Probabilistic assignment of formulas to mass peaks in metabolomics experiments
Probabilistic assignment of formulas to mass peaks in metabolomics experiments
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DOI：
10.1093/bioinformatics/btn642
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发表时间：
2009-02-15
期刊：
影响因子：
5.8
通讯作者：
Breitling, Rainer
中科院分区：
文献类型：
--
作者：
Rogers, Simon;Scheltema, Richard A.;Breitling, Rainer
动机：高精度质谱是一种流行的技术，用于高通量测量细胞代谢物（代谢组学）。其中一个主要的挑战是正确识别所观察到的质量峰，包括其经验公式的分配，基于测得的mass.Results：我们提出了一种新的概率方法分配经验公式的质量峰在高通量代谢组学质谱测量。该方法结合了关于经验公式之间可能的生化转化的信息，以将更高的概率分配给可以从样品中的其他代谢物创建的公式。在一系列的实验中，我们表明，该方法表现良好，并提供了更大的洞察力比基于质量的分配。此外，我们扩展了模型，将同位素信息，以实现更可靠的公式识别。
Motivation: High-accuracy mass spectrometry is a popular technology for high-throughput measurements of cellular metabolites (metabolomics). One of the major challenges is the correct identification of the observed mass peaks, including the assignment of their empirical formula, based on the measured mass.Results: We propose a novel probabilistic method for the assignment of empirical formulas to mass peaks in high-throughput metabolomics mass spectrometry measurements. The method incorporates information about possible biochemical transformations between the empirical formulas to assign higher probability to formulas that could be created from other metabolites in the sample. In a series of experiments, we show that the method performs well and provides greater insight than assignments based on mass alone. In addition, we extend the model to incorporate isotope information to achieve even more reliable formula identification.