Learning the Regulatory Code of Alzheimer's Disease Genomes

学习阿尔茨海默病基因组的调控密码

• 批准号: 10406760
• 负责人: David Arthur Knowles
• 金额: $ 28.9万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10406760
• 关键词: Address; Adopted; Alternative Splicing; Alzheimer' s Disease; Alzheimer' s disease risk; Autopsy; Award; Awareness; Bipolar Disorder; Brain; Cell physiology; Chromosomes; Cloud Computing; Code; Collection; Communities; Complex; Computational algorithm; Computer software; Data; Data Analyses; Data Set; Dependence; Development; Disease; Docking; Documentation; Ecosystem; Ensure; Environment; Functional disorder; Galaxy; Generations; Genes; Genetic; Genetic Diseases; Genome; Genomics; Genotype-Tissue Expression Project; Heritability; Human Genetics; Image; Introns; Junk DNA; Language; Learning; Machine Learning; Mainstreaming; Maps; Modeling; Names; Nature; Neural Network Simulation; Output; Paper; Parents; Parkinson Disease; Pattern; Post-Transcriptional Regulation; Process; Proteins; Pythons; Quantitative Trait Loci; RNA Splicing; Readiness; Regulator Genes; Sampling; Schizophrenia; Single Nucleotide Polymorphism; Software Engineering; Standardization; Structure; System; Testing; Training; Trans-Splicing; Translating; Untranslated RNA; Untranslated Regions; Variant; Work; analysis pipeline; autism spectrum disorder; cell type; cohort; data format; deep learning; flexibility; functional genomics; genome wide association study; genome-wide; genomic data; indexing; insight; interoperability; novel; open source; personalized approach; repository; risk variant; software repository; tool; transcriptome; transcriptome sequencing; transcriptomics; user-friendly; virtual machine

Project Summary Alternative splicing is a key cellular process whose dysregulation has been implicated broadly across human genetic disease. Dr. Knowles previously developed LeafCutter, a flexible, scalable, annotation-free tool to quantify local patterns of RNA splicing from short-read RNA-seq data. While LeafCutter has been quite widely adopted and we have actively maintained it and addressed issues on github, it remains “early stage” software. We propose software engineering improvements: 1) appropriate packaging using conda with standard build, installation, testing and logging processes, 2) containerization using Docker, 3) standardization of input/output data formats/interfaces, and 4) refactoring to use a standard workflow language. These improvements will allow us to distribute LeafCutter through repositories including PyPI, BioConda, DockStore, and Galaxy. Finally improvements to documentation, testing and version management will make contributions to LeafCutter from the open source community more feasible and easier to integrate. The parent award for the proposed work is U01 AG068880-01 “Learning the Regulatory Code of Alzheimer's Disease Genomes”, where we are developing state-of-the-art deep learning (DL) and machine learning (ML) models to better understand the genetic basis of AD. This award makes extensive use of LeafCutter. In Aim 1 we are building DL models of pre- and post- transcriptional regulation: for the latter LeafCutter provides training data for our neural network model of the sequence determinants of RNA splicing in AD-relevant cell types and states. In Aim 2 we connect AD-associated structural variation to functional variation, including RNA splicing variation. In Aim 3, we will build trans-expression QTL networks across thousands of post-mortem brain samples: with the improvements to the LeafCutter ecosystem proposed here we will be able to straightforwardly extend to trans splicing QTL networks. While we and our collaborators are ourselves heavy users of LeafCutter, we will continue to ensure we provide for the needs and use-cases of the broader genomics community.

项目摘要 选择性剪接是一个关键的细胞过程，其失调已被广泛涉及 跨越人类遗传病。诺尔斯博士之前开发了切叶机，一种灵活的，可扩展的， 无注释工具，从短读rna-seq数据中量化rna剪接的局部模式。 虽然切叶机已经被广泛采用，我们也积极维护和 虽然GitHub上的问题已得到解决，但它仍处于“早期”软件阶段。我们建议使用软件 工程改进：1)使用标准版本的CONDA进行适当的封装， 安装、测试和记录流程，2)使用Docker进行集装箱化，3)标准化 输入/输出数据格式/接口，以及4)重构以使用标准工作流 语言。这些改进将使我们能够通过存储库分发切叶机 包括PyPI、BioConda、DockStore和Galaxy。最后是对文档的改进， 测试和版本管理将为LeadCutter的开源做出贡献 社区更可行，更容易整合。 拟开展工作的家长奖为U01 AG068880-01《学习监管规范》 阿尔茨海默病基因组“，在那里我们正在开发最先进的深度学习(DL) 和机器学习(ML)模型，以更好地了解AD的遗传基础。本奖项 广泛使用切叶机。在目标1中，我们正在构建前和后的动态链接库模型 转录调控：后者为我们的神经提供训练数据 AD相关细胞类型和细胞中RNA剪接序列决定因素的网络模型 各州。在目标2中，我们将AD相关的结构变异与功能变异联系起来，包括 RNA剪接变异。在目标3中，我们将建立覆盖数千人的反式表达QTL网络 对死后大脑样本的研究：建议改善切叶机生态系统 在这里，我们将能够直接扩展到反式剪接QTL网络。当我们和 我们的合作者本身就是切叶机的重度用户，我们将继续确保我们 提供更广泛的基因组学社区的需求和用例。