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CLOCI: unveiling cryptic fungal gene clusters with generalized detection.

Zachary Konkel1,2, Laura Kubatko3,4, Jason C Slot1,2

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We developed CLOCI, a novel algorithm to identify gene clusters, including previously undiscovered types. This unbiased approach enhances small molecule discovery and reveals more about genome organization and evolution.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene clusters are functionally linked genes crucial for various biological processes.
  • Current methods often miss novel or non-canonical gene clusters, limiting natural product discovery.
  • Unbiased detection is needed to fully understand gene cluster repertoires and genome organization.

Purpose of the Study:

  • To develop a function-agnostic algorithm for comprehensive gene cluster identification.
  • To improve the detection of both known and novel gene cluster classes.
  • To facilitate genome-enabled small molecule mining and evolutionary studies.

Main Methods:

  • Developed CLOCI (Co-occurrence Locus and Orthologous Cluster Identifier) algorithm.
  • Utilized multiple proxies of selection for coordinated gene evolution.
  • Applied CLOCI for generalized gene cluster detection and family circumscription.

Main Results:

  • CLOCI successfully identifies diverse gene cluster classes, including non-canonical types.
  • The algorithm improves detection accuracy for known functional gene clusters.
  • CLOCI provides a tunable framework for delineating gene cluster families and homologous loci.

Conclusions:

  • CLOCI offers a powerful, unbiased method for gene cluster discovery.
  • This algorithm expands the potential for identifying novel natural products.
  • CLOCI enhances our understanding of genome organization and the evolution of gene clusters.