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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos 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

项目摘要

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.

项目概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 Knowles 博士之前开发了 LeafCutter，一种灵活的、可扩展的、无需注释的工具，可根据短读长 RNA-seq 数据量化 RNA 剪接的局部模式。虽然 LeafCutter 已被广泛采用，并且我们一直积极维护它并且解决了 github 上的问题，它仍然是“早期”软件，我们建议软件。工程改进：1）使用标准构建的 conda 进行适当的包装，安装、测试和日志记录过程，2) 使用 Docker 进行容器化，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程这些改进将使我们能够通过存储库分发 LeafCutter。包括 PyPI、BioConda、DockStore 和 Galaxy 最后改进了文档，测试和版本管理将从开源中为LeafCutter做出贡献社区更有可能且更容易整合。拟议工作的家长奖是 U01 AG068880-01“学习监管代码 “阿尔茨海默病基因组”，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。在目标 1 中，我们正在构建前后的 DL 模型。转录调控：对于后者，LeafCutter 为我们的神经网络提供了训练数据 AD相关细胞类型中RNA剪接的序列决定因素的网络模型和在目标 2 中，我们将 AD 相关的结构变异与功能变异联系起来，包括。在目标 3 中，我们将构建跨数千个的反式表达 QTL 网络。尸检大脑样本：提出了对 LeafCutter 生态系统的改进在这里，我们将能够直接扩展到反式剪接QTL网络。我们的合作者本身就是 LeafCutter 的重度用户，我们将继续确保我们满足更广泛的基因组学社区的需求和用例。