Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation.

Metabolite annotation has been a challenging issue especially in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limitations of publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known metabolites. Machine learning provides the opportunity to predict molecular fingerprints based on MS/MS data. The predicted molecular fingerprints can then be used to help rank putative metabolite IDs obtained by using either the precursor mass or the formula of the unknown metabolite. This method is particularly useful to help annotate metabolites whose corresponding MS/MS spectra are missing or cannot be matched with those in accessible spectral libraries. We investigated a convolutional neural network (CNN) for molecular fingerprint prediction based on data acquired by MS/MS. We used more than 680,000 MS/MS spectra obtained from the MoNA repository and NIST 20, representing about 36,000 compounds for training and testing our CNN model. The trained CNN model is implemented as a python package, MetFID. The package is available on GitHub for users to enter their MS/MS spectra and corresponding putative metabolite IDs to obtain ranked lists of metabolites. Better performance is achieved by MetFID in ranking putative metabolite IDs using the CASMI 2016 benchmark dataset compared to two other machine learning-based tools (CSI:FingerID and ChemDistiller).

Integrative Analysis to Identify Race-Associated Metabolite Biomarkers for Hepatocellular Carcinoma.

Compared to European-Americans (EAs), the incidence of hepatocellular carcinoma (HCC) is higher in African-Americans (AAs) and is associated with more advanced tumor stage at diagnosis and lower survival rates. The increasing burden makes discovery of novel diagnostic, prognostic, and therapeutic biomarkers distinguishing HCC from underlying cirrhosis a significant focus. In this study, we analyzed tissue and serum samples from 40 HCC cases and 25 patients with liver cirrhosis to identify candidate biomarkers that distinguish HCC from cirrhotic patients in a race specific manner. Through integrative analysis of transcriptomic and metabolomic data, we investigated candidate metabolite biomarkers that are specific to AAs and EAs. The results from this demonstrate the utility of integrating transcriptomic and metabolomic data to prioritize clinically and biologically relevant metabolite biomarkers that can increase understanding of molecular mechanisms driving HCC in different racial groups.