Parameter-free Peak Detection Algorithm for Reducing False Positive/Negative Compound Identification from Raw Mass Spectrometry Metabolomics Data.

无参数峰检测算法，用于减少原始质谱代谢组学数据中的假阳性/阴性化合物鉴定。

• 批准号: 9433358
• 负责人: Xiuxia Du
• 金额: $ 13.67万
• 依托单位: UNIVERSITY OF NORTH CAROLINA CHARLOTTE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-19 至 2019-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9433358
• 关键词: Address; Algorithm Design; Algorithms; Biological; Bypass; Chromatography; Collaborations; Communities; Computer software; Coupled; Data; Data Files; Detection; Development; Dimensions; Disease; Error Sources; Evaluation; Gas Chromatography; Imagery; Ions; Java; Laboratories; Lead; Liquid substance; Manuals; Mass Spectrum Analysis; Measurement; Measures; Metabolism; Morphologic artifacts; North Carolina; Process; Pythons; Reference Standards; Research; Research Personnel; Resolution; Sampling; Scientist; Serum; Specific qualifier value; Specificity; Therapeutic; Time; Translating; Universities; Vendor; Water; Work; base; biomarker discovery; clinical biomarkers; computerized data processing; cost; design; experimental study; flexibility; improved; metabolomics; metabolomics resource; novel; open source; prototype

Project Summary / Abstract Parameter-free Peak Detection Algorithm for Reducing False Positive/Negative Compound Identification from Raw Mass Spectrometry Metabolomics Data Mass spectrometry (MS) coupled to liquid or gas chromatography (LC or GC) have become indispensable analyt- ical platforms for untargeted metabolomics. With sensitivity, chromatographic resolution, and mass measurement accuracy continuously improving, more and more analytes are now detectable, and this has enormous potential to lead to great strides in our understanding of metabolism. The first step in the preprocessing of raw LC- and GC-MS metabolomics data is the detection and extraction of peaks that represent ions. However, existing peak detection algorithms invariably yield an immense number of false positive and false negative peaks. These incorrect peaks can translate downstream into spurious or missing compound identifications. Furthermore, a large number of parameters must be specified for these algorithms to work. Unfortunately, general users often do not understand how to optimize these parameters, and maximizing one aspect (e.g., sensitivity) often has deleterious effects on another (e.g., specificity). To address the challenges, we propose a paradigm shift in the detection of peaks by simultaneously considering the three dimension of an ion’s information. This will significantly increase the accuracy of peak detection compared to what can be achieved by existing algorithms that carry out peak detection by processing data in three separate 2D planes. The results of our proposed research will benefit metabolomics research in multiple ways. (1) The more accurate algorithm will eliminate or reduce manual checking of results to a minimum. (2) With the parameter-free design, researchers will not have to go through many rounds of trial-and- error to determine the best compromise for a set of processing parameters. (3) The more accurate algorithm will provide greater confidence in the detection of truly unknown compounds and allow prioritization of candidates for more detailed and costly structural analysis. (4) The more accurate algorithm will benefit biomarker discovery by increasing the accuracy of quantitative metabolite information extracted from the raw metabolomics data.

项目摘要 /摘要 无参数的峰值检测算法，用于减少从 原始质谱代谢组学数据 与液态或气相色谱法（LC或GC）结合的质谱（MS）已成为必不可少的分析 非靶向代谢组学的ICAL平台。具有灵敏度，色谱分辨率和质量测量 准确性不断提高，现在可以检测到越来越多的分析物，这具有巨大的潜力 在我们对新陈代谢的理解方面取得了长足的进步。 RAW LC-和GC-MS预处理的第一步 代谢组学数据是代表离子的峰的检测和提取。但是，现有的峰值检测 算法总是产生大量的假阳性和假阴性峰。这些不正确的峰 可以将下游转化为虚假或缺失的复合标识。此外，大量 必须为这些算法指定参数。不幸的是，普通用户通常不了解 如何优化这些参数，并最大化一个方面（例如灵敏度）通常会删除效果 在另一个（例如特异性）上。为了应对挑战，我们提出了检测峰的范式转变 通过简单地考虑离子信息的三个维度。这将大大增加 与执行峰值检测的现有算法相比，峰值检测的准确性 通过在三个单独的2D平面中处理数据。我们提出的研究结果将使代谢组学受益 通过多种方式进行研究。 （1）更准确的算法将消除或减少对结果的手动检查 最低。 （2）通过无参数设计，研究人员将不必经历许多一轮反复试验 错误以确定一组处理参数的最佳折衷。 （3）更准确的算法将 对检测真正未知化合物的检测提供更大的信心，并允许候选人的优先级 更详细且昂贵的结构分析。 （4）更准确的算法将使生物标志物的发现受益 提高从原始代谢组学数据中提取的定量代谢物信息的准确性。