Separating Drivers from Passengers in Whole Genome Analysis: Identification of Combinatorial Effects of Genes by Mining Knowledge Sources

Abstract—This study aimed to develop a new informatics platform for the discovery, recovery and multi-level analysis of the effects of individual genes and multiple gene combinations on pathophenotypes of bacteria. Natural language processing algorithms were employed to extract gene-disease associations from PubMed literature and annotated genomes of bacteria with epidemic potential. From these associations gene virulence profiles were generated enabling the comparison of gene signatures within and across genomes. It allowed the identification of virulence genes and construction of their association networks as well as the detection of knowledge gaps. This proof-of-concept study confirmed the feasibility of our original approach for integrating bacterial genome level knowledge with published observations from clinical settings