Computational Approaches for Studying Transcription Elongation Control in Cancer
Computational Approaches for Studying Transcription Elongation Control in Cancer
批准号:
10516514
负责人:
Marta Iwanaszko
金额:
$14.44万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-09-05 至 2027-08-31
关键词:
AcuteAcute Lymphocytic LeukemiaBase SequenceBioinformaticsCancer BiologyChromatin StructureComplement Factor BComplexComputational BiologyComputer AnalysisDNA Polymerase IIDataData AnalysesDefectDevelopmentGenesGoalsLeadMLL geneMachine LearningMalignant NeoplasmsMethodsMyelogenousOncogenicPositive Transcriptional Elongation Factor BRNAReproducibilityResearchResearch DesignResearch PersonnelSignal TransductionSpeedTechniquesTherapeuticTranscription Elongationhigh standardinsightlarge datasetsnext generationnext generation sequencingnoveltranscription factortumorigenesis
中文摘要
项目摘要
该提案的目标是提供生物信息学支持,以推进对基因表达调控失调的研究。
转录延长在肿瘤发生中的作用例如,混合谱系白血病(MLL)基因的易位
与表达转录延长调节因子的多种基因一起导致急性髓样或急性淋巴样白血病,
白血病这些因子中的许多是超延伸复合物(SEC)的亚基,其可以增强细胞内的细胞凋亡。
转录延伸由两个亚模块:转录因子B的正调节因子(P-TEF B),
暂停Pol II释放的信号,以及RNA持续合成因子ELL。其中一个共同点是,
项目是需要细致的,生物相关的计算分析,最终有几个
功能:将下一代测序数据解码成一组有用的发现,将来自多个测序中心的数据
多维视角的实验平台,并整合机器学习方法,
提取数据特定的签名。这些功能服务于帮助研究人员的目标,从学习开始
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,
帮助评估癌症治疗方法。Iwanaszko博士将负责实施这些方法,
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的信息,加快分析速度,并保持高标准的科学严谨性
和再现性。
英文摘要
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.
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Computational Approaches for Studying Transcription Elongation Control in Cancer
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批准号:10697319
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项目类别:
-
资助金额:$14.44万
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财政年份:2022
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负责人:Marta Iwanaszko
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依托单位:
海外基金