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bioRxiv · 10.1101/2025.01.20.633932

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.
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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.

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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. A linked Genomics Sequencing and Mass Spectrometry multi-modal dataset and models for Streamlined Natural Products Discovery in Microbial Strain Libraries. https://doi.org/10.1101/2025.01.20.633932

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