RESOURCES
EUREMAP shares the tools and materials developed through the project so that researchers, partners and the wider marine bioprospecting community can put them to use. The MGnify BGCs platform offers standardised predictions of biosynthetic gene clusters from metagenomic data, helping users explore the biosynthetic potential of marine and other environmental microbiomes. The EUREMAP Data Management Book guides researchers through FAIR, open science practices, from sample registration to data deposition.
This section also gives access to the EUREMAP Visual Identity Guide, which sets out how to use the project’s logo, colours and graphic elements consistently across presentations, publications and social media.
MGnify biosynthetic gene clusters platform
Uncovering nature’s chemical blueprints
Biosynthetic gene clusters (BGCs) are the genetic instructions behind many of the natural products found in marine and other environmental organisms — including antibiotics, pigments, and other bioactive compounds of interest to health and food research. Identifying these clusters at scale, however, has traditionally required significant computational expertise and resources.
To address this, EUREMAP partner MGnify (EMBL-EBI) has developed the MGnify BGCs platform, a dedicated resource for exploring, analysing, and retrieving BGC predictions from metagenomic assemblies. The platform brings together three leading detection tools — antiSMASH, GECCO, and SanntiS — into a single standardised pipeline, making it possible to compare results across studies and identify consensus predictions with greater confidence.
Built for both researchers and non-specialists, MGnify BGCs offers an accessible analytics interface alongside a full API for advanced data queries, opening up a rich dataset of natural product potential to the wider scientific and industry community. It forms part of the broader MGnify resource, which provides automated pipelines for analysing microbiome data and determining the taxonomic and functional diversity of environmental samples.
By making standardised, cross-validated BGC predictions freely available, this partnership directly supports EUREMAP’s mission: unlocking the biosynthetic potential of marine organisms to discover new bioactive compounds for health and food applications.
The biosynthetic classes
TWe group BGCs by the chemestry of the molecule they encode, following the classes used by the MIBiG reference repository. Some clusters combine chemistries; we label these as hybrids. (Class definitions follow Hanson, J.R.(2003), Natural Products: The Secondary Metabolites (Vol. 17), Royal Society of Chemistry; and Kautsar, S.A. et al. (2020) MIBiG 2.0, Nucleic Acids Research, 48(D1), D454-D458.C)
Image: Terpenes used for chiral pool synthesis. (T. Gaich, J. Mulzer 2012)
Composed of isoprene (C5) units derived from isopentenyl pyrophosphate
Nitrogen-containing compounds derived from amino acids (e.g., ornithine, lysine, tyrosine, tryptophan)
Image: (Wang, S., Lin, S., Fang, Q. et al. A ribosomally synthesised and post-translationally modified peptide containing a β-enamino acid and a macrocyclic motif. Nat Commun 13, 5044 (2022).)
Ribosomally synthesised and Post-translationally modified Peptide
Image: The structure of cyclosporine. (D.K. Daley, K.J. Brown, S. Badal, Chapter 20 – Fungal Metabolites, Editor(s): Simone Badal, Rupika Delgoda, Pharmacognosy, Academic Press, 2017, Pages 413-421, ISBN 9780128021040, https://doi.org/10.1016/B978-0-12-802104-0.00020-2. )
Nonribosomal Peptide
Image: Carbohydrates (Girija S. Singh, Chapter Thirteen – Carbohydrate-based antibiotics: Opportunities and challenges, Editor(s): Vinod Kumar Tiwari, Carbohydrates in Drug Discovery and Development, Elsevier, 2020, Pages 523-559, ISBN 9780128166758, https://doi.org/10.1016/B978-0-12-816675-8.00013-0.)
Carbohydrate-based natural products (e.g., aminoglycoside antibiotics)
Image: cyclitols (J.P. Kamerling, 1.01 – Basics Concepts and Nomenclature Recommendations in Carbohydrate Chemistry, Editor(s): Hans Kamerling, Comprehensive Glycoscience, Elsevier, 2007, Pages 1-38, ISBN 9780444519672, https://doi.org/10.1016/B978-044451967-2/00001-5.)
