MS-based de novo peptide sequencing has been improved remarkably with significant development of mass-spectrometry and computational approaches but still lacks quality-control methods. Here we proposed a novel algorithm pSite to evaluate the confidence of each amino acid rather than the full-length peptides obtained by de novo peptide sequencing. A semi-supervised learning approach was used to discriminate correct amino acids from random one; then, an expectation-maximization algorithm was used to adaptively control the false amino-acid rate (FAR). On three test data sets, pSite recalled 86% more amino acids on average than PEAKS at the FAR of 5%.

中文翻译：

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pSite：用于从头肽测序和修饰位点定位的质量控制的氨基酸置信度评估

随着质谱和计算方法的重大发展，基于质谱的从头肽测序已得到显着改善，但仍缺乏质量控制方法。在这里，我们提出了一种新颖的算法pSite来评估每个氨基酸的可信度，而不是通过从头测序获得的全长肽的可信度。使用半监督学习方法将正确的氨基酸与随机的氨基酸区分开。然后，采用期望最大化算法自适应地控制假氨基酸率（FAR）。在三个测试数据集上，pSite在5％的FAR时召回的氨基酸平均比PEAKS多86％。