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Related Concept Videos

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

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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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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
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Related Experiment Video

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Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
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Contamination detection and microbiome exploration with GRIMER.

Vitor C Piro1,2, Bernhard Y Renard1

  • 1Data Analytics and Computational Statistics, Hasso Plattner Insititute, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany.

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|March 30, 2023
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Summary
This summary is machine-generated.

Detecting contamination in microbiome studies is crucial for accurate results. GRIMER is a new tool that automates analysis and provides interactive dashboards to help identify and remove contaminants, especially in low-biomass samples.

Keywords:
ContaminationMicrobiomeTaxonomyVisualization

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Contamination detection is critical in microbiome studies to prevent biased results.
  • Identifying and removing contaminants is challenging, particularly in low-biomass samples or studies lacking controls.
  • Interactive platforms and external evidence aid in detecting and mitigating contamination.

Purpose of the Study:

  • To introduce GRIMER, a novel tool for automated microbiome contamination detection.
  • To provide an interactive dashboard for visualizing and analyzing potential contamination.
  • To integrate multiple evidence sources for robust contaminant identification.

Main Methods:

  • GRIMER performs automated analyses on contingency tables, independent of quantification methods.
  • It generates a portable, interactive dashboard integrating annotation, taxonomy, and metadata.
  • The tool utilizes an extensive list of known common contaminants (210 genera, 627 species).

Main Results:

  • GRIMER offers automated analysis and an interactive dashboard for contamination detection.
  • The tool integrates various data sources and external contaminant information.
  • Reports are generated quickly and are accessible to non-specialists.

Conclusions:

  • GRIMER facilitates visual data exploration and analysis for microbiome contamination detection.
  • The open-source tool and data are available for broader use.
  • GRIMER supports the identification and mitigation of contaminants in microbiome research.