Collaborative Research: Proteomics Biomarker Information System (ProBIS)

合作研究：蛋白质组学生物标志物信息系统（ProBIS）

• 批准号: 0612152
• 负责人: Jean Gao
• 金额: --
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0612152&HistoricalAwards=false
• 关键词: Collaborative; Research; Proteomics; Biomarker; Information

Biomarkers measured in a minimally invasive and repeatable way can expedite the early diagnosis of disease, the indication of disease prognosis, and the discovery of new drug targets for therapy. Serum proteomic profiling for biomarker detection may reflect the abnormality or pathologic state of various diseases through the protein/peptide peaks that are expressed differently between diseased and healthy individuals. Since the procedures are simple, inexpensive, and minimally invasive, serum proteomic methods readily lend themselves to screening-test development; their robustness and ease promise to translate into routine clinical practice. The objective of this project is to construct a high-throughput proteomic profiling analysis toolset, Proteomics Biomarker Information System (ProBIS), which can provide early biomarker detection and identification for clinical proteomics healthcare. Our main contributions are: (1) High Resolution Proteomic Profiling: different from traditional low resolution mass spectrometry analysis, we will use high resolution profile to analyze the low-molecular weight (LMW) end of the proteomic spectrum for more precise analysis; and (2) Integrated System for Biomarker Discovery and Identification: identification and sequencing of the underlying discriminatory proteins/peptides will reveal the insights of biomarkers and characterize disease pathway. The results of this project will have an impact on the computer science community and an equal, if not greater, impact on the broad medical community that is in need of such a proteomic profiling data analysis tool. The experiments will be driven by a series of case studies, including ovarian cancer early relapse monitoring and prostate cancer diagnosis. The solutions for these case studies will have a direct impact on individual areas. Our source code and data will be available for general dissemination over the internet, and discoveries will be integrated into the classroom.

以微创和可重复的方式测量生物标志物可以加快疾病的早期诊断、疾病预后的指示和发现新的治疗药物靶点。血清蛋白质组学检测生物标记物可以通过疾病个体和健康个体表达不同的蛋白质/多肽峰来反映各种疾病的异常或病理状态。由于该方法简单、廉价且微创，血清蛋白质组学方法很容易用于筛查试验的开发；它们的健壮性和易用性有望转化为常规的临床实践。本项目的目标是构建一个高通量的蛋白质组谱分析工具集--蛋白质组学生物标志物信息系统(ProBIS)，为临床蛋白质组学保健提供早期生物标志物检测和鉴定。我们的主要贡献是：(1)高分辨率蛋白质组图谱：与传统的低分辨率质谱学分析不同，我们将使用高分辨率蛋白质组谱分析来分析蛋白质组谱的低相对分子质量(LMW)端，以实现更精确的分析；以及(2)生物标志物发现和鉴定集成系统：对潜在的识别蛋白质/肽进行鉴定和测序，将揭示生物标志物的洞察力，并描述疾病途径。该项目的结果将对计算机科学界产生影响，并对需要这种蛋白质组图谱数据分析工具的广大医学界产生同等或更大的影响。这些实验将由一系列案例研究推动，包括卵巢癌早期复发监测和前列腺癌诊断。这些案例研究的解决方案将对个别领域产生直接影响。我们的源代码和数据将在互联网上广泛传播，并将发现整合到课堂上。