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antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters

Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we i...

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Autores principales: Weber, Tilmann, Blin, Kai, Duddela, Srikanth, Krug, Daniel, Kim, Hyun Uk, Bruccoleri, Robert, Lee, Sang Yup, Fischbach, Michael A, Müller, Rolf, Wohlleben, Wolfgang, Breitling, Rainer, Takano, Eriko, Medema, Marnix H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489286/
https://www.ncbi.nlm.nih.gov/pubmed/25948579
http://dx.doi.org/10.1093/nar/gkv437
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author Weber, Tilmann
Blin, Kai
Duddela, Srikanth
Krug, Daniel
Kim, Hyun Uk
Bruccoleri, Robert
Lee, Sang Yup
Fischbach, Michael A
Müller, Rolf
Wohlleben, Wolfgang
Breitling, Rainer
Takano, Eriko
Medema, Marnix H
author_facet Weber, Tilmann
Blin, Kai
Duddela, Srikanth
Krug, Daniel
Kim, Hyun Uk
Bruccoleri, Robert
Lee, Sang Yup
Fischbach, Michael A
Müller, Rolf
Wohlleben, Wolfgang
Breitling, Rainer
Takano, Eriko
Medema, Marnix H
author_sort Weber, Tilmann
collection PubMed
description Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we introduced antiSMASH, a web server and stand-alone tool for the automatic genomic identification and analysis of biosynthetic gene clusters, available at http://antismash.secondarymetabolites.org. Here, we present version 3.0 of antiSMASH, which has undergone major improvements. A full integration of the recently published ClusterFinder algorithm now allows using this probabilistic algorithm to detect putative gene clusters of unknown types. Also, a new dereplication variant of the ClusterBlast module now identifies similarities of identified clusters to any of 1172 clusters with known end products. At the enzyme level, active sites of key biosynthetic enzymes are now pinpointed through a curated pattern-matching procedure and Enzyme Commission numbers are assigned to functionally classify all enzyme-coding genes. Additionally, chemical structure prediction has been improved by incorporating polyketide reduction states. Finally, in order for users to be able to organize and analyze multiple antiSMASH outputs in a private setting, a new XML output module allows offline editing of antiSMASH annotations within the Geneious software.
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spelling pubmed-44892862015-07-07 antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters Weber, Tilmann Blin, Kai Duddela, Srikanth Krug, Daniel Kim, Hyun Uk Bruccoleri, Robert Lee, Sang Yup Fischbach, Michael A Müller, Rolf Wohlleben, Wolfgang Breitling, Rainer Takano, Eriko Medema, Marnix H Nucleic Acids Res Web Server issue Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we introduced antiSMASH, a web server and stand-alone tool for the automatic genomic identification and analysis of biosynthetic gene clusters, available at http://antismash.secondarymetabolites.org. Here, we present version 3.0 of antiSMASH, which has undergone major improvements. A full integration of the recently published ClusterFinder algorithm now allows using this probabilistic algorithm to detect putative gene clusters of unknown types. Also, a new dereplication variant of the ClusterBlast module now identifies similarities of identified clusters to any of 1172 clusters with known end products. At the enzyme level, active sites of key biosynthetic enzymes are now pinpointed through a curated pattern-matching procedure and Enzyme Commission numbers are assigned to functionally classify all enzyme-coding genes. Additionally, chemical structure prediction has been improved by incorporating polyketide reduction states. Finally, in order for users to be able to organize and analyze multiple antiSMASH outputs in a private setting, a new XML output module allows offline editing of antiSMASH annotations within the Geneious software. Oxford University Press 2015-07-01 2015-05-06 /pmc/articles/PMC4489286/ /pubmed/25948579 http://dx.doi.org/10.1093/nar/gkv437 Text en © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Web Server issue
Weber, Tilmann
Blin, Kai
Duddela, Srikanth
Krug, Daniel
Kim, Hyun Uk
Bruccoleri, Robert
Lee, Sang Yup
Fischbach, Michael A
Müller, Rolf
Wohlleben, Wolfgang
Breitling, Rainer
Takano, Eriko
Medema, Marnix H
antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters
title antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters
title_full antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters
title_fullStr antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters
title_full_unstemmed antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters
title_short antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters
title_sort antismash 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters
topic Web Server issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489286/
https://www.ncbi.nlm.nih.gov/pubmed/25948579
http://dx.doi.org/10.1093/nar/gkv437
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