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Automating Assessment of the Undiscovered Biosynthetic Potential of Actinobacteria

View ORCID ProfileBogdan Tokovenko, View ORCID ProfileYuriy Rebets, View ORCID ProfileAndriy Luzhetskyy
doi: https://doi.org/10.1101/036087
Bogdan Tokovenko
1Actinomycetes Metabolic Engineering Group, Helmholtz Institute for Pharmaceutical Research Saarland, Saarbrücken, Germany
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  • For correspondence: Andriy.Luzhetskyy@helmholtz-hzi.de to.bogdan@gmail.com
Yuriy Rebets
1Actinomycetes Metabolic Engineering Group, Helmholtz Institute for Pharmaceutical Research Saarland, Saarbrücken, Germany
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Andriy Luzhetskyy
1Actinomycetes Metabolic Engineering Group, Helmholtz Institute for Pharmaceutical Research Saarland, Saarbrücken, Germany
2Department of Pharmaceutical Biotechnology, Faculty of Natural Sciences and Technology, University of Saarland, Saarbrücken, Germany
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  • For correspondence: Andriy.Luzhetskyy@helmholtz-hzi.de to.bogdan@gmail.com
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Abstract

Background. Biosynthetic potential of Actinobacteria has long been the subject of theoretical estimates. Such an estimate is indeed important as a test of further exploitability of a taxon or group of taxa for new therapeutics. As neither a set of available genomes nor a set of bacterial cultivation methods are static, it makes sense to simplify as much as possible and to improve reproducibility of biosynthetic gene clusters similarity, diversity, and abundance estimations.

Results. We have developed a command-line computational pipeline (available at https://bitbucket.org/qmentis/clusterscluster/) that assists in performing empirical (genome-based) assessment of microbial secondary metabolite gene clusters similarity and abundance, and applied it to a set of 208 complete and de-duplicated Actinobacteria genomes. After a brief overview of Actinobacteria biosynthetic potential as compared to other bacterial taxa, we use similarity thresholds derived from 4 pairs of known similar gene clusters to identify up to 40-48% of 3247 gene clusters in our set of genomes as unique. There is no saturation of the cumulative unique gene clusters curve within the examined dataset, and Heap’s alpha is 0.129, suggesting an open pan-clustome. We identify and highlight pitfalls and possible improvements of genome-based gene cluster similarity measurements.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 07, 2016.
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Automating Assessment of the Undiscovered Biosynthetic Potential of Actinobacteria
Bogdan Tokovenko, Yuriy Rebets, Andriy Luzhetskyy
bioRxiv 036087; doi: https://doi.org/10.1101/036087
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Automating Assessment of the Undiscovered Biosynthetic Potential of Actinobacteria
Bogdan Tokovenko, Yuriy Rebets, Andriy Luzhetskyy
bioRxiv 036087; doi: https://doi.org/10.1101/036087

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