Commentary | Open | Published:

Minimum Information about a Biosynthetic Gene cluster

Nature Chemical Biology volume 11, pages 625631 (2015) | Download Citation

A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.

Living organisms produce a range of secondary metabolites with exotic chemical structures and diverse metabolic origins. Many of these secondary metabolites find use as natural products in medicine, agriculture and manufacturing. Research on natural product biosynthesis is undergoing an extensive transformation, driven by technological developments in genomics, bioinformatics, analytical chemistry and synthetic biology. It has now become possible to computationally identify thousands of biosynthetic gene clusters (BGCs) in genome sequences, and to systematically explore and prioritize them for experimental characterization1,2. A BGC can be defined as a physically clustered group of two or more genes in a particular genome that together encode a biosynthetic pathway for the production of a specialized metabolite (including its chemical variants). It is becoming possible to carry out initial experimental characterization of hundreds of such natural products, using high-throughput approaches powered by rapid developments in mass spectrometry3,4,5 and chemical structure elucidation6. At the same time, single-cell sequencing and metagenomics are opening up access to new and uncharted branches of the tree of life7,8,9, enabling scientists to tap into a previously undiscovered wealth of BGCs. Furthermore, synthetic biology allows the redesign of BGCs for effective heterologous expression in preengineered hosts, which will ultimately empower the construction of standardized high-throughput platforms for natural product discovery10,11.

In this changing research environment, there is an increasing need to access all the experimental and contextual data on characterized BGCs for comparative analysis, for function prediction and for collecting building blocks for the design of novel biosynthetic pathways. For this purpose, it is paramount that this information be available in a standardized and systematic format, accessible in the same intuitive way as, for example, genome annotations or protein structures. Currently, the situation is far from ideal, with information on natural product biosynthetic pathways scattered across hundreds of scientific articles in a wide variety of journals; it requires in-depth reading of papers to confidently discern which of the molecular functions associated with a gene cluster or pathway have been experimentally verified and which have been predicted solely on the basis of biosynthetic logic or bioinformatic algorithms. Although some valuable existing manually curated databases have data models in place to store some of this information12,13,14, all are specialized towards certain subcategories of BGCs and include just a limited number of parameters defined by the interests of a subset of the scientific community. To enable the future development of databases with universal value, a generally applicable community standard is required that specifies the exact annotation and metadata parameters agreed upon by a wide range of scientists, as well as the possible types of evidence that are associated with each variable in publications and/or patents. Such a standard will be of great value for the consistent storage of data and will thus alleviate the tedious process of manually gathering information on BGCs. Moreover, a comprehensive data standard will allow future data infrastructures to enable the integration of multiple types of data, which will generate new insights that would otherwise not be attainable.

The Genomic Standards Consortium (GSC)15 (Box 1) previously developed the Minimum Information about any Sequence (MIxS) framework16. This extensible 'minimum information' standardization framework includes the Minimum Information about a Genome Sequence (MIGS)17 and the Minimum Information about a MARKer gene Sequence (MIMARKS)16 standards. MIxS is a flexible framework that can be expanded upon to serve a wide variety of purposes. The GSC facilitates the community effort of maintaining and extending MIxS, and stimulates compliance among the community.

Box 1: The Genomic Standards Consortium and its MIxS framework

The Genomic Standards Consortium (GSC, was founded in 2005 as an open-membership working body with the purpose of promoting the standardization of genome descriptions as well as the exchange and integration of genomic data.

The GSC initiates and coordinates the design of and compliance with several minimum information standards (also known as checklists). An overarching framework has been designed to connect and standardize these checklists themselves: the Minimum Information about any (x) Sequence (MIxS)16. MIxS consists of three layers.

First, the MIxS standard includes a number of shared descriptors that are relevant to all types of nucleotide sequences, such as collection date, environmental origin, geographical coordinates of the location of origin of the sample and sequencing method.

Second, for a wide range of different environmental origins, so-called 'environmental packages' are available that constitute checklists of measurements and observations that are specific to each environment: for example, for host-associated microbial DNA samples, the taxonomy of the host, the habitat of the host and several phenotypic characteristics of the host can be collected. In this manner, rich contextual information on the context of each microbial sample is stored.

Third, several checklists are available for specific sequence types, each having their own checklist-specific descriptors. Previous checklists include the Minimum Information about a Genome Sequence (MIGS)17, Minimum Information about a Metagenome Sequence (MIMS)17 and Minimum Information about a MARKer gene Sequence (MIMARKS)16.

