This project develops computational methods to investigate the role of post-transcriptional regulation in cancer patients using both high-throughput sequencing data and molecular networks to improve the understanding of cancer and assess the cause of the disease in a patient. Deregulation of gene expression is a hallmark of the human tumor cells. Post-transcriptional regulation is a pervasive mechanism in the relation of most human genes; its implication in cancer is only beginning to be appreciated. The central hypothesis underlying this project is that by considering post-transcriptional regulation events, estimated protein expressions can provide more accurate molecular signatures to detect complex disease mechanisms, compared to mRNA expressions. This project studies post-transcriptional regulation, with the goal of generating computational methods to predict the changes of protein expression level without doing large-scale proteomics experiments. The outcomes of the proposed research lower the barriers for interacting with analyzing high-dimensional genomic profiles and cut time and costs spent on biomedical research. The new methods enable biologists and biomedical researchers to perform comprehensive analysis with high-throughput sequencing data and biological networks together to investigate the impact of post-transcriptional regulation in certain cancer types.
This study is supported by NSF.