The Metabarcoding Analysis Pipeline (MAP): Simple, accurate, and flexible metabarcoding
- 1Centre for Biodiversity Genomics, University of Guelph
Abstract
Current metabarcoding pipelines are inflexible with respect to study design and are poorly suited to long-read sequence data. To address these limitations, we developed MAP, the Metabarcoding Analysis Pipeline, which is a sequence-to-answer workflow supporting the analysis of amplicons from highly multiplexed and replicated study designs. Although MAP can analyze amplicons of any length from any genetic marker, it includes several features tailored to long-read COI metabarcoding. MAP installs from a Docker container and requires only sequence data, a parameters file, and a reference library. It produces intuitive reports, enabling users to evaluate their data immediately after analysis. We validate MAP by showing that it generates biodiversity estimates that correspond closely to a ground-truth dataset of single-specimen DNA barcode data and by demonstrating that it outperforms alternative platforms for COI metabarcoding. MAP is free, open-source, and available from: https://github.com/cbg-innov/MAP.
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BibTeX
@misc{prosser2026metabarcoding,
title = {{The Metabarcoding Analysis Pipeline (MAP): Simple, accurate, and flexible metabarcoding}},
author = {S. W. Prosser and N. W. Bard and K. A. Thompson and R. A. Floyd and S. Padhye and E. Ozsahin and S. Jafarpour and P. D. N. Hebert},
howpublished = {bioRxiv},
year = {2026},
month = jul,
doi = {10.64898/2026.07.22.740107},
url = {https://www.biorxiv.org/content/10.64898/2026.07.22.740107v2},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - The Metabarcoding Analysis Pipeline (MAP): Simple, accurate, and flexible metabarcoding
AU - S. W. Prosser
AU - N. W. Bard
AU - K. A. Thompson
AU - R. A. Floyd
AU - S. Padhye
AU - E. Ozsahin
AU - S. Jafarpour
AU - P. D. N. Hebert
PY - 2026
DA - 2026/07/22
PB - bioRxiv
DO - 10.64898/2026.07.22.740107
UR - https://www.biorxiv.org/content/10.64898/2026.07.22.740107v2
AB - Current metabarcoding pipelines are inflexible with respect to study design and are poorly suited to long-read sequence data. To address these limitations, we developed MAP, the Metabarcoding Analysis Pipeline, which is a sequence-to-answer workflow supporting the analysis of amplicons from highly multiplexed and replicated study designs. Although MAP can analyze amplicons of any length from any genetic marker, it includes several features tailored to long-read COI metabarcoding. MAP installs from a Docker container and requires only sequence data, a parameters file, and a reference library. It produces intuitive reports, enabling users to evaluate their data immediately after analysis. We validate MAP by showing that it generates biodiversity estimates that correspond closely to a ground-truth dataset of single-specimen DNA barcode data and by demonstrating that it outperforms alternative platforms for COI metabarcoding. MAP is free, open-source, and available from: https://github.com/cbg-innov/MAP.
ER -