Computational Approaches for Studying Transcription Elongation Control in Cancer

The goal of this proposal is to provide bioinformatics support to advance research into the misregulation of transcription elongation in oncogenesis. For example, translocation of the mixed lineage leukemia (MLL) gene with a variety of genes expressing regulators of transcription elongation result in acute myeloid or acute lymphoid leukemia. Many of these factors are subunits of the Super Elongation Complex (SEC), which can enhance transcription elongation by two submodules: the positive regulator of transcription factor b (P-TEFb), which signals for the release of paused Pol II, and the RNA processivity factor ELL. A common denominator of these projects is the need for meticulous, biologically relevant computational analysis, which ultimately have several functions: decoding next generation sequencing data into a useful set of findings, combining data from multiple experimental platforms for a multi-dimensional perspective, and integrating machine learning approaches to extract data-specific signatures. These functions serve the goal of helping researchers, beginning with study design, data analysis, and further hypothesis building, to advance our understanding of cancer biology and to help evaluate cancer therapeutics. Dr. Iwanaszko will be responsible for implementing these approaches and providing expertise in both bioinformatics and computational biology to support cancer biologists in extracting more information from their data, speeding up the analysis, and to maintain a high standard of scientific rigor and reproducibility.