In spring 2013, the MIBiG project proposal was accepted by the board of the GSC to form a new checklist within the MIxS framework. Besides a number of general descriptors, it also includes pathway type–specific packages that function analogously, for different classes of biosynthetic pathways, to the way the MIxS environmental packages do for different environmental origins.

Here, we introduce the “Minimal Information about a Biosynthetic Gene cluster” (MIBiG) specification as a coherent extension of the GSC's MIxS standards framework. MIBiG provides a comprehensive and standardized specification of BGC annotations and gene cluster–associated metadata that will allow their systematic deposition in databases. Through a community annotation of BGCs that have been experimentally characterized and described in the literature during previous decades, we have constructed an MIBiG-compliant seed dataset. Moreover, a large part of the research community has committed to continue submitting data on newly characterized gene clusters in the MIBiG format in the future. Together, the MIBiG standard and the resulting MIBiG-compliant data sets will allow data infrastructures to be developed that will facilitate key future developments in natural product research.

Design of the MIBiG standard

The MIBiG standard covers general parameters that are applicable to each and every gene cluster as well as compound type–specific parameters that apply only to specific classes of pathways (Fig. 1). Notably, the standard has been designed to be suitable for biosynthetic pathways from any taxonomic origin, including those from bacteria, archaea, fungi and plants.

Figure 1: Schematic overview of the MIBiG standard.
Figure 1

The MIBiG standard is composed of general and compound class–specific parameters. Wherever relevant, evidence coding is used to indicate the experimental support for items in the checklist. Fields annotated with an asterisk are absolutely mandatory; fields with two asterisks are conditionally mandatory.

The general parameters cover important data items that are universally applicable. First, they include identifiers of the publications associated with the characterization of the gene cluster, so that the full description of the experimental results that support the entire entry can be accessed easily.

The second key group of general parameters describes the associated genomic locus (or loci) and its accession numbers and coordinates, as deposited in or submitted to one of the databases of the International Nucleotide Sequence Database Collaboration (INSDC): the DNA Data Bank of Japan (DDBJ), the European Nucleotide Archive (EBI-ENA) or GenBank, all of which share unified accession numbers. The INSDC accession numbers are also used to link each MIBiG entry (which is given a separate MIBiG accession number) and its annotations to the corresponding nucleotide sequence(s) computationally; hence, a GenBank/ENA/DDBJ submission of the underlying nucleotide sequence is always required to file a MIBiG submission.

The third group of general parameters describes the chemical compounds produced from the encoded pathway, including their structures, molecular masses, biological activities and molecular targets. Additionally, these parameters allow documentation of miscellaneous chemical moieties that are connected to the core scaffold of the molecule (but synthesized independently) and the genes associated with their biosynthesis; this will facilitate the design of tools for the straightforward comparison of such 'sub-clusters', which are frequently present in different variants across multiple parent BGCs.

Finally, there is a group of general parameters describing experimental data on genes and operons in a gene cluster, including gene knockout phenotypes, experimentally verified gene functions and operons verified by techniques such as RNA-seq.

Beside the general parameters, the MIBiG standard contains dedicated class-specific checklists for gene clusters encoding pathways to produce polyketides, nonribosomal peptides (NRPs), ribosomally synthesized and post-translationally modified peptides (RiPPs), terpenes, saccharides and alkaloids. These include items such as acyltransferase domain substrate specificities and starter units for polyketide BGCs, release/cyclization types and adenylation domain substrate specificities for NRP BGCs, precursor peptides and peptide modifications for RiPP BGCs, and glycosyltransferase specificities for saccharide BGCs. Where applicable, the standard was made compliant with earlier community agreements, such as the recently published classification of RiPPs18. Hybrid BGCs that cover multiple biochemical classes can be described by simply entering information on each of the constituent compound types: the checklists have been designed in such a way that this does not lead to conflicts. Importantly, the modularity of the checklist system allows for the straightforward addition of further class-specific checklists when new types of molecules are discovered in the future.

The combination of general and compound-specific MIBiG parameters, together with the MIxS checklist, provides a complete description of the chemical, genomic and environmental dimensions that characterize a biosynthetic pathway (Fig. 2). A minimal set of key parameters is mandatory, while other parameters are optional. For many parameters, a specific ontology has been designed in order to standardize the inputs and to make it easier to categorize and search the resulting data.

