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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Overview of data preprocessing for machine learning applications in human microbiome research.

Eliana Ibrahimi¹, Marta B Lopes^2,3, Xhilda Dhamo⁴

¹Department of Biology, Faculty of Natural Sciences, University of Tirana, Tirana, Albania.

Frontiers in Microbiology

|October 23, 2023

Summary

This summary is machine-generated.

Analyzing human microbiome sequencing data is challenging due to its unique statistical properties. This review highlights the limited use of appropriate data transformation methods, impacting research reproducibility and comparability.

Keywords:

compositionality data preprocessing human microbiome machine learning metagenomics data normalization

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Last Updated: Jul 12, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Published on: October 15, 2019

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Published on: March 23, 2018

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Published on: August 25, 2018

Area of Science:

Microbiome Research
Bioinformatics
Statistical Analysis

Background:

Metagenomic sequencing is key for microbiome-host interaction studies.
Microbiome data presents unique statistical challenges (sparsity, compositionality).
Standard analysis methods may not adequately address these challenges.

Purpose of the Study:

To review preprocessing and transformation methods in human microbiome studies.
To assess the adoption of methods addressing microbiome data's statistical specificities.
To provide guidance for selecting appropriate data transformation techniques.

Main Methods:

Literature review of recent human microbiome publications.
Analysis of preprocessing and transformation methods reported.
Evaluation of method suitability for microbiome data characteristics.

Main Results:

Limited adoption of transformation methods tailored to microbiome data's statistical features.
Prevalence of relative and normalization-based transformations lacking specific considerations.
Inconsistent or missing information on data preprocessing in many studies.

Conclusions:

Current practices in microbiome data transformation may hinder robust analysis.
Incomplete reporting of methods raises concerns for reproducibility and comparability.
Guidance is needed to select appropriate transformations for microbiome research.