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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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Multitable Methods for Microbiome Data Integration.

Kris Sankaran1, Susan P Holmes2

  • 1Mila, Universite de MontrĂ©al, MontrĂ©al, QC, Canada.

Frontiers in Genetics
|September 27, 2019
PubMed
Summary
This summary is machine-generated.

Analyzing multiple data types, common in microbiome research, requires diverse methods. This review distills themes and provides workflows for multitable data analysis, regardless of data characteristics.

Keywords:
data integrationdimensionality reductionheterogeneitymicrobiomemultiomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Simultaneous analysis of multiple data types is common in microbiome research (e.g., 16S rRNA, metagenomics, metabolomics, transcriptomics).
  • Numerous methods exist for multitable microbiome data analysis, driven by increasing data availability and new scientific questions.
  • Multitable analysis techniques are also applied in diverse fields like economics, robotics, genomics, chemometrics, and neuroscience, often termed data integration or multi-omic methods.

Purpose of the Study:

  • To distill common themes across various multitable data analysis approaches.
  • To provide concrete workflows for multitable data analysis based on goals and data characteristics (heterogeneity, dimensionality, sparsity).
  • To offer a broader perspective on multitable methods beyond the microbiome field.

Main Methods:

  • Review and synthesis of existing multitable analysis methodologies.
  • Categorization of approaches based on underlying data structure and analysis objectives.
  • Development of practical workflows adaptable to different data types and research questions.

Main Results:

  • Identification of recurring themes and principles in multitable data analysis across disciplines.
  • Framework for selecting appropriate analysis strategies based on data properties and research aims.
  • Code and figures for analysis and visualization are publicly available.

Conclusions:

  • A unified understanding of multitable analysis methods can benefit microbiome research.
  • Tailoring analysis workflows to specific data characteristics and research goals is crucial for effective insights.
  • The provided workflows and code facilitate reproducible and robust multitable data analysis.