Figure 2: An example MIBiG entry, describing the relatively simple hybrid NRPS-PKS biosynthetic gene cluster for isoflavipucine/dihydroisoflavipucine from Aspergillus terreus.
Figure 2

Fields without information have been omitted, and some JSON field abbreviations have been modified for clarity. The full entry is available from

Whenever possible, parameters are linked to a system of evidence attribution that specifies the kinds of experiments performed to arrive at the conclusions indicated by the chosen parameter values. Hence, each annotation entered during submission is assigned a specific evidence code: for example, when annotating the substrate specificity of a nonribosomal peptide synthetase (NRPS) adenylation domain, the submitter can choose between 'activity assay', 'structure-based inference' and 'sequence-based prediction' as evidence categories to support a given specificity.

During the design of the standard, great care was taken to make it compatible with unusual biosynthetic pathways, such as branched or module-skipping polyketide synthase (PKS) and NRPS assembly lines. Also, to ensure that the standard is compliant with the current state of the art in the various subfields of natural product research, we conducted an online community survey at an early stage of standard development (see Supplementary Data Set 1). Feedback was provided by 61 principal investigators from 16 different countries (most of whom also coauthored this paper), including at least ten leading experts for each major class of biosynthetic pathways covered.

Addressing key research needs

Adoption of the MIBiG standard will allow the straightforward collation of all annotations and experimental data on each BGC, which would otherwise be dispersed across multiple scientific articles and resources. Moreover, there are at least three additional key ways in which MIBiG will facilitate new scientific and technological developments: it will enable researchers to systematically connect genes to chemistry (and vice versa), to better understand secondary metabolite biosynthesis and the compounds produced in their ecological and environmental context, and to effectively use synthetic biology to engineer newly designed BGC configurations underpinned by an evidence-based parts registry (Fig. 3).

Figure 3: The MIBiG data standard and submission system will lead to a continuously growing dataset (stored in the online MIBiG repository) that will be loaded into several databases and web services.
Figure 3

The lower part of the figure shows the threefold potential of MIBiG for the study of BGCs, which will make it possible to (1) systematically connect genes and chemistry by identifying which genes are responsible for the biosynthesis of which chemical moieties; (2) understand the natural genetic diversity of BGCs within their environmental and ecological context, by combining MIBiG- and MIxS-derived metadata sets; and (3) develop an evidence-based parts registry for engineering biosynthetic pathways and gene clusters through synthetic biology.

First, the comprehensive dataset generated through MIBiG-compliant submissions will enable researchers to systematically connect genes and chemistry. Not only will it allow individual researchers to predict enzyme functions by comparing enzyme-coding genes in newly identified BGCs to a thoroughly documented dataset, it will also facilitate general advances in chemistry predictions. Substrate specificities of PKS acyltransferase domains and NRPS adenylation domains, as well as their evidence codes, will be registered automatically for all gene clusters. This will enable automated updating of the training sets for key chemistry prediction algorithms19,20,21, which can then be curated by the degree of evidence available, increasing the accuracy of predictions of core peptide and polyketide scaffolds. Also, because groups of genes associated with the biosynthesis of specific chemical moieties (such as sugars and nonproteinogenic amino acids) will be registered consistently, a continuously growing dataset of such sub-clusters will be available to use as a basis for chemical structure predictions.

In addition, MIBiG has the potential to greatly enhance the understanding of secondary metabolite biosynthesis in its ecological and environmental context: the connection of MIBiG to the MIxS standard should stimulate researchers to supply MIxS data on the genome and metagenome sequences that contain the BGCs. This will generate opportunities for a range of analyses, such as the biogeographical mapping of secondary metabolite biosynthesis22, thereby identifying locations and ecosystems harboring rich biosynthetic diversity. But even if the contextual data associated with the genome sequences cannot always be made MIxS compliant (perhaps because the origin of a strain can no longer be traced), the MIBiG standard itself provides a comprehensive reference dataset for annotating large-scale MIxS-compliant metagenomic data from projects such as the Earth Microbiome Project23, Tara Oceans24 and Ocean Sampling Day25. This will enable scientists to obtain a better understanding of the distribution of BGCs in the environment. Altogether, the standard will play a significant role in guiding sampling efforts for future natural product discovery.

Finally, the data resulting from MIBiG-compliant submissions will provide an evidence-based parts registry for the engineering of biosynthetic pathways. Synthetic biologists need a toolbox containing genetic parts that have been experimentally characterized. The MIBiG standard, through its systematic annotation of gene function by evidence coding, knockout mutant phenotypes and substrate specificities, will streamline the identification of all available candidate genes and proteins available to perform a desired function, together with the pathway context in which they natively occur. In this manner, it will provide a comprehensive catalog of parts that can be used for the modification of existing biosynthetic pathways or the de novo design of new pathways.

