MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters

 

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Detalhes bibliográficos
Autores: Terlouw, Barbara R., Blin, Kai, Navarro Muñoz, Jorge C., Avalon, Nicole E., Chevrette, Marc G., Egbert, Susan, Lee, Sanghoon, Meijer, David, Recchia, Michael J.J., Reitz, Zachary L., van Santen, Jeffrey A., Selem Mojica, Nelly, Tørring, Thomas, Zaroubi, Liana, Alanjary, Mohammad, Aleti, Gajender, Aguilar, César, Al-Salihi, Suhad A.A., Augustijn, Hannah E., Avelar Rivas J. Abraham, Avitia Domínguez, Luis A., Barona Gómez, Francisco, Bernaldo Agüero, Jordan, Bielinski, Vincent A., Biermann, Friederike, Booth, Thomas J., Carrion Bravo, Victor J., Castelo Branco, Raquel, Chagas, Fernanda O., Cruz Morales, Pablo, Du, Chao, Duncan, Katherine R., Gavriilidou, Athina, Gayrard, Damien, Gutiérrez García, Karina, Haslinger, Kristina, Helfrich, Eric J. N., van der Hooft, Justin J.J., Jati, Afif P., Kalkreuter, Edward, Kalyvas, Nikolaos, Kang, Kyo Bin, Kautsar, Satria, Kim, Wonyong, Kunjapur, Aditya M., Li, Yongxin, Lin, Gengmin, Loureiro, Catarina, Louwen, Joris J. R., Louwen, Nico L. L., Lund, George, Parra Villalobos, Jonathan, Philmus, Benjamin, Pourmohsenin, Bita, Pronk, Lotte J.U., Rego, Adriana, Balaya Rex, Devasahayam Arokia, Robinson, Serina, Rosas Becerra, L. Rodrigo, Roxborough, Eve T., Schorn, Michelle A., Scobie, Darren J., Saurabh Singh, Kumar, Sokolova, Nika, Tang, Xiaoyu, Udwary, Daniel, Vigneshwari, Aruna, Vind, Kristiina, Vromans, Sophie P.J.M., Waschulin, Valentin, Williams, Sam E., Winter, Jaclyn M., Witte, Thomas E., Xie, Huali, Yang, Dong, Yu, Jingwei, Zdouc, Mitja, Zhong, Zheng, Collemare, Jérôme, Linington, Roger G., Weber, Tilmann, Medema, Marnix H.
Formato: artículo original
Data de Publicação:2023
Descrição:With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.
País:Kérwá
Recursos:Universidad de Costa Rica
Repositorio:Kérwá
Idioma:Inglés
OAI Identifier:oai:kerwa.ucr.ac.cr:10669/91659
Acesso em linha:https://academic.oup.com/nar/article/51/D1/D603/6833236
https://hdl.handle.net/10669/91659
Palavra-chave:gene clusters
BIOSYNTHESIS
MiBiG 3.0
database update
ARTIFICIAL INTELLIGENCE

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