Computational Approaches for Studying Transcription Elongation Control in Cancer

项目摘要 这项提案的目标是提供生物信息学支持，以推进对监管不当的研究 转录延伸在肿瘤发生中的作用。例如，混合血统白血病(MLL)基因的易位 与多种基因表达的转录调节延长导致急性髓系或急性淋巴系 白血病。其中许多因子是超级延长复合体(SEC)的亚单位，它可以增强 转录延伸的两个亚模块：转录因子b的正调控因子(P-TEFb)，它 释放暂停的POL II的信号和RNA加工性因子ELL。它们的一个共同点是 项目需要细致的、与生物相关的计算分析，这最终有几个 功能：将下一代测序数据解码为一组有用的发现，将来自 多维视角的实验平台，并将机器学习方法集成到 提取特定于数据的签名。这些功能服务于帮助研究人员的目标，从研究开始 设计、数据分析和进一步的假设建立，以促进我们对癌症生物学的理解和 帮助评估癌症治疗方法。Iwanaszko博士将负责实施这些方法和 提供生物信息学和计算生物学方面的专业知识，以支持癌症生物学家提取 从他们的数据中获得更多信息，加快分析速度，并保持高标准的科学严谨性 和再现性。

Project Summary 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.