Research Initiation Award: Computational Inference of Mechanisms Underlying Double Minute Chromosome Formation

研究启动奖为传统黑人学院和大学的初级和中期职业教师提供支持，他们正在建立新的研究项目或重新指导和重建现有的研究项目。期望该奖项有助于提高教师的研究能力和效率，改善所在机构的研究和教学，并使本科生参与研究经验。路易斯安那州泽维尔大学（Xavier University of Louisiana）获得的这一奖项可能在许多领域产生更广泛的影响。该项目的目标是通过开发新的算法来推进对双分钟染色体的科学理解。这项工作以示范性的方式将教育和研究结合起来，从而有望为该大学新建立的生物信息学轨道做出贡献。该项目的目标是开发，实施和评估算法，以1)通过整合标准的下一代测序数据（sNGS）和染色体构象捕获（Hi-C）数据准确发现双分钟染色体，以及2)预测双分钟形成背后最可能的细胞遗传学机制。双分钟染色体（DM）是染色体外DNA的环状无中心片段，通过提高肿瘤细胞的致癌基因和耐药基因的表达而导致癌症的恶性。该目标将通过开发一种基于图形的算法来整合sNGS和Hi-C数据，以明确确定dm的结构，并通过算法确定每个双分钟形成的机制来实现。虽然以前推断DM机制的研究主要是在实验台上进行的，但本项目提出开发能够自动推断这些机制的软件。这将为帮助开发有效的癌症治疗方法提供必要的信息。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at the home institution, and involves undergraduate students in research experiences. The award to Xavier University of Louisiana has potential broader impacts in a number of areas. The goal of this project is to advance scientific understanding of double minute chromosomes via the development of novel algorithms. This work integrates education and research in an exemplary manner and thus promises to contribute to the newly established Bioinformatics track at the university.The goal of the project is to develop, implement, and evaluate algorithms to 1) accurately discover double minute chromosomes by integrating standard next-generation sequencing data (sNGS) and chromosome conformation capture (Hi-C) data, and 2) to predict the most likely cytogenetic mechanism behind the formation of double minutes. Double minute chromosomes (DM) are circular acentric fragments of extrachromosomal DNA that engender the malignancy of cancer by elevating the expression of the oncogenes and drug resistance genes of tumor cells. The goal will be achieved by developing a graph-based algorithm to integrate sNGS and Hi-C data to unambiguously determine the structure of DMs and algorithmically determining the mechanism that underlies the formation of each double minute. While previous studies to infer DM mechanisms have largely been performed at the bench, this project proposes the development of software that will infer these mechanisms automatically. This will provide essential information for aiding the development of effective cancer therapies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.