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. 2013 Jul;41(Web Server issue):W204-12.
doi: 10.1093/nar/gkt449. Epub 2013 Jun 3.

antiSMASH 2.0--a versatile platform for genome mining of secondary metabolite producers

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antiSMASH 2.0--a versatile platform for genome mining of secondary metabolite producers

Kai Blin et al. Nucleic Acids Res. 2013 Jul.

Abstract

Microbial secondary metabolites are a potent source of antibiotics and other pharmaceuticals. Genome mining of their biosynthetic gene clusters has become a key method to accelerate their identification and characterization. In 2011, we developed antiSMASH, a web-based analysis platform that automates this process. Here, we present the highly improved antiSMASH 2.0 release, available at http://antismash.secondarymetabolites.org/. For the new version, antiSMASH was entirely re-designed using a plug-and-play concept that allows easy integration of novel predictor or output modules. antiSMASH 2.0 now supports input of multiple related sequences simultaneously (multi-FASTA/GenBank/EMBL), which allows the analysis of draft genomes comprising multiple contigs. Moreover, direct analysis of protein sequences is now possible. antiSMASH 2.0 has also been equipped with the capacity to detect additional classes of secondary metabolites, including oligosaccharide antibiotics, phenazines, thiopeptides, homo-serine lactones, phosphonates and furans. The algorithm for predicting the core structure of the cluster end product is now also covering lantipeptides, in addition to polyketides and non-ribosomal peptides. The antiSMASH ClusterBlast functionality has been extended to identify sub-clusters involved in the biosynthesis of specific chemical building blocks. The new features currently make antiSMASH 2.0 the most comprehensive resource for identifying and analyzing novel secondary metabolite biosynthetic pathways in microorganisms.

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Figures

Figure 1.
Figure 1.
Overview page of the antiSMASH results. antiSMASH 2.0 gives an overview of all the output results in a single page, showing all the detected biosynthetic gene clusters with their type classifications and nucleotide positions. For inputs consisting of multiple entries/contigs, the clusters are separated by input entry/contig. Gene cluster types are signified by specific colors.
Figure 2.
Figure 2.
ClusterBlast and SubclusterBlast outputs for the balhimycin (23) biosynthesis gene cluster. The top six hits of each analysis module are shown. The ClusterBlast module shows the homology between the balhimycin gene cluster and the vancomycin, VEG, A40926 and teicoplanin biosynthesis gene clusters. Homologous genes are shown in identical colors, whereas white-colored genes have no BLAST hits between the gene clusters. The novel SubclusterBlast module can identify homologous sub-clusters encoding the biosynthesis of specific chemical moieties. In this case, SubclusterBlast is able to identify the dihydroxyphenylglycine (dHpg), hydroxyphenylglycine (Hpg) and hydroxytyrosine (Bht) precursor biosynthesis sub-clusters, as well as the vancosamine-like sugar biosynthesis sub-cluster.
Figure 3.
Figure 3.
Top part of a gene cluster overview in the re-designed antiSMASH 2.0 output. The gene cluster shown is the calcium-dependent antibiotic biosynthesis gene cluster from Streptomyces coelicolor A3(2). The gene cluster-type–specific coloring of the numbered gene cluster buttons makes it easier to navigate through large result files. smCOG-based coloring of biosynthetic, transport-related and regulatory genes within the gene cluster make it easier to interpret the architecture of the gene cluster.

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