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

基本信息

批准号：
10703230
负责人：
Martin T Morgan
金额：
$ 70.34万
依托单位：
GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
依托单位国家：
美国
项目类别：
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/10703230
关键词：
Address Adoption Architecture Archives Area Bioconductor Biological Assay Cancer Biology Cells Cellular Assay Communities Community Participation Complex Comprehension Computer software DNA Sequence Data Data Set Development Documentation Ecosystem Environment Epigenetic Process Event Exposure to FAIR principles Funding Gene Expression Genes Genome Genomic DNA Genomics Goals Growth Individual Infrastructure Institution Link Maintenance Malignant Neoplasms Memory Messenger RNA Methods Methylation Molecular Molecular Biology Point Mutation Precision therapeutics Programming Languages Proteins Protocols documentation Recurrence Regulatory Element Reporting Reproducibility Research Research Personnel Resources Role Specimen Standardization Statistical Data Interpretation System Technology The Cancer Genome Atlas Training Untranslated RNA Variant Visualization anticancer research cancer cell cancer genomics clinically relevant cloud based computer infrastructure cost data integration data resource differential expression experimental study falls genetic information genome resource genomic data improved interoperability learning materials multiple omics navigation aid neoplastic cell novel novel strategies open source operation outreach programs residence single cell analysis software development software infrastructure training opportunity treatment response tumor heterogeneity tumor microenvironment tumor-immune system interactions usability

项目摘要

Abstract Bioconductor is an ecosystem of more than 1,500 open-source software and data packages for the statistical analysis and comprehension of high-throughput genomic data. It is widely used by the cancer genomics research community for statistical analysis and visualization. This software ecosystem is supported by core data classes and methods, reused by both users and developers, that provide convenient representations and efficient operations for many kinds of high-throughput molecular data. Falling sequencing costs and single-cell assays enable increasingly resolved study of the molecular biology of cancer, through combined assaying of DNA sequence, epigenetics, gene expression, protein, and other aspects, even at the single-cell level, for a single specimen. These developments present new challenges in complexity, size, and interpretability of the data. The overarching goal of this project is to create and adapt core Bioconductor software infrastructure to meet these challenges, through the following aims. First, we develop infrastructure for the analysis of single-cell multi-omic experiments. Second, we implement FAIR principles for improved somatic variant prioritization, by defining performant data architecture that harmonizes and integrates the large amount of experimental and annotation data available through Bioconductor. Users of our system will be able to create provenance-rich interoperable reports on structural and functional contexts of somatic variants for use in prioritization. Third, we develop scalable infrastructure for the curation, distribution, maintenance, discoverability, and usability of cancer data resources within and externally to Bioconductor. Finally, we develop a program of user training and new outreach approaches to support adoption of advanced Bioconductor infrastructure by developers of new cancer-related packages and existing packages critical to the cancer research community.

抽象的 Bioconductor 是一个由 1,500 多个开源软件和数据包组成的生态系统，用于统计高通量基因组数据的分析和理解。它被癌症基因组学广泛使用统计分析和可视化研究社区。该软件生态系统由核心支持数据类和方法，由用户和开发人员重用，提供方便的表示和对多种高通量分子数据进行高效操作。测序成本下降和单细胞通过联合检测，癌症分子生物学的研究日益深入 DNA 序列、表观遗传学、基因表达、蛋白质和其他方面，甚至在单细胞水平上，单个标本。这些发展在复杂性、规模和可解释性方面提出了新的挑战。数据。该项目的总体目标是创建和调整核心 Bioconductor 软件基础设施通过以下目标应对这些挑战。首先，我们开发用于分析的基础设施单细胞多组学实验。其次，我们实施公平原则来改进体细胞变异通过定义协调和集成大量数据的高性能数据架构来确定优先级可通过 Bioconductor 获得实验和注释数据。我们系统的用户将能够创建关于体细胞变异的结构和功能背景的来源丰富的可互操作报告，用于优先顺序。第三，我们开发可扩展的基础设施，用于管理、分发、维护、 Bioconductor 内部和外部的癌症数据资源的可发现性和可用性。最后，我们制定用户培训计划和新的推广方法，以支持先进技术的采用新的癌症相关包和现有包的开发人员开发的生物导体基础设施对于癌症至关重要癌症研究团体。

项目成果

期刊论文数量（45）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

BiocPkgTools: Toolkit for mining the Bioconductor package ecosystem.

BiocPkgTools：用于挖掘 Bioconductor 包生态系统的工具包。

DOI：
发表时间：
2019
期刊：
影响因子：
0
作者：
Su, Shian;Carey, Vincent J;Shepherd, Lori;Ritchie, Matthew;Morgan, Martin T;Davis, Sean
通讯作者：
Davis, Sean

Multiomic Integration of Public Oncology Databases in Bioconductor.

Bioconductor 中公共肿瘤学数据库的多组学整合。

DOI：
发表时间：
2020
期刊：
影响因子：
4.2
作者：
Ramos, Marcel;Geistlinger, Ludwig;Oh, Sehyun;Schiffer, Lucas;Azhar, Rimsha;Kodali, Hanish;de Bruijn, Ino;Gao, Jianjiong;Carey, Vincent J;Morgan, Martin;Waldron, Levi
通讯作者：
Waldron, Levi

Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi.

Illumina HumanMmethylationEPIC 阵列与 minfi 的预处理、标准化和集成。