Catch-all class for clusters encoding metabolites outside main classes (e.g. cyclitols, indolocarbazoles, and phosphonates)
Image: Diterpene and triterpene alkaloids. (Brocksom, Timothy & Oliveira, Kleber & Desiderá, André. (2017). The Chemistry of the Sesquiterpene Alkaloids. Journal of the Brazilian Chemical Society. 10.21577/0103-5053.20170049. )
Clusters combining two or more classes; Polyketide + NRP is flagged separately
The datasets collection
- MIBiG 4.0 – experimentally characterised, published BGCs; the reference set of clusters.
- BacDive – marine bacterial genomes from type strains.
- MGnify Marine Genome Catalogues – genomes assembled from marine water-column and sediment metagenomes.
- MGnify Assembles (v5) – assembled contigs from marine-related metagenomes.
BGC prediction and classification
Three detectors (antiSMASH, GECCO, and SanntiS) annotable each dataset. We merge overlapping predictions on the same genomic interval into one integrated BGC. We derive the integrated BGC class by consensus across detectors. Each detector’s class prediction is collapsed to the shared vocabulary above and the calls are pooled. A single agreed class wins outright. A Polyketide + NRP combination is flagged as that specific hybrid. Any other multi-class combination becomes a general hybrid. Clusters with no usable signal default to Other.
Tree calculation
Trees were calculated using the CommonTree tool of the NCBI taxonomy datebase. Synonyms were resolved as directed by the CommonTree tool, and other unresolved taxa removed from the tree (this included many BGCs resolving to Candidatus phyla in Bacteria).
EUREMAP STRAINS
Strains developed for the EUREMAP Biomass Production Service
In the context of the EUREMAP demo projects, we developed facilities and protocols towards the production of microbial biomass required for the discovery and testing of bioactive compounds from three transconjugant strains of Streptomyces coelicolor and a wild strain of Streptomyces.
Streptomyces sp. CA-237351. This strain produces novel Streptocyclinones analogous to compounds that ameliorate Alzheimer’s disease pathological processes in vitro.
- S. coelicolor P08-G05_c16. This strain is producing a novel compound with activity against MRSA and E. faecium.
- S. coelicolor P01-F02_c14. production of compounds not produced by the host
- S. coelicolor P01-F02_c15. production of compounds not produced by the host
EUREMAP DATA MANAGEMENT
EUREMAP data management book
To help researchers navigate how data are handled across the EUREMAP pipeline, the EUREMAP Data Management Book offers a short, practical guide available on GitHub. Built around ELIXIR’s contribution to the European Open Science Cloud (EOSC), the guide walks through EUREMAP’s data management workflow: from linking every sample and dataset to a project’s BioProject/Study accession, to registering sample metadata in BioSamples, depositing data in the appropriate repository (e.g. EMBL-ENA for sequence data, MetaboLights for metabolomics), and tying it all together under a single BioStudies accession. A quick-start section takes users step by step through open science principles, drafting a Data Management Plan, and submitting through EMBL-EBI’s Webin Portal. Designed to be read in about 20 minutes, it’s a practical companion for anyone submitting data through the EUREMAP Marine Bioprospecting Pipeline.
EUREMAP Visual guide book
Visual ID guide
Help us keep the EUREMAP name and look consistent wherever the project appears. Our visual identity guide sets out how to use the EUREMAP logo, colours, typography and graphic elements, so that partners, collaborators and anyone communicating about the project can represent it clearly and recognisably.
Whether you are preparing a presentation, a poster, a publication or a social media post, the guide will help you get it right. We kindly ask that the EUREMAP identity is used as described in the guide and that the logo is not altered, recoloured or distorted.
