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.

项目概要 Bioconductor 是一个由 2,000 多个开源软件包组成的生态系统，用于可重复的 我们的母基金的目标 1“癌症基因组学”：对各种类型的基因组数据进行生物信息学分析。 “R/Bioconductor 中的集成和可扩展解决方案”(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 我们家长资助的目标 3 为以下项目建立 ExperimentHub 网络资源。 癌症数据资源的管理、分发、维护、可发现性和可用性 R/Bioconductor 社区。该提案针对数百个主要关注癌症的基因组和 针对基于 R/Bioconductor 的使用进行优化并包含显着附加值的宏基因组数据集 以协调和手动管理的形式超过主要来源，但对于其实质性领域和 目前需要将生物导体特定的专业知识转化为适合广泛使用的人工智能/机器学习的格式 首先，它通过翻译现有的组学软件创建 Bioconductor 机器学习存储库。 其次，R/Bioconductor 版本的 TCGA、cBioPortal、宏基因组学和其他数据集。 任何开发的模型的代表性和普遍性，它采用手动管理来统一注释 每个研究队列的关键特征，包括种族/民族、作为生物变量的性别、地理位置 最后，它提供了可运行的导入和使用的记录示例。 TensorFlow、PyTorch 和 scikit-learn 中的这些数据集总共将产生第一个大规模的、 独立于平台、支持 AI/ML 的数据存储库，可提供多样化且精心策划的组学数据。 对我们存储库中的研究和样本的少数地位进行注释将有助于识别 边缘化人群的偏见和健康差异。