Systems biology provides opportunities to fully understand the genes and pathways in disease pathogenesis. We used literature knowledge and unbiased multiple data meta-analysis paradigms to analyze microarray datasets across different mouse strains and acute allergic asthma models. Our combined gene-driven and pathway-driven strategies generated a stringent signature list totaling 933 genes with 41% (440) asthma-annotated genes and 59% (493) ignorome genes, not previously associated with asthma. Within the list, we identified inflammation, circadian rhythm, lung-specific insult response, stem cell proliferation domains, hubs, peripheral genes, and super-connectors that link the biological domains (Il6, Il1ß, Cd4, Cd44, Stat1, Traf6, Rela, Cadm1, Nr3c1, Prkcd, Vwf, Erbb2). In conclusion, this novel bioinformatics approach will be a powerful strategy for clinical and across species data analysis that allows for the validation of experimental models and might lead to the discovery of novel mechanistic insights in asthma.