RAMSY: ratio analysis of mass spectrometry to improve compound identification.
RAMSY: ratio analysis of mass spectrometry to improve compound identification.
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DOI：
10.1021/ac4019268
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发表时间：
2013-11-19
影响因子：
7.4
通讯作者：
Raftery, Daniel
中科院分区：
文献类型：
--
作者：
Gu, Haiwei;Gowda, G. A. Nagana;Neto, Fausto Carnevale;Opp, Mark R.;Raftery, Daniel
生物样品的复杂性对质谱（MS）中可靠的化合物鉴定提出了重大挑战。干扰化合物的存在会在光谱中产生额外的峰，这可能会使解释和分配变得困难。为了克服这个问题，需要新的方法来降低复杂性和简化光谱解释。最近，专注于未知代谢物的鉴定，我们提出了一种新的方法，RANASY，（核磁共振光谱的比率分析，分析。2011，83，7616-7623），其基于峰强度比提取与相同代谢物相关的1H信号。基于这一概念，我们提出了比率分析质谱（RAMSY）的方法，这有利于改善复杂的MS谱中的化合物鉴定。RAMSY的工作原理是，在给定的一组实验条件下，来自相同代谢物的质量片段之间的丰度/强度比相对恒定。因此，使用来自相同离子色谱图的一小组MS光谱生成的平均峰比及其标准偏差的乘积，有效地允许代谢物峰的统计回收率，并促进可靠的鉴别。RAMSY应用于气相色谱（GC）-MS和液相色谱串联MS（LC-MS/MS）数据，以证明其实用性。RAMSY的性能通常优于相关方法的结果。RAMSY承诺为代谢组学或其他领域的MS用户改进未知代谢物鉴定。
The complexity of biological samples poses a major challenge for reliable compound identification in mass spectrometry (MS). The presence of interfering compounds that cause additional peaks in the spectrum can make interpretation and assignment difficult. To overcome this issue, new approaches are needed to reduce complexity and simplify spectral interpretation. Recently, focused on unknown metabolite identification, we presented a new approach, RANSY, (Ratio Analysis of Nuclear Magnetic Resonance Spectroscopy, Anal. Chem. 2011, 83, 7616–7623), which extracts the 1H signals related to the same metabolite based on peak intensity ratios. Based on this concept, we present the Ratio Analysis of Mass Spectrometry (RAMSY) method, which facilitates improved compound identification in complex MS spectra. RAMSY works on the principle that, under a given set of experimental conditions, the abundance/intensity ratios between the mass fragments from the same metabolite are relatively constant. Therefore, the quotients of average peak ratios and their standard deviations, generated using a small set of MS spectra from the same ion chromatogram, efficiently allow the statistical recovery of the metabolite peaks and facilitate reliable identification. RAMSY was applied to both gas chromatography (GC)-MS and liquid chromatography tandem MS (LC-MS/MS) data to demonstrate its utility. The performance of RAMSY is typically better than the results from correlation methods. RAMSY promises to improve unknown metabolite identification for MS users in metabolomics or other fields.
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影响因子：
13.1
作者：
Dunn, WB;Ellis, DI
通讯作者：
Ellis, DI
影响因子：
7.7
作者：
Reaves, Marshall Louis;Rabinowitz, Joshua D.
通讯作者：
Rabinowitz, Joshua D.
影响因子：
4.4
作者：
Holmes, E.;Cloarec, O.;Nicholson, J. K.
通讯作者：
Nicholson, J. K.
影响因子：
14.9
作者：
Wishart DS;Jewison T;Guo AC;Wilson M;Knox C;Liu Y;Djoumbou Y;Mandal R;Aziat F;Dong E;Bouatra S;Sinelnikov I;Arndt D;Xia J;Liu P;Yallou F;Bjorndahl T;Perez-Pineiro R;Eisner R;Allen F;Neveu V;Greiner R;Scalbert A
通讯作者：
Scalbert A
影响因子：
7.4
作者：
Kind, Tobias;Wohlgemuth, Gert;Lee, Do Yup;Lu, Yun;Palazoglu, Mine;Shahbaz, Sevini;Fiehn, Oliver
通讯作者：
Fiehn, Oliver