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Updated: Mar 25, 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

MADRe: Strain-level metagenomic classification through assembly-driven database reduction.

Josipa Lipovac¹, Mile Šikić^1,2, Riccardo Vicedomini³

¹Laboratory for Bioinformatics and Computational Biology, Faculty of Electrical Engineering and Computing, University of Zagreb, 3 Unska Street, 10000, Zagreb, Croatia.

|March 23, 2026

Summary

This summary is machine-generated.

MADRe, a novel pipeline, enhances strain-level metagenomic classification by reducing large reference databases. This method improves precision in identifying microbial strains, even with unknown sample compositions.

Keywords:

database reduction metagenomic classification metagenomics strain-level

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Published on: August 20, 2021

Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Strain-level metagenomic classification is crucial for microbial ecology.
Challenges include unknown sample composition and redundant reference databases.
Existing methods struggle with accuracy and scalability.

Purpose of the Study:

To present MADRe, a modular and scalable pipeline for long-read strain-level metagenomic classification.
To introduce Metagenome Assembly-Driven Database Reduction for improved accuracy.
To leverage statistical strategies for enhanced database reduction and read reassignment.

Main Methods:

MADRe combines long-read metagenome assembly with contig-to-reference reassignment.
An expectation-maximization framework is used for reference database reduction.
Probabilistic read mapping reassignment is performed on the reduced database.

Main Results:

MADRe was evaluated on simulated, mock, and real metagenomic datasets.
The pipeline consistently improved precision by reducing false-positive strain detections.
Individual steps of MADRe can be used separately, with the classification step performing comparably to other tools.

Conclusions:

MADRe offers a sensitive and precise approach to strain-level metagenomic classification.
The Metagenome Assembly-Driven Database Reduction strategy effectively addresses database redundancy.
MADRe is an open-source tool available for broader research applications.