标题：A graph-based filtering method for top-down mass spectral identification
作者：Yang, Runmin; Zhu, Daming
作者机构：[Yang, Runmin; Zhu, Daming] Shandong Univ, Sch Comp Sci & Technol, 1500 Shun Hua Lu, Jinan 250101, Shandong, Peoples R China.
会议名称：IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Genomics
会议日期：NOV 13-16, 2017
关键词：Mass spectrometry; Filtering algorithm; Spectrum graph
摘要：Background: Database search has been the main approach for proteoform identification by top-down tandem mass spectrometry. However, when the target proteoform that produced the spectrum contains post-translational modifications (PTMs) and/or mutations, it is quite time consuming to align a query spectrum against all protein sequences without any PTMs and mutations in a large database. Consequently, it is essential to develop efficient and sensitive filtering algorithms for speeding up database search.; Results: In this paper, we propose a spectrum graph matching (SGM) based protein sequence filtering method for top-down mass spectral identification. It uses the subspectra of a query spectrum to generate spectrum graphs and searches them against a protein database to report the best candidates. As the sequence tag and gaped tag approaches need the preprocessing step to extract and select tags, the SGM filtering method circumvents this preprocessing step, thus simplifying data processing. We evaluated the filtration efficiency of the SGM filtering method with various parameter settings on an Escherichia coli top-down mass spectrometry data set and compared the performances of the SGM filtering method and two tag-based filtering methods on a data set of MCF-7 cells.; Conclusions: Experimental results on the data sets show that the SGM filtering method achieves high sensitivity in protein sequence filtration. When coupled with a spectral alignment algorithm, the SGM filtering method significantly increases the number of identified proteoform spectrum-matches compared with the tag-based methods in top-down mass spectrometry data analysis.