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

Authors :
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.A.
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, Yong-Xin
Lin, Geng-Min
Loureiro, Catarina
Louwen, Joris J.R.
Louwen, Nico L.L.
Lund, George
Parra, Jonathan
Philmus, Benjamin
Pourmohsenin, Bita
Pronk, Lotte J.U.
Rego, Adriana
Rex, Devasahayam Arokia Balaya
Robinson, Serina
Rosas-Becerra, L.R.
Roxborough, Eve T.
Schorn, Michelle A.
Scobie, Darren J.
Singh, Kumar Saurabh
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.
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.A.
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, Yong-Xin
Lin, Geng-Min
Loureiro, Catarina
Louwen, Joris J.R.
Louwen, Nico L.L.
Lund, George
Parra, Jonathan
Philmus, Benjamin
Pourmohsenin, Bita
Pronk, Lotte J.U.
Rego, Adriana
Rex, Devasahayam Arokia Balaya
Robinson, Serina
Rosas-Becerra, L.R.
Roxborough, Eve T.
Schorn, Michelle A.
Scobie, Darren J.
Singh, Kumar Saurabh
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.
Source :
ISSN: 0305-1048
Publication Year :
2023

Abstract

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.

Details

Database :
OAIster
Journal :
ISSN: 0305-1048
Notes :
application/pdf, Nucleic acids research 51 (2023) D1, ISSN: 0305-1048, ISSN: 0305-1048, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1376682493
Document Type :
Electronic Resource