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Updated: Jun 2, 2026

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

Computational challenges of sequence classification in microbiomic data.

Paolo Ribeca¹, Gabriel Valiente

¹Spanish National Center for Genomic Analysis (CNAG), Barcelona, Spain.

Briefings in Bioinformatics

|April 21, 2011

Summary

This summary is machine-generated.

Next-generation sequencing enables culture-independent study of microbial communities. This research evaluates computational tools for analyzing 16S ribosomal RNA metagenomic data, assessing their impact on accuracy and performance in species assignment.

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Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

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Last Updated: Jun 2, 2026

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Next-generation sequencing (NGS) revolutionized microbiology by enabling culture-independent analysis of microbial communities.
Metagenomic data analysis presents challenges due to complex mixtures of species, often with unsequenced genomes.

Purpose of the Study:

To survey computational challenges in characterizing and quantifying microbial samples using 16S ribosomal RNA (rRNA) metagenomic data.
To evaluate the impact of high-throughput analysis tools on result quality.
To assess the performance of established methods for assigning sequence reads to microbial species.

Main Methods:

Analysis of 16S rRNA metagenomic data using established and new high-throughput computational tools.
Comparison of sequence read assignment performance with and without taxonomic information.
Evaluation of computational challenges in characterizing and quantifying microbial communities.

Main Results:

The study surveys computational challenges in analyzing 16S rRNA metagenomic data for microbial community characterization.
It examines the trade-offs between high-throughput analysis tools and result quality.
Performance of various methods for microbial species assignment is assessed.

Conclusions:

Accurate characterization and quantification of microbial communities from metagenomic data require careful selection and evaluation of computational tools.
The study highlights the importance of assessing tool performance, especially when dealing with complex, unsequenced microbial samples.