Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery.

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.

CodaChrome: a tool for the visualization of proteome conservation across all fully sequenced bacterial genomes.

BACKGROUND: The relationships between bacterial genomes are complicated by rampant horizontal gene transfer, varied selection pressures, acquisition of new genes, loss of genes, and divergence of genes, even in closely related lineages. As more and more bacterial genomes are sequenced, organizing and interpreting the incredible amount of relational information that connects them becomes increasingly difficult. RESULTS: We have developed CodaChrome (http://www.sourceforge.com/p/codachrome), a one-versus-all proteome comparison tool that allows the user to visually investigate the relationship between a bacterial proteome of interest and the proteomes encoded by every other bacterial genome recorded in GenBank in a massive interactive heat map. This tool has allowed us to rapidly identify the most highly conserved proteins encoded in the bacterial pan-genome, fast-clock genes useful for subtyping of bacterial species, the evolutionary history of an indel in the Sphingobium lineage, and an example of horizontal gene transfer from a member of the genus Enterococcus to a recent ancestor of Helicobacter pylori. CONCLUSION: CodaChrome is a user-friendly and powerful tool for simultaneously visualizing relationships between thousands of proteomes.