the strategy to a large human gut 16S rRNA dataset.
  • Utilizing computational methods for deep microbial community analysis.

Main Results:

  • The approach allowed for detailed characterization of microbial community structure.
  • Quantitative disease-associated microbial signatures were successfully derived.
  • The methods provided insights beyond basic microbial diversity estimation.

Conclusions:

  • The novel analytical strategy significantly enhances the understanding of microbial communities.
  • This approach offers rigorous statistical tools for sequence-based association studies.
  • It facilitates the elucidation of organism-environment and organism-condition relationships.