Skip to main content
SpringerLink
Log in

A Bioinformatics Workflow for Investigating Fungal Biosynthetic Gene Clusters

  • Protocol
  • First Online:
Engineering Natural Product Biosynthesis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2489))

Abstract

Predicting secondary metabolite biosynthetic gene clusters is a routine analysis performed for each newly sequenced fungal genome. Yet, the usefulness of such predictions remains restricted as they provide total numbers of biosynthetic pathways with only very limited biological significance. In this chapter, we describe a workflow to predict and analyze biosynthetic gene clusters in fungal genomes. It relies on similarity networking and phylogeny to perform genetic dereplication and to prioritize candidate gene clusters that potentially produce new compounds. This basic workflow includes the generation of high-quality figures for publication.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hyde KD, Xu J, Rapior S et al (2019) The amazing potential of fungi: 50 ways we can exploit fungi industrially. Fungal Divers 97:1–136

    Article  Google Scholar 

  2. Mosunova O, Navarro-Muñoz JC, Collemare J (2020) The biosynthesis of fungal secondary metabolites: from fundamentals to biotechnological applications. In: Reference module in life sciences. Elsevier, Amsterdam

    Google Scholar 

  3. Keller NP, Hohn TM (1997) Metabolic pathway gene clusters in filamentous fungi. Fungal Genet Biol 21:17–29

    Article  CAS  Google Scholar 

  4. Greco C, Keller NP, Rokas A (2019) Unearthing fungal chemodiversity and prospects for drug discovery. Curr Opin Microbiol 51:22–29

    Article  CAS  Google Scholar 

  5. Medema MH, Blin K, Cimermancic P et al (2011) antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res 39:W339–W346

    Article  CAS  Google Scholar 

  6. Blin K, Shaw S, Steinke K et al (2019) AntiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res 47:W81–W87

    Article  CAS  Google Scholar 

  7. Khaldi N, Seifuddin FT, Turner G et al (2010) SMURF: genomic mapping of fungal secondary metabolite clusters. Fungal Genet Biol 47:736–741

    Article  CAS  Google Scholar 

  8. Wolf T, Shelest V, Nath N et al (2016) CASSIS and SMIPS: promoter-based prediction of secondary metabolite gene clusters in eukaryotic genomes. Bioinformatics 32:1138–1143

    Article  CAS  Google Scholar 

  9. Umemura M, Koike H, Nagano N et al (2013) MIDDAS-M: motif-independent de novo detection of secondary metabolite gene clusters through the integration of genome sequencing and transcriptome data. PLoS One 8:e84028

    Article  Google Scholar 

  10. Vesth TC, Brandl J, Andersen MR (2016) FunGeneClusterS: predicting fungal gene clusters from genome and transcriptome data. Synth Syst Biotechnol 1:122–129

    Article  Google Scholar 

  11. Takeda I, Umemura M, Koike H et al (2014) Motif-independent prediction of a secondary metabolism gene cluster using comparative genomics: application to sequenced genomes of Aspergillus and ten other filamentous fungal species. DNA Res 21:447–457

    Article  CAS  Google Scholar 

  12. Almeida H, Palys S, Tsang A et al (2020) TOUCAN: a framework for fungal biosynthetic gene cluster discovery. NAR Genom Bioinform 2:1–11

    CAS  Google Scholar 

  13. Blin K, Shaw S, Kautsar SA et al (2021) The antiSMASH database version 3: increased taxonomic coverage and new query features for modular enzymes. Nucleic Acids Res 49:D639–D643

    Article  CAS  Google Scholar 

  14. Kautsar SA, Blin K, Shaw S et al (2019) MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Res 48:D454–D458

    PubMed Central  Google Scholar 

  15. Kautsar SA, Blin K, Shaw S et al (2021) BiG-FAM: the biosynthetic gene cluster families database. Nucleic Acids Res 49:D490–D497

    Article  CAS  Google Scholar 

  16. Weber T, Blin K, Duddela S et al (2015) antiSMASH 3.0--a comprehensive resource for the genome mining of biosynthetic gene clusters. Nucleic Acids Res 43:1–7

