The C-score: a Bayesian framework to sharply improve proteoform scoring in high-throughput top down proteomics.
The C-score: a Bayesian framework to sharply improve proteoform scoring in high-throughput top down proteomics.
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DOI：
10.1021/pr401277r
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发表时间：
2014-07-03
影响因子：
4.4
通讯作者：
Kelleher, Neil L.
中科院分区：
文献类型：
--
作者：
LeDuc, Richard D.;Fellers, Ryan T.;Early, Bryan P.;Greer, Joseph B.;Thomas, Paul M.;Kelleher, Neil L.
自上而下蛋白质组学产生的数据的自动化处理将受益于蛋白质鉴定和高度相关蛋白质形式（蛋白质形式）表征的改进评分。在这里，我们提出了“C分数”（表征分数的缩写），这是一种用于蛋白质形式识别和表征问题的贝叶斯方法，在一个框架内实现，以允许将专家知识注入到利用蛋白质已知特性和自上而下分析系统（例如，片段化倾向、“偏离1 Da”不连续误差以及针对位点特定修改的智能加权）。将基于初始生成模型的评分系统的性能与ProSightPC和ProSightPTM中使用的当前基于概率的评分系统在手动策划的295种人蛋白质组上进行比较。目前实施的C评分框架产生了显着的改善，现有的评分系统作为测量的ROC曲线图上的曲线下面积（AUC为0.99与0.78）。
The automated processing of data generated by top down proteomics would benefit from improved scoring for protein identification and characterization of highly related protein forms (proteoforms). Here we propose the “C-score” (short for Characterization Score), a Bayesian approach to the proteoform identification and characterization problem, implemented within a framework to allow the infusion of expert knowledge into generative models that take advantage of known properties of proteins and top down analytical systems (e.g., fragmentation propensities, “off-by-1 Da” discontinuous errors, and intelligent weighting for site-specific modifications). The performance of the scoring system based on the initial generative models was compared to the current probability-based scoring system used within both ProSightPC and ProSightPTM on a manually curated set of 295 human proteoforms. The current implementation of the C-score framework generated a marked improvement over the existing scoring system as measured by the area under the curve on the resulting ROC chart (AUC of 0.99 versus 0.78).
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