Peptizer, a Tool for Assessing False Positive Peptide Identifications and Manually Validating Selected Results
Peptizer, a Tool for Assessing False Positive Peptide Identifications and Manually Validating Selected Results
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DOI：
10.1074/mcp.m800082-mcp200
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发表时间：
2008-12-01
影响因子：
7
通讯作者：
Martens, Lennart
中科院分区：
文献类型：
--
作者：
Helsens, Kenny;Timmerman, Evy;Martens, Lennart
假阳性肽鉴定是以肽为中心、质谱驱动的无凝胶蛋白质组学领域的一个主要问题。它们发生在真阳性和真阴性分数分布重叠的区域。消除这些假阳性识别必然需要在敏感性和特异性之间进行权衡。现有的后处理工具通常依赖于一组固定或半固定的假设来尝试优化使用 MS/MS 谱图识别肽和蛋白质的灵敏度和特异性。然而，由于可用蛋白质组学技术的多样性不断扩大，这些后处理工具往往难以适应新兴技术的特定特性。在这里，我们提出了一种名为 Peptizer 的新颖工具，它通过使用可插入假设来解决这种适应性问题。这种面向研究的后处理工具还包括一个图形用户界面，可以对可疑识别进行有效的手动验证，以实现最佳的灵敏度恢复。 Peptizer 是 Apache2 许可证下的开源软件，用 Java 编写。分子与细胞蛋白质组学 7：2364-2372，2008。
False positive peptide identifications are a major concern in the field of peptidecentric, mass spectrometry-driven gel-free proteomics. They occur in regions where the score distributions of true positives and true negatives overlap. Removal of these false positive identifications necessarily involves a trade-off between sensitivity and specificity. Existing postprocessing tools typically rely on a fixed or semifixed set of assumptions in their attempts to optimize both the sensitivity and the specificity of peptide and protein identification using MS/MS spectra. Because of the expanding diversity in available proteomics technologies, however, these postprocessing tools often struggle to adapt to emerging technology-specific peculiarity. Here we present a novel tool named Peptizer that solves this adaptability issue by making use of pluggable assumptions. This research-oriented postprocessing tool also includes a graphical user interface to perform efficient manual validation of suspect identifications for optimal sensitivity recovery. Peptizer is open source software under the Apache2 license and is written in Java. Molecular & Cellular Proteomics 7: 2364-2372, 2008.