Automatic Quality Assessment of Peptide Tandem Mass Spectra
Automatic Quality Assessment of Peptide Tandem Mass Spectra
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DOI：
10.1093/bioinformatics/bth947
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发表时间：
2004-08-04
期刊：
影响因子：
5.8
通讯作者：
Yates, John R., III
中科院分区：
文献类型：
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作者：
Bern, Marshall;Goldberg, David;Yates, John R., III
动机：一种强大的蛋白质组学方法将高效液相色谱(HPLC)与串联质谱仪和数据库搜索软件(如SEQUEST)相结合。然而，这样的设置产生了大量的光谱，其中许多质量太差而没有用。因此，在数据库搜索之前消除不良频谱的过滤器可以显著提高吞吐量和稳健性。此外，被判断为高质量但无法通过数据库搜索识别的光谱，是计算更密集的方法的首选候选方法，例如从头测序或更广泛的数据库搜索，包括翻译后修改。结果：我们报告了两种不同的方法在识别之前评估光谱质量：二进制分类，预测SEQUEST是否能够进行识别；统计回归，预测更普遍的质量度量，包括b离子和y离子的数量。我们最好的二进制分类器可以消除75%以上的不可识别光谱，而只损失10%的可识别光谱。统计回归可以挑选出可以通过从头程序识别但不能通过SEQUEST识别的修饰多肽的光谱。在独立感兴趣的一节中，我们讨论了质量谱的强度归一化。
Motivation: A powerful proteomics methodology couples high-performance liquid chromatography (HPLC) with tandem mass spectrometry and database-search software, such as SEQUEST. Such a set-up, however, produces a large number of spectra, many of which are of too poor quality to be useful. Hence a filter that eliminates poor spectra before the database search can significantly improve throughput and robustness. Moreover, spectra judged to be of high quality, but that cannot be identified by database search, are prime candidates for still more computationally intensive methods, such as de novo sequencing or wider database searches including post-translational modifications.Results: We report on two different approaches to assessing spectral quality prior to identification: binary classification, which predicts whether or not SEQUEST will be able to make an identification, and statistical regression, which predicts a more universal quality metric involving the number of b- and y-ion peaks. The best of our binary classifiers can eliminate over 75% of the unidentifiable spectra while losing only 10% of the identifiable spectra. Statistical regression can pick out spectra of modified peptides that can be identified by a de novo program but not by SEQUEST. In a section of independent interest, we discuss intensity normalization of mass spectra.