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

用于准确、高效和可重复分析 RNA-Seq 数据的模块化框架

• 批准号: 10238765
• 负责人: Michael Isaiah Love
• 金额: $ 29.5万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-12 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10238765
• 关键词: Address; Adopted; Adoption; Algorithms; Alleles; Archives; Area; Attention; Biological; Biological Assay; Biomedical Research; Characteristics; Communities; Data; Data Set; Databases; Development; Disease; Event; Follow-Up Studies; Gene Expression Profiling; Generations; Genes; Genetic; Genome; Genomics; Goals; Health; Human; Hybrids; Infrastructure; Knowledge; Lead; Location; Measurement; Metadata; Methods; Modeling; Nucleotides; Organism; Phenotype; Process; Protein Isoforms; RNA; RNA Editing; RNA analysis; Reporting; Reproducibility; Reproducibility of Results; Research Personnel; Resources; Salmon; Sampling; Science; Sequence Alignment; Source; Speed; Statistical Data Interpretation; Testing; Time; Transcript; Uncertainty; Variant; Vision; Visualization; Visualization software; analysis pipeline; computational pipelines; cryptography; design; differential expression; experimental study; human error; improved; light weight; task analysis; tool; transcriptome; transcriptome sequencing; transcriptomics; wasting

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).

项目摘要 /摘要 我们建议开发改进的模块化管道，以进行更准确和可再现的RNA-Seq分析。 RNA- SEQ实验广泛用于生物学和生物医学科学，以确定所有基因的表达水平 和多个样品的同工型。必须预处理原始RNA-seq数据以确定RNA的丰度 分子。量化RNA丰度的最先进的工具是快速且有效的，型号的，并且可以纠正常见 技术偏见，并提供抽象不确定性的估计。可视化和 理想情况下，抽象的统计测试应纳入量化中的抽象估计值的不确定性 步骤，请考虑所有测序实验中观察固有的采样变异性，并估算 每个转录本，样品抽象的潜在生物学变化。而孤立的工具完成了子集 在上述特征中，我们建议开发一条解决所有这些的管道，同时 利用强大的现有基础设施进行基因表达分析。我们改善的模块化方法 当前的RNA-seq分析管道还将寻求利用用于基因集分析和的最佳下游工具 动态报告生成。当前的RNA-seq计算管道不会跟踪关键的元数据 在整个分析中，包括基因组和转录组版本，因此无法可靠地重复研究结果 由于可能会丢失有关注释出处的信息，因此在正确的基因组环境中引起或放置。快速 并且已迅速用于基因和笔录级量化的轻量级工具，尚未对其进行优化 对于某些RNA-seq分析任务，例如对等位基因表达的定量。我们已经开发了一组顶部 执行用于抽象定量和下游推理的工具。我们建议将现有工具形式化 进入管道，并建立其他工具和基础架构，从而最佳地估计并传播不确定性 根据抽象估计（AIM 1中描述），并自动存储关键的出处元数据 用户的代表 - 此元数据标记和传播将与社区资源集成（描述 在AIM 2）。此外，我们建议建立我们现有量化基础设施的能力以允许 为了提高映射准确性和更健壮，更准确的倾斜表达估计（在AIM 3中进行了描述）。