4.8 Article

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

期刊

NUCLEIC ACIDS RESEARCH
卷 43, 期 W1, 页码 W237-W243

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkv437

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资金

  1. NWO Rubicon fellowship
  2. Novo Nordisk Foundation
  3. German Center for Infection Research (DZIF)
  4. BBSRC
  5. EPSRC [BB/M017702/1]
  6. Ministry of Science, ICT, and Future Planning (MSIP) through the National Research Foundation (NRF) of Korea [NRF-2012M1A2A2026556]
  7. NWO Incentive Fund Open Access
  8. National Research Foundation of Korea [2012M1A2A2026557] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
  9. Biotechnology and Biological Sciences Research Council [BB/M017702/1] Funding Source: researchfish
  10. NNF Center for Biosustainability [New Bioactive Compounds] Funding Source: researchfish
  11. Novo Nordisk Fonden [NNF10CC1016517] Funding Source: researchfish
  12. BBSRC [BB/M017702/1] Funding Source: UKRI

向作者/读者索取更多资源

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 standalone 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 patternmatching procedure and Enzyme Commission numbers are assigned to functionally classify all enzymecoding 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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