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      The antiSMASH database version 2: a comprehensive resource on secondary metabolite biosynthetic gene clusters

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          Abstract

          Natural products originating from microorganisms are frequently used in antimicrobial and anticancer drugs, pesticides, herbicides or fungicides. In the last years, the increasing availability of microbial genome data has made it possible to access the wealth of biosynthetic clusters responsible for the production of these compounds by genome mining. antiSMASH is one of the most popular tools in this field. The antiSMASH database provides pre-computed antiSMASH results for many publicly available microbial genomes and allows for advanced cross-genome searches. The current version 2 of the antiSMASH database contains annotations for 6200 full bacterial genomes and 18,576 bacterial draft genomes and is available at https://antismash-db.secondarymetabolites.org/.

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          antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification

          Kai Blin, Thomas Wolf, Marc Chevrette … (2017)
          Abstract Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.
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            antiSMASH 2.0—a versatile platform for genome mining of secondary metabolite producers

            Kai Blin, Marnix H Medema, Daniyal Kazempour … (2013)
            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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              The evolution of genome mining in microbes - a review.

              Nadine Ziemert, Mohammad Alanjary, Tilmann Weber (2016)
              Covering: 2006 to 2016The computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Thousands of bacterial genome sequences are publically available these days containing an even larger number and diversity of secondary metabolite gene clusters that await linkage to their encoded natural products. With the development of high-throughput sequencing methods and the wealth of DNA data available, a variety of genome mining methods and tools have been developed to guide discovery and characterisation of these compounds. This article reviews the development of these computational approaches during the last decade and shows how the revolution of next generation sequencing methods has led to an evolution of various genome mining approaches, techniques and tools. After a short introduction and brief overview of important milestones, this article will focus on the different approaches of mining genomes for secondary metabolites, from detecting biosynthetic genes to resistance based methods and "evo-mining" strategies including a short evaluation of the impact of the development of genome mining methods and tools on the field of natural products and microbial ecology.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 08 January 2019
                Publication date (Electronic): 05 November 2018
                Publication date PMC-release: 05 November 2018
                Volume: 47
                Issue: Database issue , Database issue
                Pages: D625-D630
                Affiliations
                [1 ]Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800 Kgs. Lyngby, Denmark
                [2 ]Bioinformatics Group, Wageningen University, Wageningen, Netherlands
                [3 ]Warwick Integrative Synthetic Biology Centre, University of Warwick, Coventry, UK
                [4 ]Faculty of Science and Engineering, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
                [5 ]Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
                Author notes
                To whom correspondence should be addressed. Tel: +45 24 89 61 32; Email: tiwe@ 123456biosustain.dtu.dk
                Author information
                http://orcid.org/0000-0002-2191-2821
                http://orcid.org/0000-0002-8260-5120
                Article
                Publisher ID: gky1060
                DOI: 10.1093/nar/gky1060
                PMC ID: 6324005
                PubMed ID: 30395294
                SO-VID: 7b315106-625b-4465-bb48-fd247adc4dcb
                Copyright © © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                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.

                History
                Date accepted : 17 October 2018
                Date revision received : 15 October 2018
                Date received : 15 September 2018
                Page count
                Pages: 6
                Funding
                Funded by: Novo Nordisk Foundation
                Award ID: NNF10CC1016517
                Award ID: NNF16OC0021746
                Funded by: Netherlands Organization for Scientific Research
                Award ID: 863.15.002
                Funded by: Engineering and Physical Sciences Research Council 10.13039/501100000266
                Award ID: BB/M017982/1
                Categories
                Subject: Database Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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