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首页> 外文期刊>Journal of mass spectrometry: JMS >A strategy for the prior processing of high-resolution mass spectral data obtained from high-dimensional combined fractional diagonal chromatography
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A strategy for the prior processing of high-resolution mass spectral data obtained from high-dimensional combined fractional diagonal chromatography

机译：从高维组合分数对角色谱获得的高分辨率质谱数据的先处理策略

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摘要

Combined fractional diagonal chromatography (COFRADIC) is a novel suite of gel-free technologies for the identification of biomarkers in complex peptide mixtures. For this purpose, reversed-phase high performance liquid chromatography (HPLC) technology and, in this case, matrix assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometers are extensively used. The particular characteristic of COFRADIC mass spectrometry data is the high number of chromatographic fractions, over which a peptide can be scattered. This can obstruct the quantification of the peptide abundance in the biological sample, which is required for statistical analysis. On the other hand, because of the superior peptide sorting properties of the methodology, the mass spectra become less crowded. Consequently, each peptide appears in a mass spectrum as a series of peaks with peak heights proportional to the probability of occurrence of the isotopic variants of the peptide. In this manuscript, we propose an analysis strategy concerned with the preprocessing of COFRADIC mass spectra prior to a downstream statistical analysis. The preprocessing algorithm produces for each mass spectrum a peptide list by exploiting the characteristic features that should be associated with peaks corresponding to an isotopically resolved cluster of peptide peaks. This reduction step is necessary to facilitate the clustering used in a next step to assemble the validated monoisotopic peptide peaks found over several fractions into a single peptide abundance. To assess the performance of the algorithm, two technical experiments were conducted. The proposed strategy is memory and computationally efficient.
机译：组合分数对角色谱（COFRADIC）是一套新颖的无凝胶技术，用于鉴定复杂肽混合物中的生物标志物。为此，广泛使用反相高效液相色谱（HPLC）技术，在这种情况下，广泛使用基质辅助的激光解吸/电离飞行时间质谱仪（MALDI-TOF）。 COFRADIC质谱数据的特殊特征是大量的色谱馏分，可以在上面分散肽。这可能会妨碍生物样品中多肽丰度的定量，这是统计分析所必需的。另一方面，由于该方法具有出色的肽分选特性，因此质谱图变得不太拥挤。因此，每种肽在质谱图中均显示为一系列峰，其峰高与该肽的同位素变体的出现概率成正比。在此手稿中，我们提出了一种在下游统计分析之前涉及COFRADIC质谱预处理的分析策略。预处理算法通过利用应该与与同位素峰解析的肽峰簇相对应的峰相关的特征来为每个质谱图生成一个肽表。此还原步骤对于促进下一步中的聚类是必要的，以便将在多个馏分中发现的经过验证的单同位素肽峰组装成单个肽丰度。为了评估算法的性能，进行了两个技术实验。所提出的策略是存储器并且计算效率高。

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