CAREER: Unifying short and long read RNA-seq analysis of alternative splicing using network flow models

职业：使用网络流模型统一选择性剪接的短读和长读 RNA-seq 分析

• 批准号: 2146398
• 负责人: David Knowles
• 金额: $ 50万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146398&HistoricalAwards=false
• 关键词: CAREER; Unifying; short; long; read

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Alternative splicing (AS), i.e. variation in the regions of A precursor messenger RNA removed, is a crucial cellular process in higher organisms. It has been found to be key for cellular responses to external stimuli from immune response in mammals to climate adaptation in plants, and it has been implicated in common diseases, from autoimmune (e.g. multiple sclerosis) to neurodegenerative (e.g., Alzheimer’s disease). Despite its importance, the tools to study AS have been limited by technological (short read) and conceptual problems. The former problem is now being addressed by the increasing availability and affordability of long read sequencing. However, the latter problem remains unaddressed. Long reads have been used to identify which alternatively spliced isoforms are present in cells, but not to statistically identify changes in AS or the specific splicing events involved. This research project is an attempt to fundamentally transform how AS is analyzed, into a unified framework making joint use of all available data (both long and short reads). Software tools will be developed under the FAIR (Findable, Accessible, Interoperable, Reusable) criteria including open source sharing of code and package distribution. An annual three-day codeathon based at New York Genome Center (NYGC) specifically targeting women and underrepresented minorities (recruited from Barnard College and Hunter College respectively) will be organized. This project proposes a new conceptual framework for AS analysis that 1) unifies local and isoform-level quantification, 2) accounts for uncertainty to enable powerful statistical testing, 3) enables exploratory data analysis of large-scale datasets, and 4) can jointly leverage short and/or long read RNA-seq data. This framework will use network flow algorithms to connect local splicing events with isoform usage rates, develop well-calibrated statistical tests, and convex dimensionality reduction techniques accounting for varying noise levels. These tools will be developed in the context of impactful real world applications (neurodevelopmental gene regulation, neurodegenerative disease progression, and spliceosomal mutant cancer) in close collaboration with colleagues at the NYGC, the non-profit research institute where the PI is co-affiliated. Results from the project will be posted at https://daklab.github.io/the_splice_must_flow/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。选择性剪接（AS），即前体信使RNA移除区域的变异，是高等生物中一个至关重要的细胞过程。研究发现，从哺乳动物的免疫反应到植物的气候适应，它是细胞对外部刺激反应的关键，并且它与常见疾病有关，从自身免疫性疾病（如多发性硬化症）到神经退行性疾病（如阿尔茨海默病）。尽管它很重要，但研究AS的工具受到技术（简短阅读）和概念问题的限制。前一个问题现在正通过长读测序的日益可用性和可负担性得到解决。但是，后一个问题仍然没有得到解决。长读取已被用于确定细胞中存在哪些选择性剪接同种异构体，但不能从统计学上确定AS的变化或所涉及的特定剪接事件。这个研究项目试图从根本上将AS的分析方式转变为一个统一的框架，使所有可用的数据（包括长读和短读）都能被联合使用。软件工具将在FAIR（可查找、可访问、可互操作、可重用）标准下开发，包括代码和包分发的开源共享。每年在纽约基因组中心（NYGC）举办为期三天的代码死亡大会，专门针对女性和代表性不足的少数族裔（分别从巴纳德学院和亨特学院招募）。该项目提出了一个新的AS分析概念框架，1)统一局部和同型水平量化，2)考虑不确定性以实现强大的统计测试，3)允许对大规模数据集进行探索性数据分析，4)可以共同利用短读和/或长读RNA-seq数据。该框架将使用网络流算法将具有相同使用率的局部拼接事件连接起来，开发校准良好的统计测试，以及考虑不同噪声水平的凸维降维技术。这些工具将在有影响力的现实世界应用（神经发育基因调控、神经退行性疾病进展和剪接体突变癌症）的背景下与NYGC的同事密切合作开发，NYGC是PI共同附属的非营利性研究机构。该项目的结果将发布在https://daklab.github.io/the_splice_must_flow/.This上，该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。