Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements.
Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements.
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DOI：
10.1186/1471-2105-9-375
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发表时间：
2008-09-15
影响因子：
3
通讯作者：
Gröpl C
中科院分区：
文献类型：
--
作者：
Lange E;Tautenhahn R;Neumann S;Gröpl C
液相色谱-质谱联用（LC-MS）已成为分析复杂蛋白质组学和代谢组学样品的重要工具。在许多应用中，需要对多个LC-MS测量值进行比较，例如，为了提高可靠性或在统计比较分析中结合不同样品的结果。与所有物理实验一样，LC-MS数据受到不确定性的影响，并且在所有数据集中都遇到保留时间的可变性。因此，有必要估计和纠正潜在的保留时间轴的扭曲，以在不同的样品中寻找相应的化合物。为此，在过去四年中开发了各种所谓的LC-MS地图校准算法。这些方法中的大多数都有很好的文档记录，但是它们通常只在非常具体的样本上进行评估。到目前为止，还没有出版物使用标准LC-MS样品以及常用的质量标准来评估不同的校准算法。我们提出了两个LC-MS蛋白质组学和两个LC-MS代谢组学数据集，代表典型的比对场景。此外，我们还引入了一种新的质量度量来评估LC-MS校准算法。使用这四个数据集来比较六种免费提供的用于代谢组学和蛋白质组学LC-MS测量的比对算法，我们发现在比对质量、运行时间和总体可用性方面存在显著差异。大量可用的对齐方法需要生成标准数据集和质量度量，从而允许用户和开发人员在公平的基础上对他们的地图对齐工具进行基准测试和比较。我们的研究是朝着这个方向迈出的第一步。目前，“正确”参数设置的安装和评估可能是一项相当耗时的任务，并且特定方法的成功仍然高度依赖于用户的经验。因此，我们建议将这类研究延续并扩展为社区范围的比赛。所有数据以及我们的评估脚本可在。
Liquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is therefore necessary to estimate and correct the underlying distortions of the retention time axis to search for corresponding compounds in different samples. To this end, a variety of so-called LC-MS map alignment algorithms have been developed during the last four years. Most of these approaches are well documented, but they are usually evaluated on very specific samples only. So far, no publication has been assessing different alignment algorithms using a standard LC-MS sample along with commonly used quality criteria. We propose two LC-MS proteomics as well as two LC-MS metabolomics data sets that represent typical alignment scenarios. Furthermore, we introduce a new quality measure for the evaluation of LC-MS alignment algorithms. Using the four data sets to compare six freely available alignment algorithms proposed for the alignment of metabolomics and proteomics LC-MS measurements, we found significant differences with respect to alignment quality, running time, and usability in general. The multitude of available alignment methods necessitates the generation of standard data sets and quality measures that allow users as well as developers to benchmark and compare their map alignment tools on a fair basis. Our study represents a first step in this direction. Currently, the installation and evaluation of the "correct" parameter settings can be quite a time-consuming task, and the success of a particular method is still highly dependent on the experience of the user. Therefore, we propose to continue and extend this type of study to a community-wide competition. All data as well as our evaluation scripts are available at .
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影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
5.8
作者：
Bellew, Matthew;Coram, Marc;McIntosh, Martin
通讯作者：
McIntosh, Martin
影响因子：
4.8
作者：
Ong, SE;Foster, LJ;Mann, M
通讯作者：
Mann, M
影响因子：
4.3
作者：
Colinge J;Bennett KL
通讯作者：
Bennett KL
影响因子：
5.8
作者：
Lange, Eva;Groepl, Clemens;Reinert, Knut
通讯作者：
Reinert, Knut