msmsEval: tandem mass spectral quality assignment for high-throughput proteomics.
msmsEval: tandem mass spectral quality assignment for high-throughput proteomics.
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DOI：
10.1186/1471-2105-8-51
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发表时间：
2007-02-09
影响因子：
3
通讯作者：
Cagney, Gerard
中科院分区：
文献类型：
--
作者：
Wong, Jason W. H.;Sullivan, Matthew J.;Cartwright, Hugh M.;Cagney, Gerard
在蛋白质组学实验中，数据库搜索程序是从串联质谱鉴定蛋白质的首选方法。然而，随着氨基酸序列数据库的增长，这些程序所需的计算资源已经变得令人望而却步，特别是在搜索修饰蛋白质时。最近，不同的研究小组已经提出了根据光谱质量限制要搜索的光谱数量的方法，但是光谱质量的排名迄今为止一直是基于任意的截止值。在这项工作中，我们开发了一个更容易解释的光谱质量统计提供的可能性，光谱将被识别的概率值。我们描述了一个应用程序，msmsEval，建立在以前的工作通过统计建模的光谱质量判别函数使用高斯混合模型。这允许研究人员基于光谱将最终通过数据库搜索识别的概率来过滤光谱。我们表明，光谱预测的msmsEval是高品质的，但在标准的数据库搜索中仍然不明，是更密集的搜索策略的候选人。使用一个经过充分研究的公共数据集，我们还表明，高比例（83.9%）的光谱预测msmsEval是高品质的，但逃避标准的搜索策略，实际上是可解释的。msmsEval将对高通量蛋白质组学项目有用，可从免费下载。支持Windows、Mac OS X和Linux/Unix操作系统。
In proteomics experiments, database-search programs are the method of choice for protein identification from tandem mass spectra. As amino acid sequence databases grow however, computing resources required for these programs have become prohibitive, particularly in searches for modified proteins. Recently, methods to limit the number of spectra to be searched based on spectral quality have been proposed by different research groups, but rankings of spectral quality have thus far been based on arbitrary cut-off values. In this work, we develop a more readily interpretable spectral quality statistic by providing probability values for the likelihood that spectra will be identifiable. We describe an application, msmsEval, that builds on previous work by statistically modeling the spectral quality discriminant function using a Gaussian mixture model. This allows a researcher to filter spectra based on the probability that a spectrum will ultimately be identified by database searching. We show that spectra that are predicted by msmsEval to be of high quality, yet remain unidentified in standard database searches, are candidates for more intensive search strategies. Using a well studied public dataset we also show that a high proportion (83.9%) of the spectra predicted by msmsEval to be of high quality but that elude standard search strategies, are in fact interpretable. msmsEval will be useful for high-throughput proteomics projects and is freely available for download from . Supports Windows, Mac OS X and Linux/Unix operating systems.
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影响因子：
4.4
作者：
Xu, M;Geer, LY;Markey, SP
通讯作者：
Markey, SP
影响因子：
5.8
作者：
Craig, R;Beavis, RC
通讯作者：
Beavis, RC
DOI：
10.1016/s1044-0305(00)00097-0
发表时间：
2000-05-01
影响因子：
3.2
作者：
Moore, RE;Young, MK;Lee, TD
通讯作者：
Lee, TD
影响因子：
2
作者：
TANG, XJ;BOYD, RK
通讯作者：
BOYD, RK
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB