Cell-free genome-wide transcriptomics through machine learning optimization
Résumé
Despite advances in transcriptomics, understanding of genome regulation remains limited by the complex interactions within living cells. To address this, we performed cell-free transcriptomics by developing a platform using an active learning workflow to explore over 1,000,000 buffer conditions. This enabled us to identify a buffer that increased mRNA yield by 20-fold, enabling cell-free transcriptomics. By employing increasingly complex conditions, our approach untangles the regulatory layers controlling genome expression.
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Est complété par 10.1101/2025.06.04.657821 Autre Wagner, L., Hoang, A., Rue, O., Delumeau, O., Loux, V., Faulon, J.-L., Jules, M., & Borkowski, O. (2025). Cell-free genome-wide transcriptomics through machine learning optimization. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2025.06.04.657821
BioRxiv