LIP index for peptide classification using MS/MS and SEQUEST search via logistic regression
LIP index for peptide classification using MS/MS and SEQUEST search via logistic regression
复制标题
DOI：
10.1089/omi.2004.8.357
复制
发表时间：
2004-12-01
影响因子：
3.3
通讯作者：
Kolker, E
中科院分区：
文献类型：
--
作者：
Higdon, R;Kolker, N;Kolker, E
本研究解决了由串联质谱蛋白质组学分析，然后通过数据库搜索的肽鉴定的问题。这项工作表明，相对于手动验证的“金”标准，肽的逻辑识别（LIP）指数实现了肽分类的高灵敏度和特异性，并且还准确地估计了正确肽匹配的概率。LIP指数是基于逻辑回归模型的SEQUEST输出变量的加权平均值，是一种透明，易于使用，包容性，可扩展且统计上合理的方法，用于对正确的肽鉴定进行分类。修改，如肽长度的标准化交叉相关性（Xcorr），调整电荷状态和胰蛋白酶末端的数量，显着提高了逻辑回归模型的拟合，以及增加灵敏度和特异性。LIP指数还结合了早期开发的光谱质量评估和肽识别统计模型，进一步提高了灵敏度和特异性。
This study addresses the issue of peptide identification resulting from tandem mass spectrometry proteomics analysis followed by database search. This work shows that the Logistic Identification of Peptides (LIP) Index achieves high sensitivity and specificity for peptide classification relative to a manually verified "gold" standard and also accurately estimates the probability of a correct peptide match. The LIP Index is a weighted average of SEQUEST output variables based on logistic regression models and is a transparent, easy to use, inclusive, extendable, and statistically sound approach to classify correct peptide identifications. Modifications, such as normalizing cross-correlations (Xcorr) for peptide length, adjusting for charge state, and the number of tryptic termini, significantly improve the fit the logistic regression models, as well as increase sensitivity and specificity. The LIP Index also incorporates earlier developed statistical models on spectral quality assessment and peptide identification, which further improves sensitivity and specificity.