Project description:Metabolomics provides a direct functional readout of a tumor’s physiology. Yet, it is lagging behind other omics technologies in facilitating disease monitoring and prognostication. This stems partly from the scarcity of large-scale metabolomic studies, but also the analytical complexities of detecting diverse metabolites with varying physicochemical properties and concentrations. To address this, we developed a machine learning framework using both tumor tissue and cell line samples across multiple cancer types that allows prediction of metabolomics from gene expression data. Two different model types were selected and trained for tissues and cell lines with their generalization capacity validated on independent cohorts, accurately predicting as high as 70-80% of tested metabolites. This work offers a scalable and efficient machine learning pipeline to determine metabolic from transcriptomic signatures, opening avenues to reconstruct and study the metabolic landscape of samples across novel and existing datasets lacking direct metabolomics measurements.
Project description:Non-coding RNAs have increasingly recognized roles in critical molecular mechanisms of disease. However, the non-coding genome of Drosophila melanogaster, one of the most powerful disease model organisms, has been understudied. Here, we present FLYNC – FLY Non-Coding RNA discovery and classification – a novel machine learning model that predicts the probability of a newly identified RNA transcript being a long non-coding RNA (lncRNA). Integrated into an end-to-end bioinformatics pipeline capable of processing single-cell or bulk RNA sequencing data, FLYNC outputs potential new non-coding RNA genes. FLYNC leverages large-scale genomic and transcriptomic datasets to identify patterns and features that distinguish non-coding genes from protein-coding genes, thereby facilitating lncRNA prediction. We demonstrate the application of FLYNC to publicly available Drosophila adult head bulk transcriptome and single-cell transcriptomic data from Drosophila neural stem cell lineages and identify several novel tissue- and cell-specific lncRNAs. We have further experimentally validated the existence of a set of FLYNC predicted lncRNAs by RT-PCR and RNA PolII binding. Overall, our findings demonstrate that FLYNC serves as a robust tool for identifying lncRNAs in Drosophila melanogaster, transcending current limitations in ncRNA identification and harnessing the potential of machine learning.