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

在系统范围内对 RNA 转录本进行稳健的识别和准确的定量

基本信息

批准号：
10394065
负责人：
Jingyi Jessica Li
金额：
$ 0.94万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10394065
关键词：
Affect Algorithms Animals Bayesian Method Bayesian Modeling Biological Biological Assay Biological Phenomena Biological Process Cell Differentiation process Communities Complementary DNA Complex Computer software Computing Methodologies Confidence Intervals Data Data Set Exons Gene Chips Gene Expression Genes Genome Genotype-Tissue Expression Project Goals Gold Guanine + Cytosine Composition Individual International Lead Length Malignant Neoplasms Methods Minority Groups Modeling Molecular Mus Noise Probability Procedures RNA RNA Splicing Reproducibility Resources Sampling Series Signal Transduction Statistical Methods Statistical Models Structure System Techniques Technology Testing The Cancer Genome Atlas Training Transcript Uncertainty Validation Voting base biological systems cost data quality data standards design improved insertion/deletion mutation macrophage next generation novel open source reference genome screening serial analysis of gene expression supervised learning tool transcriptome transcriptome sequencing transcriptomics

项目摘要

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

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，使用短RNA-seq读数来准确识别DNA序列仍然存在一些障碍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，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