Cancer Genomics: Integrative and Scalable Solutions in R/Bioconductor

癌症基因组学：R/Bioconductor 中的集成且可扩展的解决方案

• 批准号: 10594231
• 负责人: Martin T Morgan
• 金额: $ 31.86万
• 依托单位: GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10594231
• 关键词: Administrative Supplement; Bioconductor; Bioinformatics; Biological; Cells; Characteristics; Code; Cohort Studies; Collaborations; Communities; Computer software; Data; Data Set; Deposition; Documentation; Ecosystem; Epigenetic Process; Ethnic Origin; Gene Expression; Genomics; Geographic Locations; Geography; Individual; Learning; Machine Learning; Maintenance; Malignant Neoplasms; Manuals; Metadata; Metagenomics; Methods; Minority; Minority Groups; Modeling; Modification; Mutation; Participant; Process; Race; Readiness; Records; Reproducibility; Research; Research Personnel; Resources; Review Literature; Running; Sampling; Sequence Read Archive; Source; Standardization; Structure; TensorFlow; Text; The Cancer Genome Atlas; Training; Translating; Underrepresented Populations; United States National Institutes of Health; base; cBioPortal; cancer genomics; cohort; data mining; data modeling; data repository; data resource; design; genomic data; health disparity; improved; interest; large scale data; literate; machine learning algorithm; marginalized population; multimodality; online resource; open source; parent grant; platform-independent; recruit; repository; sex; software development; usability

Project Summary Bioconductor is an ecosystem of more than 2,000 open-source software packages for the reproducible bioinformatics analysis of various types of genomic data. Aim 1 of our parent grant, “Cancer Genomics: Integrative and Scalable Solutions in R/Bioconductor” (7U24CA180996), develops and maintains R/Bioconductor data structures for representation, downstream software development, and analysis of multimodal cancer datasets. Aim 3 of our parent grant establishes ExperimentHub web resources for the curation, distribution, maintenance, discoverability, and usability of cancer data resources for the R/Bioconductor community. This proposal targets hundreds of primarily cancer-focused genomic and metagenomic datasets that are optimized for R/Bioconductor-based usage and contain significant value-added over primary sources in the form of harmonization and manual curation, but for which substantial domain and Bioconductor-specific expertise is currently required to translate into formats suitable for widely used AI/ML softwares. First, it creates the Bioconductor Machine Learning Repository for Omics by translating existing R/Bioconductor versions of TCGA, cBioPortal, metagenomics, and other datasets. Second, in order to assess representation and generalizability of any models developed, it employs manual curation to uniformly annotate key characteristics of each study cohort including race/ethnicity, sex as a biological variable, geographical location, and recruitment period. Finally, it provides runnable documented examples of the import and use of these datasets in TensorFlow, PyTorch, and scikit-learn. In total, this proposal will produce the first large-scale, platform-independent, AI/ML-ready data repository for diverse and highly curated omics data. Thorough annotation on minority status of the studies and samples in our repository will facilitate the identification of biases and health disparities for marginalized populations.

项目摘要 生物导体是一个超过2,000个开源软件包的生态系统，可重现 各种类型基因组数据的生物信息学分析。我们的父母授予的目标1：“癌症基因组学： R/Bioconductor中的集成和可扩展的解决方案”（7U24CA180996），开发和维护 用于表示，下游软件开发和分析的R/Bioconductor数据结构 多模式癌数据集。我们父母赠款的目标3建立了实验性网络资源 癌症数据资源的策划，分配，维护，可发现性和可用性 R/Bioconductor社区。该建议针对数百种以癌症为中心的基因组和 用于基于R/bioconductor的用法优化并包含大量增值的宏基因组数据集 以统一和手动策划形式的主要来源，但为此而实现的领域和 目前需要特定于生物导体的专业知识才能转化为适用于广泛使用的AI/ML的格式 软件。首先，它通过翻译现有 TCGA，CBIOPORTAL，MENAGENOMICS和其他数据集的R/Bioconductor版本。第二，为了评估 任何开发的模型的表示和普遍性，它都采用手动策划均匀注释 每个研究队列的关键特征，包括种族/种族，性别作为生物变量，地理 位置和招聘期。最后，它提供了可运行的记录的导入和使用的示例 这些数据集中有Tensorflow，Pytorch和Scikit-Learn。总的来说，该提案将产生第一个大规模的 无独立的AI/ML准备数据存储库，用于多种多样，高度策划的OMICS数据。彻底 对我们存储库中研究和样本的少数群体状况的注释将有助于识别 边缘化人群的偏见和健康差异。