A deep learning genome-mining strategy for biosynthetic gene cluster prediction
| Autor: | Danny A. Bitton, Michael Wurst, Grazia Piizzi, Dan Chang, Jakub Kotowski, Lena Rampula, Jindrich Soukup, Andrej Palička, Christopher H. Woelk, David Prihoda, Geoffrey D. Hannigan, Jindrich Durcak, Rurun Wang, Daria J. Hazuda, Gergely Temesi, Ondrej Klempir |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
Genome 030306 microbiology Computational Biology Computational biology Bacterial genome size Biology Small molecule Bacterial genetics Random forest Biosynthetic Pathways 03 medical and health sciences Deep Learning Multigene Family Gene cluster Genetics Methods Online Data Mining Identification (biology) Gene Genome Bacterial 030304 developmental biology |
| Zdroj: | Nucleic Acids Research |
| ISSN: | 1362-4962 0305-1048 |
| Popis: | Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing machine-learning tools. We supplemented this with random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable putative BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a major addition to in-silico BGC identification. |
| Databáze: | OpenAIRE |
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