On using samples of known protein content to assess the statistical calibration of scores assigned to peptide-spectrum matches in shotgun proteomics.
On using samples of known protein content to assess the statistical calibration of scores assigned to peptide-spectrum matches in shotgun proteomics.
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DOI：
10.1021/pr1012619
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发表时间：
2011-05-06
影响因子：
4.4
通讯作者：
Kall, Lukas
中科院分区：
文献类型：
--
作者：
Granholm, Viktor;Noble, William Stafford;Kall, Lukas
关键词：
在散弹枪蛋白质组学中，观察到的光谱和肽序列之间的假设匹配的质量通过评分函数来量化。由于评分功能是任何肽鉴定管道的核心，因此该功能极大地影响了蛋白质组学分析的最终结果。因此，评估给定分数函数质量的有效统计方法是极其重要的。此前，几个研究小组已经使用已知蛋白质组成的样本来评估给定评分函数的质量。我们证明这种方法是有问题的，因为结果可能取决于分数函数本身以外的因素。然后，我们提出使用相同类型的数据来评估给定分数函数的质量的替代方法。我们的方法的中心思想是，数据库匹配不能被纯化样品中的任何蛋白质解释，包括不正确匹配的稳健表示。我们将我们的替代评估方案应用于几种常用的评分函数，并且我们表明，我们的方法产生了给定肽鉴定方法校准的可重复测量。此外，我们展示了我们的质量测试如何在开发新的分数函数中发挥作用。
In shotgun proteomics, the quality of a hypothesized match between an observed spectrum and a peptide sequence is quantified by a score function. Because the score function lies at the heart of any peptide identification pipeline, this function greatly affects the final results of a proteomics assay. Consequently, valid statistical methods for assessing the quality of a given score function are extremely important. Previously, several research groups have used samples of known protein composition to assess the quality of a given score function. We demonstrate that this approach is problematic, because the outcome can depend on factors other than the score function itself. We then propose an alternative use of the same type of data to assess the quality of a given score function. The central idea of our approach is that database matches that are not explained by any protein in the purified sample comprise a robust representation of incorrect matches. We apply our alternative assessment scheme to several commonly used score functions, and we show that our approach generates a reproducible measure of the calibration of a given peptide identification method. Furthermore, we show how our quality test can be useful in the development of novel score functions.
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