Benchmarking and Validation of a Bioinformatics Workflow for Meat Species Identification Using 16S rDNA Metabarcoding

Area of Science:

• Food Science
• Molecular Biology
• Genetics

Background:

• DNA metabarcoding is increasingly used for authenticating meat products.
• Existing methods for species identification via amplicon sequencing lack standardized comparisons of algorithms and parameter optimization for meat authenticity.
• Many current approaches utilize limited reference sequences, potentially inflating performance estimates.

Purpose of the Study:

• To predict and compare the efficacy of published DNA barcodes for distinguishing taxa within the BLAST NT database.
• To benchmark and optimize a metabarcoding analysis workflow for 16S rDNA Illumina sequencing using a diverse reference dataset.
• To offer guidance on optimal parameters, sequencing depth, and thresholds for meat metabarcoding experiments.

Main Methods:

• Comparative analysis of DNA barcode performance against the BLAST NT database.
• Benchmarking and optimization of a 16S rDNA metabarcoding workflow using 79 reference samples across 32 taxa.
• Development and provision of a publicly available analysis workflow with validation tools.

Main Results:

• Identification of optimal barcodes for distinguishing meat-derived taxa.
• Establishment of a robust and optimized metabarcoding workflow for 16S rDNA.
• Quantification of performance variations based on barcode choice and database subsetting.

Conclusions:

• The study provides a methodical comparison of DNA metabarcoding algorithms and parameters for meat authenticity.
• Recommendations are offered for improving the accuracy and reliability of meat species identification using 16S rDNA sequencing.
• A publicly accessible workflow is available to aid researchers in validating and benchmarking their metabarcoding analyses.