Computational methods for the analysis of RNA-Seq data

描述(申请人提供)：细胞内信使核糖核酸数量的测量技术是生物医学研究人员S工具箱的关键组件。基因表达的特征对于理解细胞的分子生物学和人类疾病的诊断和治疗都很重要。为了对科学家最有用，RNA测量技术应该尽可能准确和精确，因为即使是转录水平的微小扰动也可能是重大的。最近开发的一种实验方法，RNA-Seq，有望给基因表达分析带来革命性的变化，并使关于人类转录组的新发现成为可能。RNA-Seq数据需要大量的计算才能使用，目前的计算方法仍处于初级阶段。我们建议将RNA-Seq计算方法提升到一个新的水平，既提高了基因表达估计的准确性，也增加了它可能使用的场景数量。使用新的概率模型和统计学习技术，我们将使这项技术能够精确地测量替代剪接事件，并表征非模型生物的转录本。我们的计算方法将用真实和模拟的RNA-Seq数据进行验证，并将作为开放源码软件包免费提供。此外，我们将使用我们开发的方法来探索未分化细胞和已分化细胞转录之间的差异。第一个应用将是表征人类胚胎干细胞和分化的成纤维细胞之间的选择性剪接差异。第二个应用将是利用与非洲爪蛙(Silurana)密切相关的一种青蛙的基因组序列来估计非洲爪蛙叶片胚胎中的基因表达水平。这些实验的结果有望促进我们对脊椎动物细胞分化的理解，并最终促进干细胞用于治疗人类疾病和损伤的潜力。 与公共健康相关：这项拟议的研究旨在开发计算方法，以支持一项测量细胞内RNA数量的技术。有了这项技术和开发的计算方法，研究人员将能够更好地诊断和了解人类疾病的分子基础。

DESCRIPTION (provided by applicant): Technologies for the measurement of mRNA quantities within cells are key components of a biomedical researcher<s toolbox. The characterization of gene expression is important to both the understanding of the molecular biology of the cell and the diagnosis and treatment of human disease. To be most useful to scientists, RNA measurement technologies should be as accurate and precise as possible since even small perturbations in transcript levels may be significant. A recently developed experimental method, RNA-Seq, is promising to revolutionize gene expression analysis and is enabling new discoveries about the human transcriptome. RNA-Seq data demands a significant amount of computation before it can be used and the current computational methods are still in their infancy. We propose to take RNA-Seq computational methods to the next level, increasing both the accuracy of gene expression estimates and the number of scenarios in which it may be used. Using novel probabilistic models and statistical learning techniques, we will enable the technology to precisely measure alternative splicing events and characterize the transcriptomes of non-model organisms. Our computational methods will be validated with both real and simulated RNA-Seq data and will be made freely available as an open source software package. In addition, we will use the methods we develop to explore differences between the transcriptomes of undifferentiated and differentiated cells. A first application will be the characterization of alternative splicing differences between human embryonic stem cells and differentiated fibroblast cells. A second application will be the estimation of gene expression levels in embryos of the frog Xenopus leaves using the genome sequence of a closely related frog; Xenopus (Silurana) tropical is as a reference. The results of these experiments are expected to advance our understanding of cellular differentiation in vertebrates and, ultimately, the potential for stem cells to be used in the treatment of human diseases and injuries. PUBLIC HEALTH RELEVANCE: The proposed research aims to develop computational methods for the support of a technology that measures the quantities of RNA inside of a cell. With this technology and the developed computational methods, researchers will be able to better diagnose and understand the molecular basis of human disease.