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- 저자 : Medema, (검색결과 2 건)
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Blin, Kai,Medema, Marnix H.,Kottmann, Renzo,Lee, Sang Yup,Weber, Tilmann Oxford University Press 2017 Nucleic acids research Vol.45 No.d1
<P>Secondary metabolites produced by microorganisms are the main source of bioactive compounds that are in use as antimicrobial and anticancer drugs, fungicides, herbicides and pesticides. In the last decade, the increasing availability of microbial genomes has established genome mining as a very important method for the identification of their biosynthetic gene clusters (BGCs). One of the most popular tools for this task is antiSMASH. However, so far, antiSMASH is limited to <I>de novo</I> computing results for user-submitted genomes and only partially connects these with BGCs from other organisms. Therefore, we developed the antiSMASH database, a simple but highly useful new resource to browse antiSMASH-annotated BGCs in the currently 3907 bacterial genomes in the database and perform advanced search queries combining multiple search criteria. antiSMASH-DB is available at http://antismash-db.secondarymetabolites.org/.</P>
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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,Mü,ller, Rolf,Wohlleben, Wolfgang,Breitling, Rainer,Takano, Eriko,Medema, Oxford University Press 2015 Nucleic acids research Vol.43 No.w1
<P><B>Abstract</B></P><P>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.</P>
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