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A linked Genomics Sequencing and Mass Spectrometry multi-modal dataset and models for Streamlined Natural Products Discovery in Microbial Strain Libraries

Tay, D. W. P.; Koh, W.; Ang, S. J.; Wong, Z. M.; Lim, Y. W.; Heng, E.; Yeo, N. Z. X.; Adaikkappan, K.; Wong, F. T.; Lim, Y. H.

2025-01-21 synthetic biology
10.1101/2025.01.20.633932 bioRxiv
Show abstract

An integrated multi-modal characterization of a microbial strain library streamlines the effort for natural product discovery. By integrating language- and transformer-based models to cross-validate mass spectrometry (MS)-genome datasets, microbial producers of diverse natural products are rapidly identified with high (75-100%) precision. Our findings demonstrate the transformative potential of linked MS-genome datasets at the strain-level to significantly accelerate discovery and enhance our understanding of microbes beyond currently known and curated knowledge.

Published in npj Antimicrobials and Resistance · training set

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