Statistical analysis of RNA-Seq data

描述（由申请人提供）：RNA-Seq是最近开发的技术，能够提供细胞样品中RNA群体的全面的核苷酸序列水平调查。该项目的目的是开发严格的统计方法和高效的计算机程序，以便有效分析RNA-Seq实验产生的大量数据。具体而言，我们将以以下目的进行研究。目标1：读取速率的非均匀性建模：已知读取速率可以根据相同转录物上读取的位置而显著变化，并且这种非均匀性可以诱导表达定量的偏差。我们将模拟读取速率如何取决于局部序列背景，并设计方法来校正由非均匀速率引起的偏差。目标二：异构体特异性表达的推断：即使异构体是已知的，如何将双端数据纳入异构体表达定量推断的统计框架中的问题也是一个悬而未决的问题。我们将发展必要的统计理论和方法来解决这一重要问题。目标3：剪接点的定位、比对和检测：我们将设计计算方法来将读数与参考基因组进行定位和比对，并将根据比对结果开发剪接点检测方法。目标4：亚型的从头推断：前面的目标的结果将被整合和扩展，以开发一个统计框架，用于推断一个遗传位点中表达的异构体的集合。基于这个框架，我们将设计算法来发现表达的异构体集并量化它们的表达。目标5：开发RNA-Seq数据分析软件：我们将创建一个软件应用程序来支持RNA-Seq数据分析。从原始序列读数作为输入开始，该软件将允许映射到已知的转录本数据库，发现和显示新的转录本或亚型，读数的可视化和计算亚型特异性表达和相关的统计总结。通过创建统计和计算工具，从RNA-seq数据中提取有用的信息，该项目将加速与人类健康相关的许多研究领域。 公共卫生相关性：Wong博士和他的实验室成员将研究与大规模并行测序技术产生的mRNA数据分析相关的几个问题。他们将开发用于推断异构体和异构体特异性表达的统计模型。通过创建能够从RNA-seq数据中提取有用信息的工具，该项目将加速与人类健康相关的许多研究领域。

DESCRIPTION (provided by applicant): RNA-Seq is a recently developed technology capable of providing comprehensive, nucleotide sequence-level survey of the RNA population in a sample of cells. The purpose of this project is to develop rigorous statistical methods and efficient computer programs that will allow the effective analysis of the massive amount of data produced by RNA-Seq experiments. Specifically, we will conduct research with the following aims. Aim 1: Modeling non-uniformity of read rates: It is known that read rates can vary substantially depending on the position of the reads on the same transcript and that such non-uniformity can induce biases in expression quantification. We will model how the read rate may depend on local sequence context, and design methods to correct for biases caused by non-uniform rates. Aim 2: Inference of isoform-specific expression: Even when the isoforms are known, the issue of how paired-end data can be incorporated into the statistical framework for quantitative inference of isoform expression is an open problem. We will develop the necessary statistical theory and methods to resolve this important issue. Aim 3: Mapping, alignment and detection of splice junctions: We will design computational methods to map and alignment the reads to the reference genome, and will develop methods for the detection of splice junctions based on the alignment results. Aim 4: De Novo inference of isoforms: The results of the previous aims will be integrated and extended to develop a statistical framework for inferring the set of expressed isoforms in a genetic locus. Based on this framework, we will design algorithms to discover the set of expressed isoforms and to quantify their expressions. Aim 5: Development of software for RNA-Seq data analysis: We will create a software application to support the analysis of RNA-Seq data. Starting from raw sequence reads as input, this software will allow the mapping to known transcript databases, discovery and display of new transcripts or isoforms, visualization of reads and computation of isoform-specific expression and associated statistical summaries. By creating the statistical and computational tools to enable extraction of useful information from RNA-seq data, this project will accelerate many areas of research relevant to human health. PUBLIC HEALTH RELEVANCE: Dr. Wong and his lab members will conduct research on several problems related to the analysis of mRNA data produced by massively parallel sequencing technologies. They will develop statistical models for the inference of isoforms and isoform-specific expression. By creating the tools to enable extraction of useful information from RNA-seq data, this project will accelerate many areas of research relevant to human health.