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Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

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A detailed workflow to develop QIIME2-formatted reference databases for taxonomic analysis of DNA metabarcoding data.

Benjamin Dubois¹, Frédéric Debode², Louis Hautier³

¹Life Sciences Department, Bioengineering Unit, Walloon Agricultural Research Center, Chaussée de Charleroi 234, 5030, Gembloux, Belgium. b.dubois@cra.wallonie.be.

BMC Genomic Data

|July 8, 2022

Summary

This study introduces DB4Q2, a workflow for creating custom DNA metabarcoding reference databases for QIIME2. It simplifies database development for ITS2 and rbcL sequences, improving taxonomic assignment accuracy.

Keywords:

Bioinformatics workflow High-throughput sequencing ITS2 Metabarcoding Plant QIIME2 Reference database rbcL

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Area of Science:

Bioinformatics
Molecular Ecology
Genomics

Background:

DNA metabarcoding is crucial for analyzing taxa composition.
High-throughput sequencing generates vast data, necessitating bioinformatics workflows like QIIME2.
Limited pre-formatted reference databases hinder custom database development.

Purpose of the Study:

To present a detailed workflow for developing custom reference databases for any barcode sequence.
To address bottlenecks in creating curated reference databases.
To provide a user-friendly procedure for database development.

Main Methods:

Developed DB4Q2, a workflow for creating plant reference databases (ITS2, rbcL).
Included filtering steps for fungal and misidentified sequences.
Ensured flexibility in sequence processing and avoided downloading issues.

Main Results:

DB4Q2 facilitates the development of custom plant reference databases.
Databases created with DB4Q2 demonstrated strong performance compared to existing datasets.
The workflow is accessible to users with limited bioinformatics expertise.

Conclusions:

DB4Q2 provides a detailed procedure for custom reference database development for QIIME2 and other platforms.
Ready-to-use plant ITS2 and rbcL databases with assessed accuracy are provided.
This work enhances taxonomic analysis capabilities in metabarcoding studies.