Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-57821-3_34 | ISBRA |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuncheng Liu | 1 | 503 | 49.27 |
| Tao-Chuan Shih | 2 | 0 | 1.01 |
| Tun-Wen Pai | 3 | 127 | 29.71 |
| Chin-Hwa Hu | 4 | 0 | 1.01 |
| Lee-Jyi Wang | 5 | 0 | 1.35 |