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Related Concept Videos

Quality Control01:05

Quality Control

206
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
206
Quality Assurance01:19

Quality Assurance

167
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
167

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Related Experiment Video

Updated: Jul 28, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
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Quality Control in Metagenomics Data.

Abraham Gihawi1, Ryan Cardenas1, Rachel Hurst1

  • 1Bob Champion Research & Education Building, Norwich Medical School, University of East Anglia, Norwich, UK.

Methods in Molecular Biology (Clifton, N.J.)
|May 31, 2023
PubMed
Summary
This summary is machine-generated.

Quality control is essential for analyzing metagenomics data. This guide covers study design, data processing with bash and Snakemake, and R-based analysis for reproducible microbiome research.

Keywords:
MetagenomicsMicrobial bioinformatics contaminationMicrobiome bacteriaQuality control dataVirus

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Metagenomics experiments are increasingly common, generating large datasets.
  • Effective processing and quality control are crucial for reliable insights.
  • Unique considerations arise from study design and potential confounding factors.

Purpose of the Study:

  • To outline essential quality control principles for metagenomics data.
  • To introduce practical methods for data processing and analysis.
  • To guide researchers in interpreting taxonomic results within study contexts.

Main Methods:

  • Exploration of study design and confounding factors in metagenomics.
  • Implementation of general sequencing data quality control.
  • Development of data processing pipelines using bash and Snakemake (Python).
  • Statistical analysis of microbiome data in R for identifying relationships and differences.
  • Command-line interrogation of sequence alignments.

Main Results:

  • Demonstration of basic principles for metagenomics quality control and reproducibility.
  • Introduction to reproducible data processing workflows.
  • Walkthrough of microbiome data analysis techniques in R.
  • Guidance on contextualizing taxonomic findings and sequence alignments.

Conclusions:

  • Robust quality control is fundamental for valid metagenomics research.
  • Reproducible workflows enhance the reliability of bioinformatics analyses.
  • Careful data analysis and interpretation are key to extracting meaningful biological insights from metagenomic data.