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

• 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
• 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

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160万例新的癌症病例，预计将有超过580，000人死于该疾病。因此，有必要继续研究新的癌症诊断和预后生物标志物的鉴定。虽然癌症被广泛认为是一种基因组疾病，但基于DNA的驱动程序的指令是在蛋白质及其生物功能水平上执行的，潜在的蛋白质水平生物标志物的应用仍然是一个引人注目的愿景。因此，NCI和其他研究中心在高通量全球蛋白质组学实验中进行了大量投资，以挖掘癌症的新生物标志物。然而，这些标志很少取得成果。我们认为，从这些大型和复杂的蛋白质组学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不考虑蛋白质组的潜在复杂性（例如，剪接变体、翻译后修饰）。国家的最先进的统计算法，以改善质量评估，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已经开发;然而，访问这些方法的更大的社区受到阻碍，因为他们是在原型阶段，通常需要统计编程的知识。此外，这些工具转变为强大软件的可能性很低，因为它们是在现有赠款的范围内开发的，而现有赠款不支持从原型到软件的过渡。对于临床蛋白质组学领域来说，要成功地鉴定癌症的新机制病因，不仅需要关于仪器的高质量数据，而且需要对数据进行高质量的统计分析。该项目提出了一种新的信息学技术，其形式是一种强大的、交互式的和跨平台的软件环境，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的标准化的角度对全球蛋白质组学数据进行深入分析（例如，峰面积）来鉴定蛋白质生物标志物和富集的途径。该软件将采用单一编程语言（Java）设计，以确保跨平台轻松安装，并提供基于向导的数据输入和高级数据报告。Java还将支持开发先进的图形用户界面，用于数据显示和具有现代外观和感觉的交互式图形。这种方法将确保开发机构以外的科学家可以开发模块，以纳入软件或扩展数据集成，而无需重新编译应用程序。在该项目下开发的软件模块是Aim 1）肽和蛋白质水平质量评估和定量，Aim 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，以及Aim 3）通过与NCI蛋白质相互作用数据库集成发现途径生物标志物。