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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。选择性剪接(AS)，即去除前体信使RNA区域的变异，是高等生物体中一个关键的细胞过程。已发现它是细胞对外界刺激反应的关键，从哺乳动物的免疫反应到植物的气候适应，它与从自身免疫(如多发性硬化症)到神经退行性疾病(如阿尔茨海默病)的常见疾病有关。尽管它很重要，但研究AS的工具一直受到技术(简称)和概念问题的限制。前一个问题现在正通过越来越多的长阅读测序的可用性和可负担性得到解决。然而，后一个问题仍然没有得到解决。长读数已经被用来识别细胞中存在哪些选择性剪接异构体，但不能从统计上识别AS的变化或涉及的特定剪接事件。这一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，联合使用所有可用的数据(包括长读和短读)。软件工具将在公平(可发现、可访问、可互操作、可重复使用)标准下开发，包括开放源代码共享代码和包分发。将在纽约基因组中心(NYGC)组织一年一度的为期三天的Codeathon，专门针对妇女和代表不足的少数群体(分别从Barnard学院和Hunter学院招募)。该项目为AS分析提出了一个新的概念框架，该框架1)统一局部和同形水平的量化，2)考虑不确定性以实现强大的统计测试，3)能够对大规模数据集进行探索性数据分析，以及4)可以联合利用短和/或长读取的RNA-SEQ数据。该框架将使用网络流量算法将局部剪接事件与异构体使用率联系起来，开发校准良好的统计测试，以及考虑到不同噪声水平的凸维度降维技术。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。该项目的结果将张贴在https://daklab.github.io/the_splice_must_flow/.This奖上，这反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.