A Genome Data Analysis Center Focused on Batch Effect Analysis and Data Integration

专注于批量效应分析和数据整合的基因组数据分析中心

• 批准号: 10689115
• 负责人: Rehan Akbani
• 金额: $ 31.77万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10689115
• 关键词: Algorithms; Atlases; Back; Bioinformatics; Biological; Biology; Biometry; Cancer Biology; Cancer Center; Cells; Clinical; Cluster Analysis; Collaborations; Communication; Communities; Competence; Complex; Computational Biology; Computer software; Computing Methodologies; Consult; Consultations; Data; Data Analyses; Data Set; Dedications; Detection; Development; Diagnosis; Disease; Doctor of Philosophy; Experimental Designs; FAIR principles; Faculty; Future; Generations; Genome Data Analysis Center; Goals; Institution; Instruction; Laboratory Research; Leadership; Malignant Neoplasms; Medicine; Methodology; Mission; Modeling; Molecular Profiling; Morphologic artifacts; Pathway interactions; Phase; Process; Protein Array; Protein Array Analysis; Proteomics; Quality Control; Reproducibility; Research; Research Personnel; Research Project Grants; Sampling; Schedule; Software Engineering; System; The Cancer Genome Atlas; Time; Translating; Visualization; Work; bioinformatics tool; cancer genomics; computerized tools; computing resources; data integration; experience; innovation; member; molecular scale; multidisciplinary; multiple omics; prevent; programs; resistance mechanism; segregation; single cell sequencing; software systems; surveillance data; therapy resistant; tool; trend; tumor; working group

* * * * PROJECT SUMMARY * * * * Abstract: Technical batch effects pose a fundamental challenge to quality control and reproducibility of even single-laboratory research projects, but the possibilities for serious error are greatly magnified in complex, multi- institutional enterprises such as the cancer molecular profiling projects being undertaken by the NCI Center for Cancer Genomics (CCG). To aid in detection, quantitation, interpretation, and (when appropriate) correction for technical batch effects in such data, we have developed the MBatch software system. MBatch proved indispensable for quality-control “surveillance” of data in The Cancer Genome Atlas (TCGA) and ongoing CCG projects. But detecting and quantitating batch effects (or trend effects or statistical outliers) are just the first steps in a process. The next steps involve detective work in collaboration with those who generated the data, drawing upon expertise in integrative analysis across data types, pathways, and systems-level biology. That detective work usually succeeds in diagnosing the cause of a batch effect as technical or biological. If technical, then computational methods to ameliorate the batch effect can be applied (judiciously). The primary aim of the proposed Genome Data Analysis Center (GDAC) is to continue to translate that successful quality-control model to the CCG’s other current and future large-scale molecular profiling projects We will be ready to do that on Day 1. We will continue to enhance and extend the power of MBatch and incorporate a number of innovative new algorithms, tools, and interactive visualizations into it (OmicPioneer-sc, MutBatch, CarDEC, and CorNet). Evaluating and correcting batch effects is a complex process, so we will collaborate with other GDACs and data generating centers to determine the influence of artifacts on any analysis results they produce. The second aim is to contribute and enhance additional competencies. We are prepared to (i) provide integrated cluster solutions to segregate cases into biologically relevant groups; (ii) provide tools and expertise for high-level visualization of omic data (including single-cell data); and (iii) analyze RPPA proteomic data from the subset of projects that generate such data. Our final aim is to communicate results and distribute corrected data back to other network members, project stakeholders, and the scientific community. We bring a number of assets to the table, including multidisciplinary expertise in bioinformatics, biostatistics, software engineering, cancer biology and cancer medicine; PIs with a combined 40+ years of experience in molecular profiling of cancers; expertise gained in 10 years of doing the batch effects surveillance for TCGA and other CCG projects; a highly professional software engineering team with a track record of producing high-end bioinformatics tools; extensive computing resources, including one of the most powerful academic clusters in the world; and close working relationships with first-class basic, translational, and clinical researchers across MD Anderson, one of the foremost cancer centers in the U.S. The bottom-line mission of the GDAC will be to aid the research community’s effort to understand cancer and to prevent, detect, diagnose, and treat it more effectively.

* * * * 项目摘要 * * * * 摘要：技术批次效应对质量控制和重复性提出了根本性挑战。 单一实验室的研究项目，但在复杂的、多实验室的研究项目中，出现严重错误的可能性被大大放大了。 机构企业，例如 NCI 中心正在开展的癌症分子谱分析项目 癌症基因组学 (CCG) 帮助检测、定量、解释和（在适当时）校正。 在此类数据技术批次效果方面，我们开发了MBatch软件系统进行了验证。 对于癌症基因组图谱 (TCGA) 和正在进行的 CCG 中数据的质量控制“监视”不可或缺 但检测和量化批次效应（或趋势效应或统计异常值）只是第一步。 接下来的步骤涉及与生成数据、绘图的人员合作进行侦探工作。 凭借跨数据类型、途径和系统级生物学的综合分析专业知识。 工作通常能成功地将批次效应的原因诊断为技术性或生物性。 可以（明智地）应用计算方法来改善批次效应。 拟议的基因组数据分析中心 (GDAC) 的主要目标是继续转化 CCG 当前和未来的其他大规模分子分析项目的成功质量控制模型 我们将在第一天就做好准备。我们将继续增强和扩展 MBatch 和 将许多创新的新算法、工具和交互式可视化融入其中（OmicPioneer-sc、 MutBatch、CarDEC 和 CorNet）评估和校正批次效果是一个复杂的过程，因此我们将 与其他 GDAC 和数据生成中心合作，确定工件对任何分析的影响 他们产生的第二个目标是贡献和增强额外的能力。 (i) 提供综合集群解决方案，将病例分为生物学相关组；(ii) 提供工具； 组学数据（包括单细胞数据）高级可视化的专业知识；以及 (iii) 分析 RPPA 我们的最终目标是传达结果和来自生成此类数据的项目子集的蛋白质组数据。 将更正后的数据分发回其他网络成员、项目利益相关者和科学界。 我们带来了许多资产，包括生物信息学、生物统计学、 软件工程、癌症生物学和癌症医学领域的 PI 拥有 40 多年的经验 癌症的分子分析；在 10 年的 TCGA 批次效应监测中获得了专业知识； 其他 CCG 项目；高度专业的软件工程团队，拥有制作高端产品的记录 生物信息学工具；广泛的计算资源，包括最强大的学术集群之一 世界各地；并与医学博士一流的基础、转化和临床研究人员建立密切的工作关系 安德森癌症中心是美国最重要的癌症中心之一。GDAC 的基本使命是帮助 研究界致力于了解癌症并更有效地预防、检测、诊断和治疗癌症。