Robust identification and accurate quantification of RNA transcripts on a system wide scale

联系PD/PI：Li，Jingyi 项目摘要 下一代，Illumina RNA测序（RNA-seq）是迄今为止使用最广泛的 用于研究动物转录组的测定，以及许多公共RNA-seq数据集 已经在多个物种的各种生物条件下产生。然而，在这方面， 在使用短RNA-seq读段来准确地鉴定 剪接结构和量化的丰度全长RNA转录本。在这 建议，我们将开发一系列新的统计和计算方法， 提高转录本识别的鲁棒性和转录本的准确性 来自Illumina RNA-seq数据的定量。(Aim 1）我们将开发一种新的筛选方法 通过首先检测稀疏剪接结构构建转录本候选物的方法 从给定生物条件的多个RNA-seq数据集。这些成绩单 候选人将大大减少下游成绩单的搜索空间 识别方法，从而提高其精度。(Aim（2）我们将开发一个 一种稳健的转录本鉴定方法， 方法从RNA-seq数据给出现有的注释。我们的方法将基于 Neyman-Pearson范式下的统计模型选择，这将允许用户 在任何给定的条件下，控制我们鉴定的新转录本的假阳性率， 门槛高概率。(Aim 3）我们将制定准确的成绩单 一种有效利用多个RNA-seq数据集的定量方法， 同时根据低吞吐量黄金标准评估数据质量， 交叉数据相似性。所有这些方法将首先用于研究成绩单， 小鼠巨噬细胞，其金标准qPCR和全长cDNA序列将 用于培训和方法验证。这些方法将更广泛地 在其他生物系统中进行测试，其中可获得合适的金标准数据。我们 方法和软件将显著促进Illumina RNA-seq数据的使用， 在转录水平上的基因表达研究，增加了科学研究的可重复性。 转录组学研究的发现，并提高我们对基因的理解， 在各种生物学条件下的表达机制。 项目摘要/摘要第6页

Contact PD/PI: Li, Jingyi Project Summary Next-generation, Illumina RNA sequencing (RNA-seq) is by far the most widely used assay for investigating animal transcriptomes, and numerous public RNA-seq data sets have been generated for various biological conditions in multiple species. However, there remain several barriers in using short RNA-seq reads to accurately identify the splicing structures and quantify the abundances of full-length RNA transcripts. In this proposal, we will develop a series of novel statistical and computational methods to improve the robustness of transcript identification and the accuracy of transcript quantification from Illumina RNA-seq data. (Aim 1) We will develop a novel screening method to construct transcript candidates by first detecting sparse splicing structures from multiple RNA-seq data sets for a given biological condition. These transcript candidates will significantly reduce the search space of downstream transcript identification methods and hence improve their precision. (Aim 2) We will develop a robust transcript identification method to identify novel transcripts in a conservative manner from RNA-seq data given existing annotations. Our method will be based on statistical model selection under the Neyman-Pearson paradigm, which will allow users to control the false positive rate of our identified novel transcripts under any given threshold with high probability. (Aim 3) We will develop an accurate transcript quantification method to effectively leverage multiple RNA-seq data sets and to simultaneously assess the data quality based on low-throughput gold standards and cross-data similarities. All of these methods will be first used to study transcripts in mouse macrophage, for which gold standard qPCR and full length cDNA sequences will be generated for training and method validation. The methods will then be more broadly tested in other biological systems where suitable gold standard data is available. Our methods and software will significantly facilitate the use of Illumina RNA-seq data for gene expression studies at the transcript level, increase reproducibility of scientific discoveries from transcriptomic studies, and improve our understanding of gene expression mechanisms in various biological conditions. Project Summary/Abstract Page 6