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Related Concept Videos

Modern Molecular Taxonomy01:29

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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...
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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Microbiome Analysis Using 16S Amplicon Sequencing: From Samples to ASVs.

Amnon Amir1

  • 1Microbiome Center, The Chaim Sheba Medical Center, Tel-Hashomer, Ramat-Gan, Israel. amnonim@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)
|February 19, 2021
PubMed
Summary
This summary is machine-generated.

This chapter outlines a bioinformatic workflow for identifying bacterial amplicon sequence variants (ASVs). It details the transition from raw sequencing reads to ASVs, focusing on the Deblur algorithm and experimental impacts.

Keywords:
Amplicon sequencingBioinformaticsMicrobiome

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Bacterial identification relies on analyzing DNA sequences.
  • Amplicon Sequence Variants (ASVs) offer high-resolution microbial profiling.
  • Understanding the bioinformatic pipeline is crucial for accurate ASV identification.

Purpose of the Study:

  • To present a typical bioinformatic workflow for bacterial ASV identification.
  • To explain the impact of experimental choices on downstream bioinformatic analysis.
  • To describe the Deblur algorithm for processing sequencing reads into ASVs.

Main Methods:

  • Bioinformatic analysis of bacterial DNA sequences.
  • Application of the Deblur algorithm for ASV inference.
  • Evaluation of experimental factors influencing ASV data.

Main Results:

  • A structured workflow from sequencing reads to bacterial ASVs is presented.
  • The Deblur algorithm is detailed for ASV generation.
  • Key experimental decisions impacting ASV analysis are highlighted.

Conclusions:

  • Effective bioinformatic workflows are essential for accurate bacterial ASV identification.
  • The Deblur algorithm is a key tool in ASV analysis.
  • Consideration of experimental design is critical for robust microbial community analysis.