    Article  Google Scholar 

  17. Adamek M, Alanjary M, Ziemert N (2019) Applied evolution: phylogeny-based approaches in natural products research. Nat Prod Rep 36:1295–1312

    Article  CAS  Google Scholar 

  18. Navarro-Muñoz JC, Selem-Mojica N, Mullowney MW et al (2020) A computational framework to explore large-scale biosynthetic diversity. Nat Chem Biol 16:60–68

    Article  Google Scholar 

  19. Gilchrist CLM, Chooi Y-H (2021) Clinker & clustermap.js: automatic generation of gene cluster comparison figures. Bioinformatics btab007

    Google Scholar 

  20. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504

    Article  CAS  Google Scholar 

  21. Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30:772–780

    Article  CAS  Google Scholar 

  22. Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T (2009) trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25:1972–1973

    Article  CAS  Google Scholar 

  23. Minh BQ, Schmidt HA, Chernomor O et al (2020) IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 37:1530–1534

    Article  CAS  Google Scholar 

  24. Larsson A (2014) AliView: a fast and lightweight alignment viewer and editor for large datasets. Bioinformatics 30:3276–3278

    Article  CAS  Google Scholar 

  25. Letunic I, Bork P (2021) Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res 49:W293–W296

    Article  CAS  Google Scholar 

  26. Grigoriev IV, Nikitin R, Haridas S et al (2014) MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res 42:D699–D704

    Article  CAS  Google Scholar 

  27. Kroken S, Glass NL, Taylor JW et al (2003) Phylogenomic analysis of type I polyketide synthase genes in pathogenic and saprobic ascomycetes. Proc Natl Acad Sci U S A 100:15670–15675

    Article  CAS  Google Scholar 

  28. Gallo A, Ferrara M, Perrone G (2013) Phylogenetic study of polyketide synthases and nonribosomal peptide synthetases involved in the biosynthesis of mycotoxins. Toxins (Basel) 5:717–742

    Article  CAS  Google Scholar 

  29. Bushley KE, Turgeon BG (2010) Phylogenomics reveals subfamilies of fungal nonribosomal peptide synthetases and their evolutionary relationships. BMC Evol Biol 10:26

    Article  Google Scholar 

  30. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797

    Article  CAS  Google Scholar 

  31. Sievers F, Higgins DG (2018) Clustal omega for making accurate alignments of many protein sequences. Protein Sci 27:135–145

    Article  CAS  Google Scholar 

  32. Talavera G, Castresana J (2007) Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol 56:564–577

    Article  CAS  Google Scholar 

  33. Steenwyk JL, Buida TJ, Li Y et al (2020) ClipKIT: a multiple sequence alignment trimming software for accurate phylogenomic inference. PLoS Biol 18:e3001007

    Article  CAS  Google Scholar 

  34. Kalyaanamoorthy S, Minh BQ, Wong TKF et al (2017) ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 14:587–589

    Article  CAS  Google Scholar 

  35. Price MN, Dehal PS, Arkin AP (2009) FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 26:1641–1650

    Article  CAS  Google Scholar 

  36. Minh BQ, Nguyen MAT, von Haeseler A (2013) Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol 30:1188–1195

    Article  CAS  Google Scholar 

  37. Guindon S, Dufayard J-F, Lefort V et al (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol 59:307–321

    Article  CAS  Google Scholar 

  38. Huerta-Cepas J, Serra F, Bork P (2016) ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol Biol Evol 33:1635–1638

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Westerdijk Fungal Biodiversity Institute, Utrecht, Netherlands

    Jorge C. Navarro-Muñoz & Jérôme Collemare

Authors
  1. Jorge C. Navarro-Muñoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jérôme Collemare
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jérôme Collemare .

Editor information

Editors and Affiliations

  1. Department of Chemistry & BioDiscovery Institute, University of North Texas, Denton, USA

    Elizabeth Skellam

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Navarro-Muñoz, J.C., Collemare, J. (2022). A Bioinformatics Workflow for Investigating Fungal Biosynthetic Gene Clusters. In: Skellam, E. (eds) Engineering Natural Product Biosynthesis. Methods in Molecular Biology, vol 2489. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2273-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2273-5_1

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2272-8

  • Online ISBN: 978-1-0716-2273-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation