Computational methods for the analysis of RNA-Seq data

RNA-Seq 数据分析的计算方法

• 批准号: 7899578
• 负责人: Colin Noel Dewey
• 金额: $ 28.18万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7899578
• 关键词: Address; Alternative Splicing; Animal Model; Area; Cell physiology; Cells; Communities; Computer software; Computing Methodologies; Data; Data Analyses; Development; Diagnosis; Disease; Embryo; Environment; Event; Fibroblasts; Frequencies; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Genomics; Goals; Human; Individual; Injury; Knowledge; Learning; Left; Machine Learning; Maps; Measurement; Measures; Messenger RNA; Methods; Modeling; Molecular; Molecular Biology; Organism; Outcome; Output; Protein Isoforms; Protocols documentation; RNA; RNA Sequences; RNA Splicing; RNA analysis; Rana; Reading; Research; Research Personnel; Sampling; Scientist; Simulate; Spliced Genes; Staging; Statistical Models; Stem cells; Structure; Techniques; Technology; Transcript; Undifferentiated; Vertebrates; Work; Xenopus; Xenopus laevis; base; blastomere structure; computerized data processing; genome sequencing; human disease; human embryonic stem cell; infancy; method development; novel; open source; public health relevance; research study; xenopus genome

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.

描述（由申请人提供）：测量细胞内 mRNA 数量的技术是生物医学研究人员工具箱的关键组成部分。基因表达的表征对于理解细胞的分子生物学以及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，因为即使转录水平的微小扰动也可能很重要。最近开发的实验方法 RNA-Seq 有望彻底改变基因表达分析，并促进人类转录组的新发现。 RNA-Seq 数据需要大量计算才能使用，而当前的计算方法仍处于起步阶段。我们建议将 RNA-Seq 计算方法提升到一个新的水平，提高基因表达估计的准确性和可以使用它的场景数量。使用新颖的概率模型和统计学习技术，我们将使该技术能够精确测量选择性剪接事件并表征非模型生物的转录组。我们的计算方法将使用真实和模拟的 RNA-Seq 数据进行验证，并将作为开源软件包免费提供。此外，我们将使用我们开发的方法来探索未分化和分化细胞转录组之间的差异。第一个应用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。第二个应用是使用密切相关的青蛙的基因组序列来估计青蛙非洲爪蟾叶胚胎中的基因表达水平；热带非洲爪蟾（Silurana）作为参考。这些实验的结果预计将增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。 公共健康相关性：拟议的研究旨在开发计算方法，以支持测量细胞内 RNA 数量的技术。借助这项技术和发达的计算方法，研究人员将能够更好地诊断和理解人类疾病的分子基础。