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CLOCI: unveiling cryptic fungal gene clusters with generalized detection.
Zachary Konkel1,2, Laura Kubatko3,4, Jason C Slot1,2
1Department of Plant Pathology, The Ohio State University, Columbus, OH 43210, USA.
Nucleic Acids Research
|July 17, 2024
Summary
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.
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.
