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. 2015 Jul 1;43(W1):W237-43.
doi: 10.1093/nar/gkv437. Epub 2015 May 6.

antiSMASH 3.0-a Comprehensive Resource for the Genome Mining of Biosynthetic Gene Clusters

Free PMC article

antiSMASH 3.0-a Comprehensive Resource for the Genome Mining of Biosynthetic Gene Clusters

Tilmann Weber et al. Nucleic Acids Res. .
Free PMC article


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 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.


Figure 1.
Figure 1.
Example output of a KnownClusterBlast output, using the balhimycin gene cluster (GenBank Y16952.3). The significance thresholds used are the same as for the ClusterBlast module (8). Following the balhimycin gene cluster itself, several other BGCs involved in the biosynthesis of similar glycopeptides are shown as next best hits. The percentage of genes in the query cluster that are present in the hit cluster is included as extra information. Also, hyperlinks to the MIBiG repository are available, where users can find additional information on each gene cluster.
Figure 2.
Figure 2.
BiosynML output and Geneious plugin. The schematic shows the interfacing of typical tasks during BGC analysis—including antiSMASH annotation, manual BGC refinement, deposition to in-house databases and submission to the public MIBiG repository—supported by BiosynML functionality.

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