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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

Comparison of microbiome samples: methods and computational challenges.

Matteo Comin¹, Barbara Di Camillo¹, Cinzia Pizzi¹

¹University of Padova.

Briefings in Bioinformatics

|June 25, 2020

Summary

This summary is machine-generated.

Comparing metagenomic next-generation sequencing data is vital for understanding microbial communities. This review covers challenges in species identification, sample comparison, and phenotype association, discussing reference-based and reference-free solutions.

Keywords:

metagenomics microbiome next-generation sequencing

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Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Metagenomic next-generation sequencing (mNGS) is crucial for studying microbial communities.
Comparing mNGS datasets is essential for ecological and clinical insights.
Several computational methods have been developed for mNGS data analysis.

Purpose of the Study:

To review key challenges in comparing mNGS datasets.
To present current solutions for these challenges.
To discuss both reference-based and reference-free approaches.

Main Methods:

Review of existing literature and computational methods for mNGS data comparison.
Categorization of challenges into species identification/quantification, distance computation, and phenotype association.
Analysis of reference-based and reference-free methodologies.

Main Results:

Identified three primary challenges in mNGS data comparison.
Detailed current solutions for species identification and quantification.
Highlighted methods for efficient computation of distances between metagenomic samples.
Discussed approaches for identifying metagenomic features linked to phenotypes, like disease status.

Conclusions:

Effective comparison of mNGS data requires addressing species identification, distance computation, and phenotype association.
Both reference-based and reference-free methods offer viable solutions.
Continued development in bioinformatics is essential for advancing metagenomic research.