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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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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...
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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization.

Yang Cao1, Qingyang Dong1, Dan Wang2

  • 1Department of Environmental Medicine, Tianjin Institute of Environmental and Operational Medicine, Tianjin 300050, China.

Bioinformatics (Oxford, England)
|June 30, 2022
PubMed
Summary

The microbiomeMarker R package aids in identifying microbial biomarkers for precision medicine. It offers various analysis methods and visualization tools for microbiome data.

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Area of Science:

  • Microbiome research
  • Bioinformatics
  • Computational biology

Background:

  • Microbiome profiles are crucial for translational and precision medicine.
  • Identifying reliable microbiome biomarkers is essential for clinical applications.

Purpose of the Study:

  • To introduce microbiomeMarker, an R/Bioconductor package for microbiome marker identification.
  • To provide a scalable and extensible tool for analyzing microbiome data.

Main Methods:

  • Implementation of normalization and differential analysis (DA) methods.
  • Integration of three supervised learning models for marker identification.
  • Inclusion of functions for comparing DA methods and confounder analysis.

Main Results:

  • microbiomeMarker facilitates the identification and interpretation of microbiome markers.
  • The package supports standardized input/output for seamless integration with other tools.
  • It enables comparison of different analytical approaches for microbiome data.

Conclusions:

  • microbiomeMarker offers a comprehensive solution for microbiome biomarker discovery.
  • The package enhances the potential of microbiome profiling in translational and precision medicine.
  • Its design promotes scalability, extensibility, and user-friendly interpretation of results.