Community annotation effort

To accelerate the usefulness of new MIBiG-compliant data submissions, we initiated this project by annotating a significant portion of the experimental data on the hundreds of BGCs that have been characterized in recent decades. The resulting data will allow immediate contextualization of new submissions (see below) and comparative analysis of any newly characterized BGCs with a rich source of MIBiG-compliant data. Moreover, this annotation effort offered an ideal opportunity to evaluate the MIBiG standard in practice on a diverse range of BGCs. Hence, we carefully mined the literature to obtain a set of 1,170 experimentally characterized gene clusters: 303 PKS, 189 NRPS, 147 hybrid NRPS-PKS, 169 RiPP, 78 terpene, 123 saccharide, 21 alkaloid and 140 other BGCs. Compared to the 288 BGCs currently deposited in ClusterMine36012 and the 103 BGCs deposited in DoBISCUIT14, this presents a significant advance in terms of comprehensiveness. We then annotated each of these 1,170 BGCs with a minimal number of parameters (genomic locus, publications, chemical structure and biosynthetic class and subclass). Subsequently, in a community initiative involving 81 academic research groups and several companies worldwide, we performed a fully MIBiG-compliant reannotation of 405 of these BGCs according to the information available in earlier publications and laboratory archives. (All participants of this annotation effort are either listed as coauthors of this article or mentioned in the Acknowledgments, depending on the size of their contribution.) An initial visualization of the full data set arising from this reannotation is publicly available online at Altogether, these submitted entries will function as a very useful seed dataset for the development of databases on secondary metabolism. Future data curation efforts will strive to achieve a fully MIBiG-compliant annotation of the remaining 765 BGCs that are currently annotated with a more restricted set of parameters.

Planned implementation

To allow straightforward and user-friendly access, the MIBiG standard will be implemented by multiple databases and web services for genome data and secondary metabolite research. For example, the MIBiG-curated dataset has already been integrated into the antiSMASH tool in the form of a new module26 that compares any identified BGCs with the full MIBiG-compliant dataset of known BGCs. Moreover, a full-fledged database is currently under development that will be tightly integrated with antiSMASH and will build on the previously published ClusterMine360 framework12. Additionally, MIBiG-compliant data will be integrated into the recently released Integrated Microbial Genomes Atlas of Biosynthetic Clusters (IMG-ABC) database from the Joint Genome Institute ( Regular exchange of data will take place between the MIBiG repository and the IMG-ABC, antiSMASH and ClusterMine databases. Additional cross-links with the chemical databases ChemSpider28, chEMBL29 and chEBI30 are being developed so that researchers can easily find the full MIBiG annotation of the BGC responsible for the biosynthesis of given molecules. Finally, all community-curated data are freely available and downloadable in JSON format for integration into other software tools or databases, without any need to request permission, as long as the source is acknowledged.

For submission of new MIBiG-compliant data by scientists in the field, we prepared an interactive online submission form (available from, which was extensively tested through the community annotation effort. Data can also be submitted through the BioSynML plug-in26 ( that was recently built for use in the Geneious software. In this way, MIBiG-compliant data can easily be integrated with the in-house BGC content management systems of individual laboratories or companies. Finally, it will be possible to submit updates to existing MIBiG entries based on peer-reviewed articles through dedicated web forms.

Future perspectives

The MIBiG coordinating team within the GSC is committed to ensuring the continued support and curation of the MIBiG standard, in cooperation with its partners. Compliance with the standard and interoperability with other standards and databases will also be guaranteed within the GSC. In order to stay relevant and viable, MIBiG is projected to be a 'living' standard: updates will be made as needed to remain technologically and scientifically current.

Coordination with relevant journals will be sought to make MIBiG submission of BGCs (evidenced by MIBiG accession codes) a standard item to check during manuscript review. To stimulate submission of MIBiG data during the process of publishing new biosynthetic gene clusters, unique MIBiG accession numbers are provided for each BCG that can be used during article review (including for data embargoed until after publication). The research community represented by this paper commits itself to submitting MIBiG-compliant data sets as well as updates to existing entries when publishing new experimental results on BGCs. We encourage the larger community to join in this endeavor.


