Abstract
AbstractPutative disease-associated genes are often identified among those genes that are differentially expressed in disease and in normal conditions. This strategy typically yields thousands of genes. Gene prioritizing schemes boost the power of identifying the most promising disease-associated genes among such a set of candidates. We introduce here a novel system for prioritizing genes where a TF-miRNA co-regulatory network is constructed for the set of genes, while the ranks of the candidates are determined by topological and biological factors. For datasets on breast invasive carcinoma and liver hepatocellular carcinoma this novel prioritization technique identified a significant portion of known disease-associated genes and suggested new candidates which can be investigated later as putative disease-associated genes.
Funder
Deutsche Forschungsgemeinschaft
Publisher
Springer Science and Business Media LLC
Reference29 articles.
1. Soneson, C. & Delorenzi, M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14, 91+ (2013).
2. Ching, T., Huang, S. & Garmire, L. X. Power analysis and sample size estimation for RNA-Seq differential expression. RNA 20, 1684–1696 (2014).
3. Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37, W305–W311 (2009).
4. Chen, J., Aronow, B. J. & Jegga, A. G. Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinformatics 10, 1–14 (2009).
5. Moreau, Y. & Tranchevent, L.-C. C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nature reviews. Genetics 13, 523–536 (2012).
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献