The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
MIBiG 2.0: a repository for biosynthetic gene clusters of known function
Open accessAbstract
Fueled by the explosion of (meta)genomic data, genome mining of specialized metabolites has become a major technology for drug discovery and studying microbiome ecology. In these efforts, computational tools like antiSMASH have played a central role through the analysis of Biosynthetic Gene Clusters (BGCs). Thousands of candidate BGCs from microbial genomes have been identified and stored in public databases. Interpreting the function and novelty of these predicted BGCs requires comparison with a well-documented set of BGCs of known function. The MIBiG (Minimum Information about a Biosynthetic Gene Cluster) Data Standard and Repository was established in 2015 to enable curation and storage of known BGCs. Here, we present MIBiG 2.0, which encompasses major updates to the schema, the data, and the online repository itself. Over the past five years, 851 new BGCs have been added. Additionally, we performed extensive manual data curation of all entries to improve the annotation quality of our repository. We also redesigned the data schema to ensure the compliance of future annotations. Finally, we improved the user experience by adding new features such as query searches and a statistics page, and enabled direct link-outs to chemical structure databases. The repository is accessible online at https://mibig.secondarymetabolites.org/.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Wang, M., Carver, J. J., Phelan, V. V., Sanchez, L. M., Garg, N., Peng, Y., Nguyen, D. D., Watrous, J., Kapono, C. A., Luzzatto-Knaan, T., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3597 · Cited by 4,629
Self-citationUpSetR: an R package for the visualization of intersecting sets and their properties
Conway, J. R., Lex, A., & Gehlenborg, N. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx364 · Cited by 3,586
PubChem 2019 update: improved access to chemical data
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1033 · Cited by 3,249
antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline
Blin, K., Shaw, S., Steinke, K., Villebro, R., Ziemert, N., Lee, S. Y., Medema, M. H., & Weber, T. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz310 · Cited by 2,907
Self-citationStructure and function of the global topsoil microbiome
Bahram, M., Hildebrand, F., Forslund, S. K., Anderson, J. L., Soudzilovskaia, N. A., Bodegom, P. M., Bengtsson-Palme, J., Anslan, S., Coelho, L. P., Harend, H., et al. (2018) · Nature · doi:10.1038/s41586-018-0386-6 · Cited by 2,538
Self-citationInsights into Secondary Metabolism from a Global Analysis of Prokaryotic Biosynthetic Gene Clusters
Cimermancic, P., Medema, M. H., Claesen, J., Kurita, K., Wieland Brown, L. C., Mavrommatis, K., Pati, A., Godfrey, P. A., Koehrsen, M., Clardy, J., et al. (2014) · Cell · doi:10.1016/j.cell.2014.06.034 · Cited by 981
Self-citationMinimum Information about a Biosynthetic Gene cluster
Medema, M. H., Kottmann, R., Yilmaz, P., Cummings, M., Biggins, J. B., Blin, K., de Bruijn, I., Chooi, Y. H., Claesen, J., Coates, R. C., et al. (2015) · Nature Chemical Biology · doi:10.1038/nchembio.1890 · Cited by 873
Self-citationNovel soil bacteria possess diverse genes for secondary metabolite biosynthesis
Crits-Christoph, A., Diamond, S., Butterfield, C. N., Thomas, B. C., & Banfield, J. F. (2018) · Nature · doi:10.1038/s41586-018-0207-y · Cited by 435
Blin, K., Pascal Andreu, V., de los Santos, E. L. C., Del Carratore, F., Lee, S. Y., Medema, M. H., & Weber, T. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1060 · Cited by 188
Self-citationClusterMine360: a database of microbial PKS/NRPS biosynthesis
Conway, K. R., & Boddy, C. N. C. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks993 · Cited by 141
Cited by
NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products
Kim, H. W., Wang, M., Leber, C. A., Nothias, L.-F., Reher, R., Kang, K. B., van der Hooft, J. J. J., Dorrestein, P. C., Gerwick, W. H., & Cottrell, G. W. (2021) · Journal of Natural Products · doi:10.1021/acs.jnatprod.1c00399 · Cited by 551
Ecology and genomics of Actinobacteria: new concepts for natural product discovery
van Bergeijk, D. A., Terlouw, B. R., Medema, M. H., & van Wezel, G. P. (2020) · Nature Reviews Microbiology · doi:10.1038/s41579-020-0379-y · Cited by 448
Self-citationThe LOTUS initiative for open knowledge management in natural products research
Rutz, A., Sorokina, M., Galgonek, J., Mietchen, D., Willighagen, E., Gaudry, A., Graham, J. G., Stephan, R., Page, R., Vondrášek, J., et al. (2022) · eLife · doi:10.7554/elife.70780 · Cited by 429
The Natural Products Atlas 2.0: a database of microbially-derived natural products
van Santen, J. A., Poynton, E. F., Iskakova, D., McMann, E., Alsup, T. A., Clark, T. N., Fergusson, C. H., Fewer, D. P., Hughes, A. H., McCadden, C. A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab941 · Cited by 319
Self-citationFrom sequence to function through structure: Deep learning for protein design
(2022) · doi:10.1016/j.csbj.2022.11.014 · Cited by 124
Metadata
- Authors
- Kautsar, S. A., Blin, K., Shaw, S., Navarro-Muñoz, J. C., Terlouw, B. R., van der Hooft, J. J. J., van Santen, J. A., Tracanna, V., Suarez Duran, H. G., Pascal Andreu, V., Selem-Mojica, N., Alanjary, M., Robinson, S. L., Lund, G., Epstein, S. C., Sisto, A. C., Charkoudian, L. K., Collemare, J., Linington, R. G., Weber, T., & Medema, M. H.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-10-15
- Volume
- 48
- Issue
- D1
- Pages
- D454-D458
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2980832688
- Cited by
- 560
- References
- 14
- FWCI
- 40.99
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: MIBiG 2.0: a repository for biosynthetic gene clusters of known function. https://app.cochat.ai/papers/10.1093/nar/gkz882
- The paper
- Kautsar, S. A., Blin, K., Shaw, S., Navarro-Muñoz, J. C., Terlouw, B. R., van der Hooft, J. J. J., van Santen, J. A., Tracanna, V., Suarez Duran, H. G., Pascal Andreu, V., Selem-Mojica, N., Alanjary, M., Robinson, S. L., Lund, G., Epstein, S. C., Sisto, A. C., Charkoudian, L. K., Collemare, J., Linington, R. G., . . . Medema, M. H. (2019). MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz882
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.