PRONAME: A User-Friendly Pipeline for Long-Read Nanopore Metabarcoding Data
Author Information
Author(s): Benjamin Dubois, Mathieu Delitte, Salomé Lengrand, Claude Bragard, Anne Legrève, Frédéric Debode
Primary Institution: Walloon Agricultural Research Centre, Gembloux, Belgium
Hypothesis
Can a new bioinformatics pipeline improve the accuracy of long-read nanopore sequencing data processing?
Conclusion
PRONAME significantly enhances the accuracy and versatility of long-read nanopore data processing, achieving consensus sequences with at least 99.5% accuracy.
Supporting Evidence
- PRONAME achieved consensus sequences with at least 99.5% accuracy using standard settings.
- The pipeline is user-friendly and does not require installation, as it is provided as a Docker image.
- PRONAME includes precompiled databases for 16S sequences and allows for custom database input.
- Error-correction strategies in PRONAME significantly improve sequence accuracy compared to existing pipelines.
- PRONAME is adaptable for various biological taxa, enhancing its applicability in diverse research contexts.
Takeaway
PRONAME is a tool that helps scientists analyze DNA from many different species more accurately, making it easier to understand the diversity of life.
Methodology
The PRONAME pipeline processes raw Nanopore sequencing data using scripts for importing, filtering, refining, and taxonomic analysis.
Limitations
The pipeline's performance may vary based on the clustering threshold chosen, affecting species identification and computation time.
Participant Demographics
The study involved a mock community of 17 Gram-positive and Gram-negative bacterial species.
Digital Object Identifier (DOI)
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