A Modular Framework for Accurate, Efficient, and Reproducible Analysis of RNA-Seq Data

项目摘要/摘要 我们建议开发改进的、模块化的管道，以实现更准确和可重复性的RNA-SEQ分析。RNA- SEQ实验在生物和生物医学科学中被广泛用于确定所有基因的表达水平 以及跨多个样本的异构体。原始的rna-seq数据必须经过预处理才能确定rna的丰度。 分子。最先进的定量核糖核酸丰度的工具是快速和有效的，fi，模型和常见的正确 技术偏差，并提供对丰度不确定性的估计。可视化的下游工具和 丰度的统计检验理想情况下应该包含量子fi阳离子丰度估计的不确定度 步骤，考虑到所有测序实验中观测中固有的抽样变异性，并估计 每一份转录本都是样本间丰度的潜在生物差异。虽然孤立的工具完整的fi只是一个子集 针对上述特点，我们建议开发一条解决所有这些问题的管道，同时 利用强大的现有基础设施进行基因表达分析。我们采用模块化方法来改进 目前的rna-seq分析管道也将寻求利用最好的下游工具进行基因集分析和 动态报告生成。当前的rna-seq计算管道不跟踪关键的元数据片段。 在整个分析过程中，包括基因组和转录组版本，使得fiNAL结果不能可靠地再现- 由于关于注解来源的信息可能丢失，因此被归入或放入正确的基因组上下文中。虽然很快 轻量级工具已经迅速被用于基因和转录水平的fi定量检测，它们还没有得到优化 用于某些rna-seq分析任务，如等位基因fi阳离子等位基因fic表达。我们已经开发了一套顶级 进行fi丰度定量和下游推断的工具。我们建议将我们现有的工具正规化 整合到管道中，并构建其他工具和基础设施，从而以最佳方式估计和传播不确定性 来自丰度估计(在目标1中描述)，并将关键种源元数据自动存储在 代表用户-此元数据标记和传播将与社区资源集成(描述 目标2)。此外，我们建议扩展我们现有的Quantifi阳离子基础设施的能力，以允许 为了提高作图精度和更稳健、更准确的等位基因表达估计(在目标3中描述)。

PROJECT SUMMARY / ABSTRACT We propose to develop improved, modular pipelines for more accurate and reproducible RNA-seq analyses. RNA- seq experiments are widely used in biological and biomedical sciences to determine the expression level of all genes and isoforms across multiple samples. Raw RNA-seq data must be pre-processed to determine abundances of RNA molecules. State-of-the-art tools for quantifying RNA abundances are fast and efficient, model and correct for common technical biases, and provide estimates of the uncertainty of the abundances. Downstream tools for visualization and statistical testing of abundance ideally should incorporate uncertainty of abundance estimates from the quantification step, take into account the sampling variability inherent in observations in all sequencing experiments, and estimate, for each transcript, the underlying biological variation in abundances across samples. While isolated tools fulfill a subset of the above characteristics, we propose to develop a pipeline which addresses all of these, while at the same time leveraging the powerful existing infrastructure for gene expression analysis. Our modular approach to improving the current RNA-seq analysis pipelines will also seek to make use of the best downstream tools for gene set analysis and dynamic report generation. Current RNA-seq computational pipelines do not keep track of critical pieces of metadata throughout the analysis, including genome and transcriptome version, such that final results cannot reliably be repro- duced or put in the correct genomic context as the information about annotation provenance may be lost. While fast and lightweight tools have been quickly adopted for gene- and transcript-level quantification, they are not yet optimized for certain RNA-seq analysis tasks such as quantification of allele specific expression. We have developed a set of top performing tools for abundance quantification and downstream inference. We propose to formalize our existing tools into a pipeline, and build additional tools and infrastructure, which optimally estimates and propagates uncertainty from abundance estimation (described in Aim 1), and which stores critical provenance metadata automatically on the user's behalf — this metadata tagging and propagation will be integrated with community resources (described in Aim 2). Furthermore, we propose building out the capabilities of our existing quantification infrastructure to allow for improved mapping accuracy and more robust and accurate allelic expression estimation (described in Aim 3).