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Bioinformatics Approaches and Software for Detection of Secondary Metabolic Gene Clusters

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Fungal Secondary Metabolism

Part of the book series: Methods in Molecular Biology ((MIMB,volume 944))

Abstract

The accelerating pace of microbial genomics is sparking a renaissance in the field of natural products research. Researchers can now get a preview of the organism’s secondary metabolome by analyzing its genomic sequence. Combined with other -omics data, this approach may provide a cost-effective alternative to industrial high-throughput screening in drug discovery. In the last few years, several computational tools have been developed to facilitate this process by identifying genes involved in secondary metabolite biosynthesis in bacterial and fungal genomes. Here, we review seven software programs that are available for this purpose, with an emphasis on antibiotics & Secondary Metabolite Analysis SHell (antiSMASH) and Secondary Metabolite Unknown Regions Finder (SMURF), the only tools that can comprehensively detect complete secondary metabolite biosynthesis gene clusters. We also discuss five related software packages—CLUster SEquence ANalyzer (CLUSEAN), ClustScan, Structure Based Sequence Analysis of Polyketide Synthases (SBSPKS), NRPSPredictor, and Natural Product searcher (NP.searcher)—that identify secondary metabolite backbone biosynthesis genes. This chapter offers detailed protocols, suggestions, and caveats to assist researchers in using these tools most effectively.

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Acknowledgements

We thank Suman Pakala and Bill Nierman at JCVI for critical suggestions and comments. The work of MHM was supported by the Dutch Technology Foundation, which is the applied-science division of The Netherlands Organisation for Scientific Research and the Technology Programme of the Ministry of Economic Affairs under STW grant number 10463. This project has been funded in part with federal funds to NDF from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract numbers N01-AI-30071 and HHSN272200900007C.

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Authors and Affiliations

  1. The J. Craig Venter Institute, Rockville, MD, USA

    Natalie D. Fedorova & Venkatesh Moktali

  2. Groningen Bioinformatics Centre and Department of Microbial Physiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands

    Marnix H. Medema

Authors
  1. Natalie D. Fedorova
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  2. Venkatesh Moktali
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  3. Marnix H. Medema
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Corresponding author

Correspondence to Natalie D. Fedorova .

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Editors and Affiliations

  1. , Dept. of Medical Microbiology & Immunolo, University of Wisconsin-Madison, Linden Drive 1550, Madison, 53706, Wisconsin, USA

    Nancy P. Keller

  2. , Dept. of Molecular Biology & Biotechnolo, University of Sheffield, Firth Court, Sheffield, S10 2TN, United Kingdom

    Geoffrey Turner

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© 2012 Springer Science+Business Media, LLC

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Fedorova, N.D., Moktali, V., Medema, M.H. (2012). Bioinformatics Approaches and Software for Detection of Secondary Metabolic Gene Clusters. In: Keller, N., Turner, G. (eds) Fungal Secondary Metabolism. Methods in Molecular Biology, vol 944. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-122-6_2

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  • DOI: https://doi.org/10.1007/978-1-62703-122-6_2

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-121-9

  • Online ISBN: 978-1-62703-122-6

  • eBook Packages: Springer Protocols

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