LAUSR

MOTIVATION Cells respond to environments by regulating gene expression to exploit resources optimally. Recent advances in technologies allow measuring the abundances of transcripts, proteins, lipids and metabolites. These highly complex datasets reflect the state of the different layers in a biological system. Multi-omics is the integration of these disparate methods and data to gain a clearer picture of the biological state. Multi-omic studies of the proteome and metabolome are becoming more common as mass spectrometry technology continues to be democratized. However, knowledge extraction through integration of these data remains challenging. RESULTS Connections between molecules in different omic layers were discovered through a combination of machine learning and model interpretation. Discovered connections reflected protein control over metabolites. Proteins discovered to control citrate were mapped onto known genetic and metabolic networks, revealing that these protein regulators are novel. Further, clustering the magnitudes of protein control over all metabolites enabled prediction of five gene functions, each of which was validated experimentally. Two uncharacterized genes, YJR120W and YLD157C, were accurately predicted to modulate mitochondrial translation. Functions for three incompletely characterized genes were also predicted and validated, including SDH9, ISC1, and FMP52. A website enables results exploration and also MIMaL analysis of user-supplied multi-omic data. AVAILABILITY The website for MIMaL is at https://mimal.appCode for the website is at https://github.com/qdickinson/mimal-websiteCode to implement MIMaL is at https://github.com/jessegmeyerlab/MIMaL. SUPPLEMENTARY INFORMATION Supplementary figures are available at Bioinformatics online.Supporting data are available at https://doi.org/10.5281/zenodo.6537297MS data are available under the identifier MSV000090100 at https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=ba70b1440b2b4c488323fa6644b332cb.