Statistical Methods for Transcriptome Profiling Using RNA Sequencing

使用 RNA 测序进行转录组分析的统计方法

• 批准号: 8840978
• 负责人: Mingyao Li
• 金额: $ 29.59万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8840978
• 关键词: Accounting; Address; Age related macular degeneration; Alleles; Alternative Splicing; Attention; Basic Science; Biological; Cardiovascular Diseases; Cardiovascular system; Cells; Clinical Research; Collaborations; Complex; Computer software; DNA; Data; Detection; Development; Diploidy; Disease; Elements; Endotoxemia; Event; Eye diseases; Face; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Gene Expression Regulation; Gene Fusion; Genes; Genetic Transcription; Genome; Genomics; Haplotypes; Harvest; Health; Heart failure; High-Throughput Nucleotide Sequencing; Human; Individual; Maps; Measures; Messenger RNA; Methods; Modification; Nucleotides; Pathogenesis; Pennsylvania; Performance; Pharmacotherapy; Post-Transcriptional RNA Processing; Protein Isoforms; Proteins; Publishing; RNA; RNA Editing; RNA Sequence Analysis; RNA Sequences; Reading; Regulation; Research Personnel; Resolution; Rest; Sampling; Scientific Advances and Accomplishments; Shapes; Site; Statistical Methods; Statistical Models; Stimulus; Stress; Techniques; Technology; Testing; Tissue-Specific Gene Expression; Tissues; Transcript; Translations; Universities; Untranslated RNA; Variant; Work; differential expression; experience; human disease; insight; interest; method development; novel; open source; research study; response; simulation; transcriptome sequencing; transcriptomics

DESCRIPTION (provided by applicant): A transcriptome represents all transcribed sequences in a given cell. Unlike a genome, which is static, the transcriptome can be quickly restructured by changing the rate of synthesis or decay of individual mRNAs in response to external environmental conditions. Tissue and cell specific transcriptomic changes during pathophysiological stress, in disease versus health and in response to drug therapies are of particular interest to investigators studying human diseases. RNA-Sequencing (RNA-Seq) is an emerging approach that allows a comprehensive analysis of the entire transcriptome in a high-throughput manner. With deep coverage and single nucleotide resolution, RNA-Seq provides a platform to determine differential expression of genes or isoforms, alternative splicing, non-coding RNAs, post-transcriptional modifications, and gene fusions. Although studies using RNA-Seq have altered our view of the extent and complexity of eukaryotic transcriptomic variations, like other high-throughput sequencing technologies, RNA-Seq faces several analytical challenges. Fully harvesting the power of this newly developed technique requires the development of effective statistical methods. Building upon our expertise in statistical methods development and experience with analysis of genomics data for complex human diseases, we propose to develop novel statistical methods that allow robust detection of transcriptomic variations. Our specific aims are to: 1) Develop statistical methods to analyze isoform-specific gene expression and alternative splicing. 2) Develop statistical methods to identify RNA editing events. 3) Apply the proposed methods to RNA-Seq data generated from ongoing collaborations on transcriptomics studies of experimental endotoxemia, heart failure, and age-related macular degeneration. 4) Develop open source software packages for methods proposed in this application. This proposal addresses critical analytical challenges regarding the analysis of RNA-Seq data. Our methods will make efficient use of existing RNA-Seq data generated from ongoing cardiovascular and ocular transcriptomics studies. The successful completion of this work will allow biologists to better disentangle complex cellular circuitry, precisely related genomic sequence to gene regulation, and facilitate the translation of basic research findings into clinical studies of cardiovascular and eye diseases.

描述（由申请人提供）： 转录组代表给定细胞中的所有转录序列。与静态的基因组不同，转录组可以通过改变单个mRNA的合成或衰变速率来快速重构，以响应外部环境条件。组织和细胞特异性转录组学的变化，在病理生理应激，在疾病与健康和药物治疗的反应是特别感兴趣的研究人员研究人类疾病。RNA测序（RNA-Seq）是一种新兴的方法，其允许以高通量方式对整个转录组进行全面分析。凭借深度覆盖和单核苷酸分辨率，RNA-Seq提供了一个平台来确定基因或异构体的差异表达，选择性剪接，非编码RNA，转录后修饰和基因融合。虽然使用RNA-Seq的研究改变了我们对真核转录组变异的程度和复杂性的看法，但与其他高通量测序技术一样，RNA-Seq面临着几个分析挑战。要充分利用这种新开发的技术，需要开发有效的统计方法。基于我们在统计方法开发方面的专业知识和对复杂人类疾病的基因组学数据进行分析的经验，我们建议开发新的统计方法，以实现对转录组变异的稳健检测。我们的具体目标是：1）发展统计方法来分析异构体特异性基因表达和选择性剪接。2)开发统计方法来识别RNA编辑事件。3)将所提出的方法应用于正在进行的实验性内毒素血症、心力衰竭和年龄相关性黄斑变性的转录组学研究合作产生的RNA-Seq数据。4)为本申请中提出的方法开发开源软件包。该提案解决了有关RNA-Seq数据分析的关键分析挑战。我们的方法将有效利用现有的RNA-Seq数据，这些数据来自正在进行的心血管和眼部转录组学研究。这项工作的成功完成将使生物学家能够更好地解开复杂的细胞回路，精确地将基因组序列与基因调控联系起来，并促进基础研究成果转化为心血管和眼科疾病的临床研究。