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基于特征的串联质谱去同位素方法。

Features-based deisotoping method for tandem mass spectra.

作者信息

Yuan Zheng, Shi Jinhong, Lin Wenjun, Chen Bolin, Wu Fang-Xiang

机构信息

Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N5A9.

出版信息

Adv Bioinformatics. 2011;2011:210805. doi: 10.1155/2011/210805. Epub 2012 Jan 4.

Abstract

For high-resolution tandem mass spectra, the determination of monoisotopic masses of fragment ions plays a key role in the subsequent peptide and protein identification. In this paper, we present a new algorithm for deisotoping the bottom-up spectra. Isotopic-cluster graphs are constructed to describe the relationship between all possible isotopic clusters. Based on the relationship in isotopic-cluster graphs, each possible isotopic cluster is assessed with a score function, which is built by combining nonintensity and intensity features of fragment ions. The non-intensity features are used to prevent fragment ions with low intensity from being removed. Dynamic programming is adopted to find the highest score path with the most reliable isotopic clusters. The experimental results have shown that the average Mascot scores and F-scores of identified peptides from spectra processed by our deisotoping method are greater than those by YADA and MS-Deconv software.

摘要

对于高分辨率串联质谱,碎片离子单同位素质量的测定在随后的肽段和蛋白质鉴定中起着关键作用。在本文中,我们提出了一种用于自下而上谱图去同位素处理的新算法。构建同位素簇图以描述所有可能的同位素簇之间的关系。基于同位素簇图中的关系,使用一个得分函数对每个可能的同位素簇进行评估,该得分函数通过结合碎片离子的非强度和强度特征构建而成。非强度特征用于防止低强度的碎片离子被去除。采用动态规划来寻找具有最可靠同位素簇的最高得分路径。实验结果表明,用我们的去同位素方法处理后的谱图所鉴定出的肽段,其平均 Mascot 得分和 F 得分高于 YADA 和 MS-Deconv 软件处理后的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8a0/3259476/ec15c677b224/ABI2011-210805.001.jpg

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