Interactive Informatics Resource for Research-driven Cancer Proteomics

研究驱动型癌症蛋白质组学的交互式信息学资源

基本信息

批准号：
8847691
负责人：
BOBBIE-JO Mary WEBB-ROBERTSON
金额：
$ 42.17万
依托单位：
BATTELLE PACIFIC NORTHWEST LABORATORIES
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-09 至 2017-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8847691
关键词：
Accounting Address Advanced Development Algorithms Area Biological Biological Markers Biological Process Biology Breast Cancer Etiology Clinical Code Collection Communities Complex Computer software DNA Data Data Analyses Data Quality Data Reporting Data Security Data Set Databases Development Diagnosis Disease Ensure Environment Experimental Designs Funding Genes Genomics Genotype Goals Grant Health Imagery Individual Informatics Institutes Institution Investments Java Knowledge Label Letters Link Machine Learning Malignant Neoplasms Mass Spectrum Analysis Methodology Methods Mining Modeling Molecular National Institute of Allergy and Infectious Disease National Institute of Diabetes and Digestive and Kidney Diseases Ovarian Pathway interactions Patient Care Pattern Peptide Mapping Peptides Phenotype Post-Translational Protein Processing Process Prognostic Marker Programming Languages Protein Fragment Proteins Proteome Proteomics RNA Splicing Research Research Personnel Resource Informatics Resources Sampling Scientist Source Code Specificity Staging Statistical Algorithm Statistical Data Interpretation Statistical Methods Statistical Models Sum Technology Training Translating United States National Institutes of Health Validation Variant Vision Visual base built environment cancer cell cancer proteomics candidate validation computerized data processing computerized tools data integration design experience graphical user interface improved instrument link protein mathematical methods model design novel novel diagnostics prognostic programs protein expression prototype research study software development statistics tool user friendly software

项目摘要

DESCRIPTION (provided by applicant): In 2013 over 1.6 million new cases of cancer are expected to be diagnosed and over 580,000 people are expected to die of the disease. Thus, continued research in the identification of new diagnostic and prognostic biomarkers of cancer is necessary. Although cancer is widely recognized as a genomic disease, the directives of the DNA-based drivers are executed at the level of proteins and their biological functions, and the application of potential protein level biomarkers remains a compelling vision. Thus, a large investment has been made by NCI and other research centers in high-throughput global proteomics experiments to mine for novel biomarkers of cancer. However, few of these markers have come to fruition. We believe that one of the major challenges to the discovery of robust protein- or pathway-biomarker candidates from these large and complex proteomics datasets is due to naive data analysis approaches that do not take into account the underlying complexity of the proteome (e.g., splice variants, post- translational modifications). State-of-the-art statistical algorithms to improve the tasks of quality assessment, peptide and protein quantification, and pathway modeling that are designed to account for the design of the experiment have been developed; however access to these methodologies by the larger community is hindered since they are in the prototype stage and typically require knowledge of statistical programming. Furthermore, the likelihood of these tools moving to robust software is low since they are developed within the context of existing grants that do not support the transition from prototype to software. For the field of clinical proteomics to successfully identif new mechanistic etiologies of cancer requires not only high quality data with respect to the instrument, but also high quality statistical analysis of the data. This project proposes new informatics technology in the form of a robust, interactive and cross- platform software environment that will enable biomedical and biological scientists to perform in-depth analyses of global proteomics data from the point of quality assessment and normalization of raw inferred abundances (e.g., peak area) to the identification of protein biomarkers and enriched pathways. The software will be designed in a single programming language (Java) to assure easy installation across platforms with wizard-based data entry and advanced data reporting. Java will also support the development of advanced graphical user interfaces for data presentation and interactive graphics with a modern look and feel. This approach will ensure that scientists outside of the development institution can develop modules to include in the software or extensions for data integration without challenges of re-compiling the application. The software modules to be developed under this project are Aim 1) peptide and protein level quality assessment and quantification, Aim 2) protein biomarker discovery via exploratory data analysis and machine learning, and Aim 3) pathway biomarker discovery through integration with the NCI Protein Interaction Database.

描述（由申请人提供）： 2013 年预计将诊断出超过 160 万新癌症病例，预计将有超过 580,000 人死于该疾病。因此，有必要继续研究鉴定新的癌症诊断和预后生物标志物。尽管癌症被广泛认为是一种基因组疾病，但基于 DNA 的驱动因素的指令是在蛋白质及其生物功能水平上执行的，并且潜在的蛋白质水平生物标志物的应用仍然是一个令人信服的愿景。因此，NCI 和其他研究中心在高通量全球蛋白质组学实验上投入了大量资金，以挖掘新型癌症生物标志物。然而，这些标记很少取得成果。我们认为，从这些大型且复杂的蛋白质组数据集中发现稳健的蛋白质或途径生物标志物候选物的主要挑战之一是由于简单的数据分析方法没有考虑蛋白质组的潜在复杂性（例如，剪接）变体、翻译后修饰）。已经开发出最先进的统计算法，用于改进质量评估、肽和蛋白质定量以及旨在解释实验设计的途径建模的任务；然而，更大的社区对这些方法的获取受到阻碍，因为它们处于原型阶段并且通常需要统计编程知识。此外，这些工具转向强大软件的可能性很低，因为它们是在现有拨款的背景下开发的，而现有拨款不支持从原型到软件的过渡。临床蛋白质组学领域要成功识别癌症的新机制，不仅需要仪器方面的高质量数据，还需要对数据进行高质量的统计分析。该项目以强大的、交互式的、跨平台的软件环境的形式提出了新的信息学技术，使生物医学和生物科学家能够从质量评估和原始推断丰度标准化的角度对全球蛋白质组数据进行深入分析。例如，峰面积）来鉴定蛋白质生物标志物和富集途径。该软件将采用单一编程语言（Java）设计，以确保通过基于向导的数据输入和高级数据报告轻松跨平台安装。 Java 还将支持开发具有现代外观和感觉的数据呈现和交互式图形的高级图形用户界面。这种方法将确保开发机构之外的科学家可以开发模块以包含在软件或数据集成扩展中，而无需重新编译应用程序。该项目将开发的软件模块包括：目标 1) 肽和蛋白质水平质量评估和定量；目标 2) 通过探索性数据分析和机器学习发现蛋白质生物标志物；目标 3) 通过与 NCI 蛋白质相互作用集成发现通路生物标志物数据库。