High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics
High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics
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DOI：
10.1002/pmic.200300708
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发表时间：
2004-07-01
期刊：
影响因子：
3.4
通讯作者：
Bougueleret, L
中科院分区：
文献类型：
--
作者：
Colinge, J;Masselot, A;Bougueleret, L
在之前的一篇论文中，我们介绍了一种新的基于模型的方法（OLAV）来解决通过串联质谱识别肽的问题，早期的实现显示出有希望的性能。我们最近进一步将这一性能提高到一个显着的水平（假阳性率为1-2%，真阳性率为95%），并表征了OLAV的鲁棒性和训练集大小等关键属性。我们以综合和连贯的方式呈现这些结果，以及详细的性能比较，利用肽氨基酸组成的新评分组件，以及自动参数学习等新发展。最后，我们讨论了OLAV对蛋白质组学项目自动化的影响。
In a previous paper we introduced a novel model-based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1-2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects.