Using a spike-in experiment to evaluate analysis of LC-MS data.
Using a spike-in experiment to evaluate analysis of LC-MS data.
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DOI：
10.1186/1477-5956-10-13
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发表时间：
2012-02-27
期刊：
影响因子：
2
通讯作者：
Ressom HW
中科院分区：
文献类型：
--
作者：
Tuli L;Tsai TH;Varghese RS;Xiao JF;Cheema A;Ressom HW
液相色谱-质谱（LC-MS）技术的最新进展已经导致用于测量生物样品中的肽/蛋白质丰度的变化的更有效的方法。无标记LC-MS方法已用于提取定量信息和检测差异丰富的肽/蛋白质。然而，通过分析来自无标记LC-MS方法的数据进行差异检测需要各种预处理步骤，包括过滤、基线校正、峰检测、比对和归一化。虽然已经开发了几种专门的工具来分析LC-MS数据，但确定最合适的计算管道仍然具有挑战性，部分原因是缺乏既定的黄金标准。本文中的工作是一个初步的研究，开发一个简单的模型与“存在”或“不存在”的条件下，使用spike-in实验，并能够识别这些“真正的差异”，使用现有的软件工具。除了预处理管道之外，选择适当的统计测试和确定临界值也很重要。我们观察到，由于不同的假设和采用的指标，个别统计测试可能会导致不同的结果。因此，为了探索或确认的目的，最好结合几个统计检验。来自我们的加标实验的LC-MS数据可用于开发和优化LC-MS数据预处理算法，并评估现有软件工具中实施的工作流程。我们目前的工作是优化LC-MS数据采集和测试未来研究中差异检测计算工具的准确性和有效性的垫脚石，这些研究将集中在不同浓度的不同理化性质的肽上，以更好地代表差异丰富肽/蛋白质的生物标志物发现。
Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose. The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.
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影响因子：
7.4
作者：
Pierce, KM;Wood, LF;Synovec, RE
通讯作者：
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DOI：
10.1111/1467-9868.00346
发表时间：
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影响因子：
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作者：
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作者：
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