Multi-omics data integration for the discovery of COVID-19 drug targets

The novel coronavirus SARS-Cov-2 continues to have adverse impacts on human health. Despite the volume of experiments performed and data available, its biology is not yet fully understood. Functional omics technologies such as high throughput sequencing and mass spectrometry allow users to capture large quantities of complex data. From these individual data modalities, it is possible to extract valuable information associated with a biological system under study, leading to new discoveries and a deeper knowledge of biology. However, combining these blocks of information can yield information that is not visible with a single data modality.To better understand this virus, we take a multi-omics integrative view of the data, combining both proteomics and translatome data. This is in contrast to existing studies which mostly focus on a single aspect of functional omics data, primarily the genome. As a result of this fragmented view, valuable information may be masked. Using a latent variable approach, our integrative pipeline unifies proteome and translatome. We compared the features of interest contributing to each biological outcome across the individual data blocks and the integrated omics data. This revealed previously invisible and potentially medically relevant features for drug development.