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

联系PD/PI：李静怡 项目摘要 到目前为止，下一代Illumina RNA测序(rna-seq)是使用最广泛的 用于研究动物转录本的分析方法和大量公开的RNA-SEQ数据集 已经在多个物种的不同生物条件下产生了。然而， 在使用短的rna-seq读数来准确识别 剪接结构和量化全长RNA转录本的丰度。在这 建议，我们将开发一系列新的统计和计算方法来 提高成绩单识别的健壮性和成绩单的准确性 从Illumina RNA-seq数据中进行量化。(目标1)我们将开发一种新的筛查 通过首先检测稀疏剪接结构来构建候选转录本的方法 从针对给定生物条件的多个RNA-SEQ数据集中。这些成绩单 考生将显著减少下游成绩单的搜索空间 识别方法，从而提高其精确度。(目标2)我们将制定一项 一种稳健的文本识别方法在保守性条件下识别新的文本 根据给定现有注释的RNA-Seq数据的方式。我们的方法将基于 奈曼-皮尔逊范式下的统计模型选择，这将允许用户 在任何给定条件下控制我们识别的新成绩单的假阳性率 概率很高的阈值。(目标3)我们将制定一份准确的成绩单 一种有效利用多个RNA-SEQ数据集的量化方法 同时评估基于低吞吐量黄金标准和 跨数据相似性。所有这些方法都将首次用于研究 小鼠巨噬细胞，其金标准qPCR和全长cDNAs序列将 为培训和方法验证生成。然后，这些方法将更加广泛。 在有合适的黄金标准数据的其他生物系统中进行了测试。我们的 方法和软件将大大便利将Illumina RNA-seq数据用于 在转录本水平上进行基因表达研究，增加科学研究的重复性 来自转录研究的发现，并提高我们对基因的理解 在各种生物条件下的表达机制。 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