Enhanced Functional Pathway Annotations for Differentially Expressed Gene Clusters

Biological pathway enrichment analysis is mainly applied to interpret correlated behaviors of activated gene clusters. In traditional approaches, significant pathways were highlighted based on hypergeometric distribution statistics and calculated P-values. However, two important factors are ignored for enrichment analysis, including fold-change levels of gene expression and gene locations on biological pathways. In addition, several reports have shown that noncoding RNAs could inhibit/activate target genes and affect the results of over-representation analysis. Hence, in this study, we provided an alternative approach to enhance functional gene annotations by considering different fold-change levels, gene locations in a pathway, and non-coding RNA associated genes simultaneously. By considering these additional factors, the ranking of significant P-values would be rearranged and several important and associated biological pathways could be successfully retrieved. To demonstrate superior performance, we used two experimental RNA-seq datasets as samples, including Birc5a and HIF2α knocked down in zebrafish during embryogenesis. Regarding Birc5a knock-down experiments, two biological pathways of sphingolipid metabolism and Herpes simplex infection were additionally identified; for HIF2α knock-down experiments, four missed biological pathways could be re-identified including ribosome biogenesis in eukaryotes, proteasome, purine metabolism, and complement and coagulation cascades. Thus, a comprehensive enrichment analysis for discovering significant biological pathways could be overwhelmingly retrieved and it would provide integrated and suitable annotations for further biological experiments.