IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics
IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics
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DOI：
10.1016/j.jprot.2011.06.003
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发表时间：
2011-12-10
影响因子：
3.3
通讯作者：
Kolker, Eugene
中科院分区：
文献类型：
--
作者：
Higdon, Roger;Reiter, Lukas;Kolker, Eugene
在高通量质谱蛋白质组学中，肽和蛋白质并不是简单地鉴定为样品中存在或不存在，而是鉴定与不同的置信度相关。错误发现率 (FDR) 已成为衡量与识别相关的置信度的公认方法。我们开发了系统蛋白质调查研究环境 (SPIRE)，旨在整合现有的最佳蛋白质组学方法。 MAYU 和我们当前的 SPIRE 方法是估计 MS 蛋白质鉴定的 FDR 的两种成功方法。我们在此提出了一种将这两种方法结合起来的方法，以估计 MS 蛋白质鉴定的 FDR 到集成蛋白质模型 (IPM) 中。我们通过对两个大型公开蛋白质组数据集进行测试来说明这种 IPM 方法的高质量性能。 MAYU 和 SPIRE 在识别这些数据集中的蛋白质方面表现出显着的一致性。尽管如此，IPM 仍能带来更稳健的 FDR 估计方法和额外的鉴定，特别是在低丰度蛋白质中。 IPM 现在作为 SPIRE 系统的一部分实施。 (C) 2011 年由 Elsevier B.V. 出版
In high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levels of confidence. The false discovery rate (FDR) has emerged as an accepted means for measuring the confidence associated with identifications. We have developed the Systematic Protein Investigative Research Environment (SPIRE) for the purpose of integrating the best available proteomics methods. Two successful approaches to estimating the FDR for MS protein identifications are the MAYU and our current SPIRE methods. We present here a method to combine these two approaches to estimating the FDR for MS protein identifications into an integrated protein model (IPM). We illustrate the high quality performance of this IPM approach through testing on two large publicly available proteomics datasets. MAYU and SPIRE show remarkable consistency in identifying proteins in these datasets. Still, IPM results in a more robust FDR estimation approach and additional identifications, particularly among low abundance proteins. IPM is now implemented as a part of the SPIRE system. (C) 2011 Published by Elsevier B.V.