Nicholas J. Dimonaco

TL;DR: DeepViral as discussed by the authors embeds human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies, and predicts protein-protein interactions (PPI) between humans and viruses.

TL;DR: In this paper, the authors employed a collection of contemporary methodologies to compare the genomic sequences of coronaviruses isolated from human (SARS-CoV-2; n = 163), bat (bat-coV; n ≥ 215), and pangolin (pangolin-CoVs; n= 7) available in public repositories.

TL;DR: DeepViral is developed, a deep learning based method that predicts protein–protein interactions (PPI) between humans and viruses that significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction.

TL;DR: DeepGO is modified to perform protein function prediction directly from sequence reads with limited protein coding sequence prediction, and evaluated the functions predicted from the unassembled sequence reads and the protein coding sequences predicted fromThe assembled metagenome.

TL;DR: StORF-R is presented, a tool that takes as input an annotated genome and returns putative missed genes (functional and/or pseudogenised) from the intergenic regions and shows that this methodology can recover gene-families that the state-of-the-art methods continue to misreport or completely omit.