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M.H.M. was supported by a Rubicon fellowship of the Netherlands Organization for Scientific Research (NWO; Rubicon 825.13.001). The work of R.K. was supported by the European Union's Seventh Framework Programme (Joint Call OCEAN.2011–2: Marine microbial diversity—new insights into marine ecosystems functioning and its biotechnological potential) under the grant agreement no. 287589 (Micro B3). M.C. was supported by a Biotechnology and Biological Sciences Research Council (BBSRC) studentship (BB/J014478/1). The GSC is supported by funding from the Natural Environment Research Council (UK), the National Institute for Energy Ethics and Society (NIEeS; UK), the Gordon and Betty Moore Foundation, the National Science Foundation (NSF; US) and the US Department of Energy. The Manchester Synthetic Biology Research Centre, SYNBIOCHEM, is supported by BBSRC/Engineering and Physical Sciences Research Council (EPSRC) grant BB/M017702/1. We thank P. d'Agostino, P.R. August, R. Chau, C.D. Deane, S. Diethelm, L. Fernandez-Martinez, A. El Gamal, C. Garcia De Gonzalo, T.H. Grossman, C.-J. Huang, S. Kodani, A.L. Leandrini, I.A. MacNeil, M. Metelev, E.M. Molly, C. Olano, M. Ortega, L. Ray, K. Reynolds, A. Ross, I.N. Silva, R. Teufel, G. Thibodeaux, J. Tietz and D. Widdick for their contributions in the community annotation. We thank R. Baltz, M. Bibb, C. Boddy, C. Corre, E. Dittmann, H. Gramajo, N. Ichikawa, H. Ikeda, P. Jensen, C. Khosla, R. Li, M. Marahiel, D. Mohanty, C. Moore, W. Nierman, D.-C. Oh, E. Schmidt, Y. Shen, D. Stevens, B. Tudzynski and S. Van Lanen for useful comments on an early draft version of the community standard. We are grateful to three anonymous referees for their constructive suggestions.

Author information

Author notes

    • Marnix H Medema

    Present address: Bioinformatics Group, Wageningen University, Wageningen, the Netherlands.


  1. Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Bremen, Germany.

    • Marnix H Medema
    • , Renzo Kottmann
    • , Pelin Yilmaz
    •  & Frank Oliver Glöckner
  2. Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester, UK.

    • Matthew Cummings
    • , Rainer Breitling
    •  & Eriko Takano
  3. Laboratory of Genetically Encoded Small Molecules, Howard Hughes Medical Institute, The Rockefeller University, New York, New York, USA.

    • John B Biggins
    •  & Sean F Brady
  4. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark.

    • Kai Blin
    • , Hyun Uk Kim
    • , Jens Nielsen
    •  & Tilmann Weber
  5. Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, Wageningen, the Netherlands.

    • Irene de Bruijn
    •  & Jos M Raaijmakers
  6. Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, California, USA.

    • Yit Heng Chooi
    •  & Yi Tang
  7. Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California, USA.

    • Yit Heng Chooi
    •  & Yi Tang
  8. School of Chemistry and Biochemistry, University of Western Australia, Perth, Western Australia, Australia.

    • Yit Heng Chooi
  9. Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA.

    • Jan Claesen
    •  & Michael A Fischbach
  10. California Institute for Quantitative Biosciences, University of California San Francisco, San Francisco, California, USA.

    • Jan Claesen
    •  & Michael A Fischbach
  11. Department of Energy (DOE) Joint Genome Institute, Walnut Creek, California, USA.

    • R Cameron Coates
    • , Michalis Hadjithomas
    • , Amrita Pati
    •  & Nikos C Kyrpides
  12. Evolution of Metabolic Diversity Laboratory, Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav-IPN), Irapuato, Guanajuato, México.

    • Pablo Cruz-Morales
    •  & Francisco Barona-Gómez
  13. Helmholtz Institute for Pharmaceutical Research, Helmholtz Centre for Infection Research and Department of Pharmaceutical Biotechnology, Saarland University, Saarbrücken, Germany.

    • Srikanth Duddela
    • , Katrin Jungmann
    • , Daniel Krug
    • , Andriy Luzhetskyy
    •  & Rolf Müller
  14. Institute for Molecular Biosciences, Goethe University, Frankfurt am Main, Germany.

    • Stephanie Düsterhus
    • , Christoph Geiger
    • , Peter Kötter
    •  & Karl-Dieter Entian
  15. Department of Chemistry and Biochemistry, California State University, Chico, California, USA.

