Piening Brian D, Wang Pei, Bangur Chaitanya S, Whiteaker Jeffrey, Zhang Heidi, Feng Li-Chia, Keane John F, Eng Jimmy K, Tang Hua, Prakash Amol, McIntosh Martin W, Paulovich Amanda
Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N, Seattle, Washington, 98109, USA.
J Proteome Res. 2006 Jul;5(7):1527-34. doi: 10.1021/pr050436j.
Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Because the Mass Deviance metric is derived from the natural distribution of peptide masses, it is machine- and proteome-independent and enables assessment of feature detection tools in the absence of completely annotated data sets. We validate the use of Mass Deviance with a second, independent metric that is based on isotopic distributions, demonstrating that we can use Mass Deviance to identify aberrant features with high accuracy. We then demonstrate the use of independent metrics in tandem as a robust way to evaluate the performance of peptide feature detection algorithms. This work is done on complex LC-MS profiles of Saccharomyces cerevisiae which present a significant challenge to peptide feature detection algorithms.
使用液相色谱 - 质谱法进行定量蛋白质组分析正在成为生物标志物发现的重要工具，这促使人们开发用于在复杂样品中进行高通量肽特征检测的算法。然而，目前既没有注释的标准数据集，也没有质量控制指标来评估特征检测算法的有效性。我们提出了一种质量控制指标——质量偏差，用于评估特征检测工具的准确性。由于质量偏差指标源自肽质量的自然分布，它与机器和蛋白质组无关，并且能够在没有完全注释数据集的情况下评估特征检测工具。我们使用基于同位素分布的第二个独立指标验证了质量偏差的使用，证明我们可以使用质量偏差高精度地识别异常特征。然后，我们展示了串联使用独立指标作为评估肽特征检测算法性能的可靠方法。这项工作是在酿酒酵母的复杂液相色谱 - 质谱图上完成的，这对肽特征检测算法提出了重大挑战。
