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A computational framework to explore large-scale biosynthetic diversity.

Jorge C Navarro-Muñoz1,2, Nelly Selem-Mojica3, Michael W Mullowney4

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|November 27, 2019
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New bioinformatics tools, the biosynthetic gene similarity clustering and prospecting engine (BiG-SCAPE) and core analysis of syntenic orthologues to prioritize natural product gene clusters (CORASON), enable large-scale genome mining for natural product discovery.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Natural Products Chemistry

Background:

  • Genome mining is crucial for discovering novel natural products.
  • Current methods are limited for analyzing large-scale genomic datasets.
  • A need exists for efficient bioinformatic frameworks to analyze complex genomic data.

Purpose of the Study:

  • To develop and present a streamlined computational workflow for large-scale genome mining.
  • To introduce two novel software tools, BiG-SCAPE and CORASON, for analyzing biosynthetic gene clusters and their phylogenetic relationships.
  • To validate the utility of these tools in natural product discovery.

Main Methods:

  • Development of BiG-SCAPE for fast sequence similarity network analysis of biosynthetic gene clusters.
  • Development of CORASON for elucidating phylogenetic relationships within gene cluster families.
  • Validation using metabolomic data from 363 actinobacterial strains and mapping of detoxin/rimosamide-related gene clusters.

Main Results:

  • BiG-SCAPE enables efficient clustering and prospecting of biosynthetic gene clusters.
  • CORASON effectively maps phylogenetic diversity within gene cluster families.
  • The workflow led to the characterization of seven novel detoxin analogues.

Conclusions:

  • The developed computational workflow and software tools significantly advance large-scale genome mining.
  • BiG-SCAPE and CORASON provide powerful capabilities for natural product discovery from complex genomic data.
  • This approach facilitates the exploration of microbial natural product diversity.