    • Daniel J Edwards
  16. Microbiology and Biotechnology Division, Department of Food and Environmental Sciences, University of Helsinki, Helsinki, Finland.

    • David P Fewer
    •  & Kaarina Sivonen
  17. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, USA.

    • Neha Garg
    • , Alexey V Melnik
    • , Pieter C Dorrestein
    • , William H Gerwick
    •  & Bradley S Moore
  18. Department of Molecular Microbiology, John Innes Centre, Norwich Research Park, Norwich, UK.

    • Juan Pablo Gomez-Escribano
    • , Andrew W Truman
    •  & Barrie Wilkinson
  19. Department of Pharmaceutical Biology and Biotechnology, Albert-Ludwigs-University of Freiburg, Freiburg, Germany.

    • Anja Greule
    •  & Andreas Bechthold
  20. School of Biosciences, University of Birmingham, Birmingham, UK.

    • Anthony S Haines
    •  & Christopher M Thomas
  21. Institute of Microbiology, Eidgenössische Technische Hochschule (ETH) Zürich, Zürich, Switzerland.

    • Eric J N Helfrich
    •  & Jörn Piel
  22. Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

    • Matthew L Hillwig
    •  & Xinyu Liu
  23. Leibniz Institute for Natural Product Research and Infection Biology (HKI), Jena, Germany.

    • Keishi Ishida
    • , Yuta Tsunematsu
    • , Axel A Brakhage
    • , Christian Hertweck
    •  & Markus Nett
  24. Gordon and Betty Moore Foundation, Palo Alto, California, USA.

    • Adam C Jones
  25. Sustainable Studies Program, Roosevelt University Chicago, Illinois, USA.

    • Carla S Jones
  26. Merck Stiftungsprofessur für Molekulare Biotechnologie, Goethe Universität Frankfurt, Fachbereich Biowissenschaften, Frankfurt, Germany.

    • Carsten Kegler
    • , Nicholas J Tobias
    •  & Helge B Bode
  27. BioInformatics Research Center, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

    • Hyun Uk Kim
  28. Laboratory of Gene Technology, KU Leuven, Heverlee, Belgium.

    • Joleen Masschelein
    •  & Rob Lavigne
  29. Laboratory of Food Microbiology, KU Leuven, Heverlee, Belgium.

    • Joleen Masschelein
  30. Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.

    • Simone M Mantovani
    • , Nathan Moss
    • , Pieter C Dorrestein
    • , Lena Gerwick
    • , William H Gerwick
    •  & Bradley S Moore
  31. Department of Biology, William Paterson University, Wayne, New Jersey, USA.

    • Emily A Monroe
  32. Department of Biology, Memorial University of Newfoundland, St. John's, Newfoundland, Canada.

    • Marcus Moore
    •  & Kapil Tahlan
  33. Department of Metabolic Biology, John Innes Centre, Norwich Research Park, Norwich, UK.

    • Hans-Wilhelm Nützmann
    •  & Anne Osbourn
  34. Department of Chemistry, The Scripps Research Institute, Jupiter, Florida, USA.

    • Guohui Pan
    • , Xiaohui Yan
    •  & Ben Shen
  35. Institut für Chemie, Technische Universität Berlin, Berlin, Germany.

    • Daniel Petras
    •  & Roderich D Süssmuth
  36. BIOMERIT Research Centre, School of Microbiology, University College Cork–National University of Ireland, Cork, Ireland.

    • F Jerry Reen
    •  & Fergal O'Gara
  37. Departamento de Bioquímica y Genómica Microbianas, IBCE, Montevideo, Uruguay.

    • Federico Rosconi
  38. Energy Biosciences Institute, University of California Berkeley, Berkeley, California, USA.

    • Zhe Rui
  39. Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, California, USA.

    • Zhe Rui
  40. State Key Laboratory of Bioorganic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.

    • Zhenhua Tian
    •  & Wen Liu
  41. Department of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.

    • Yuta Tsunematsu
  42. Department of Medical Microbiology and Immunology, University of Wisconsin–Madison, Madison, Wisconsin, USA.

    • Philipp Wiemann
    •  & Nancy P Keller
  43. Department of Molecular Biosciences, The University of Texas, Austin, Texas, USA.

    • Elizabeth Wyckoff
    •  & Shelley M Payne
  44. Institute for Cellular and Molecular Biology, The University of Texas, Austin, Texas, USA.

    • Elizabeth Wyckoff
    •  & Shelley M Payne
  45. Department of Biochemistry and Biomedical Sciences, The M.G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.

    • Grace Yim
    •  & Gerard D Wright
  46. Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA.

    • Fengan Yu
    •  & David H Sherman
  47. Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan, USA.

    • Fengan Yu
    •  & David H Sherman
  48. Department of Chemistry, University of Michigan, Ann Arbor, Michigan, USA.

    • Fengan Yu
    •  & David H Sherman
  49. Department of Microbiology & Immunology, University of Michigan, Ann Arbor, Michigan, USA.

    • Fengan Yu
    •  & David H Sherman
  50. Key Laboratory of Tropical Marine Bio-resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, RNAM Center for Marine Microbiology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China.

    • Yunchang Xie
    • , Jianhua Ju
    •  & Changsheng Zhang
  51. Dynamique des Génomes et Adaptation Microbienne, Université de Lorraine and Institut National de la Recherche Agronomique (INRA), Unité Mixte de Recherche (UMR) 1128, Vandœuvre-lès-Nancy, France.

    • Bertrand Aigle
  52. Pharmaceutical Institute, Department of Pharmaceutical Biology, University of Tübingen, Tübingen, Germany.

    • Alexander K Apel
    • , Harald Gross
    • , Bertolt Gust
    •  & Leonard Kaysser
  53. German Centre for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany.

    • Alexander K Apel
    • , Harald Gross
    • , Bertolt Gust
    • , Leonard Kaysser
    • , Evi Stegmann
    • , Wolfgang Wohlleben
    •  & Nadine Ziemert
  54. Infectious Disease Research, Merck Research Laboratories, Kenilworth, New Jersey, USA.

    • Carl J Balibar
  55. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA.

    • Emily P Balskus
  56. Buchmann Institute for Molecular Life Sciences (BMLS), Goethe Universität Frankfurt, Frankfurt, Germany.

    • Helge B Bode
  57. Fachbereich Phytomedizin, Albrecht Thaer Institut, Humboldt Universität Berlin, Berlin, Germany.

    • Rainer Borriss
  58. UCD School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland.

    • Patrick Caffrey
  59. UNT System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, Texas, USA.

    • Yi-Qiang Cheng
  60. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA.

    • Jon Clardy
  61. Institut für Organische Chemie, Leibniz Universität Hannover, Hannover, Germany.

    • Russell J Cox
  62. School of Chemistry, University of Bristol, Bristol, UK.

    • Russell J Cox
  63. Centre of Microbial and Plant Genetics, Faculty of Bioscience Engineering, University of Leuven, Heverlee, Belgium.

    • René De Mot
  64. Naicons Srl, Milano, Italy.

    • Stefano Donadio
    •  & Margherita Sosio
  65. Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA.

    • Mohamed S Donia
  66. Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, USA.

    • Wilfred A van der Donk
    •  & Douglas A Mitchell
  67. Howard Hughes Medical Institute, USA.

    • Wilfred A van der Donk
  68. Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, California, USA.

    • Pieter C Dorrestein
  69. Department of Biology, Maynooth University, Maynooth, County Kildare, Ireland.

    • Sean Doyle
  70. Department of Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands.

    • Arnold J M Driessen
  71. Functional Microbiology, Institute of Microbiology, Department of Pathobiology, University of Veterinary Medicine Vienna, Vienna, Austria.

    • Monika Ehling-Schulz
  72. Friedrich Schiller University, Jena, Germany.

    • Christian Hertweck
  73. Department of Crop Protection, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.

    • Monica Höfte
  74. Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.

    • Susan E Jensen
  75. Synthetic Biology Engineering Research Center (SynBERC), University of California Emeryville, Emeryville, California, USA.

    • Leonard Katz
  76. Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut, USA.

    • Jonathan L Klassen
  77. Department of Bacteriology, University of Wisconsin–Madison, Madison, Wisconsin, USA.

    • Nancy P Keller
  78. Institute of Molecular Biology, Slovak Academy of Sciences, Bratislava, Slovak Republic.

    • Jan Kormanec
  79. Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, the Netherlands.

    • Oscar P Kuipers
  80. Biotechnology Research Center, The University of Tokyo, Tokyo, Japan.

    • Tomohisa Kuzuyama
  81. Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.

    • Nikos C Kyrpides
  82. Division of Bioscience and Bioinformatics, Myongji University, Yongin-si, Gyeonggi-Do, South Korea.

    • Hyung-Jin Kwon
  83. Institute of Integrative Biology of the Cell (I2BC), Commissariat à l'Energie Atomique (CEA), Centre National de la Recherche Scientifique (CNRS), Université Paris Sud, Orsay, France.

    • Sylvie Lautru
    •  & Jean-Luc Pernodet
  84. Department of Microbiology and Immunology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.

    • Chia Y Lee
  85. State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, China.

    • Bai Linquan
  86. School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

    • Bai Linquan
  87. Department of Pharmaceutical Sciences, Oregon State University, Corvallis, Oregon, USA.

    • Taifo Mahmud
  88. Microbiology/Biotechnology, Interfaculty Institute of Microbiology and Infection Medicine, Faculty of Science, University of Tübingen, Tübingen, Germany.

    • Yvonne Mast
    • , Evi Stegmann
    • , Wolfgang Wohlleben
    •  & Nadine Ziemert
  89. Departamento de Biología Funcional, Universidad de Oviedo, Oviedo, Spain.

    • Carmen Méndez
    •  & José A Salas
  90. Instituto Universitario de Oncología del Principado de Asturias (I.U.O.P.A), Universidad de Oviedo, Oviedo, Spain.

    • Carmen Méndez
  91. Department of Biochemistry, University of Turku, Turku, Finland.

    • Mikko Metsä-Ketelä
  92. School of Chemistry, University of Manchester, Manchester, UK.

    • Jason Micklefield
  93. Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.

    • Leonilde M Moreira
  94. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia.

    • Brett A Neilan
  95. Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.

    • Jens Nielsen
  96. Curtin University, School of Biomedical Sciences, Perth, Western Australia, Australia.

    • Fergal O'Gara
  97. Division of Chemistry, Graduate School of Science, Hokkaido University, Sapporo, Japan.

    • Hideaki Oikawa
  98. Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, Massachusetts, USA.

    • Marcia S Osburne
  99. Department of Genetics and Biotechnology, Ivan Franko National University of Lviv, Lviv, Ukraine.

    • Bohdan Ostash
  100. Institute of Microbiology, Academy of Sciences of the Czech Republic (ASCR), Prague, Czech Republic.

    • Miroslav Petricek
  101. Laboratoire Interdisciplinaire des Energies de Demain (LIED), UMR 8236 CNRS, Université Paris Diderot, Paris, France.

    • Olivier Ploux
  102. Novartis Institutes for BioMedical Research, Novartis Campus, Basel, Switzerland.

    • Esther K Schmitt
  103. Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand.

    • Barry Scott
  104. Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK.

    • Ryan F Seipke
  105. Molecular Therapeutics and Natural Products Library Initiative, The Scripps Research Institute, Jupiter, Florida, USA.

    • Ben Shen
  106. Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota–Twin Cities, Saint Paul, Minnesota, USA.

    • Michael J Smanski
  107. BioTechnology Institute, University of Minnesota–Twin Cities, Saint Paul, Minnesota, USA.

    • Michael J Smanski
  108. Unité BIOlogie et GEstion des Risques en agriculture (BIOGER), Institut National de la Recherche Agronomique (INRA), Grignon, France.

    • Muriel Viaud
  109. Department of Energy Great Lakes Bioenergy Research Center and Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, Michigan, USA.

    • Jonathan D Walton
  110. Chemistry, Engineering & Medicine for Human Health (ChEM-H) Institute, Stanford University, Stanford, California, USA.

    • Christopher T Walsh
  111. Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, the Netherlands.

    • Gilles P van Wezel
  112. Hofstra North Shore–Long Island Jewish School of Medicine, Hempstead, New York, USA.

    • Joanne M Willey
  113. Department of Biotechnology, Norwegian University of Science and Technology, Trondheim, Norway.

    • Sergey B Zotchev
  114. Jacobs University Bremen gGmbH, Bremen, Germany.

    • Frank Oliver Glöckner


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M.H.M., R.B., E.T. and F.O.G. initiated and coordinated the MIBiG standardization project. M.H.M. designed the first draft of the standard. M.H.M., R.K., P.Y. and F.O.G. coordinated integration within the MIxS framework. M.H.M. and R.K. constructed the submission system. M.H.M. and M.C. curated submitted community annotation entries. M.H.M., R.K., P.Y., M.C., R.B., E.T. and F.O.G. wrote the first draft of the paper. All authors contributed to the design of the standard, contributed to the community annotation of MIBiG entries and provided feedback on an early draft of the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Marnix H Medema.

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