A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

Kou, Qiang; Wu, Si; Tolić, Nikola; Paša‐Tolić, Ljiljana; Liu, Yunlong; Liu, Xiaowen

doi:10.1093/bioinformatics/btw806

Cited by 35 publications

(49 citation statements)

References 29 publications

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“…In top-down MS, many software tools have been developed for the identification of proteoforms with PTMs and other alterations [8–12]. However, these software tools are designed for analyzing tandem mass spectra from single proteoforms, not multiplexed ones.…”

Section: Introductionmentioning

confidence: 99%

A graph-based approach for proteoform identification and quantification using top-down homogeneous multiplexed tandem mass spectra

Zhu

Liu

2018

BMC Bioinformatics

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BackgroundTop-down homogeneous multiplexed tandem mass (HomMTM) spectra are generated from modified proteoforms of the same protein with different post-translational modification patterns. They are frequently observed in the analysis of ultramodified proteins, some proteoforms of which have similar molecular weights and cannot be well separated by liquid chromatography in mass spectrometry analysis.ResultsWe formulate the top-down HomMTM spectral identification problem as the minimum error k-splittable flow problem on graphs and propose a graph-based algorithm for the identification and quantification of proteoforms using top-down HomMTM spectra.ConclusionsExperiments on a top-down mass spectrometry data set of the histone H4 protein showed that the proposed method identified many proteoform pairs that better explain the query spectra than single proteoforms.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2273-4) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

A graph-based approach for proteoform identification and quantification using top-down homogeneous multiplexed tandem mass spectra

Zhu

Liu

2018

BMC Bioinformatics

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“…Represented by tools such as ProsightPC [3, 4] and TopPIC [5], database search [6, 7] is the dominant approach for top-down MS-based proteoform identification, in which top-down tandem mass (MS/MS) spectra are searched against a protein sequence database to identify the best matches between the spectra and protein sequences in the database. Maping a query spectrum generated from a proteoform with multiple alterations to its corresponding database protein sequence without alterations is a challenging computational problem.…”

Section: Introductionmentioning

confidence: 99%

“…The second approach uses spectral alignment algorithms [5–7, 10–12] to align top-down MS/MS spectra from modified proteoforms against unmodified database protein sequences. Although spectral alignment is effective for single protein spectrum matches, it is very time consuming to align thousands of query spectra to against thousands of protein sequences in a large database.…”

Section: Introductionmentioning

confidence: 99%

A graph-based filtering method for top-down mass spectral identification

Yang

Zhu²

2018

BMC Genomics

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BackgroundDatabase 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.ResultsIn 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.ConclusionsExperimental 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.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5026-x) contains supplementary material, which is available to authorized users.

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“…Database search [3], [4], represented by tools such as ProSightPC [5] and TopPIC [6], is the dominant approach for top-down MS-based proteoform identification, in which top-down tandem mass (MS/MS) spectra are searched against a protein sequence database to identify matches between spectra and protein sequences. It is a challenging computational problem because the target proteoform that produced the query spectrum often contains multiple alterations that are not included in its corresponding database protein sequence.…”

Section: Introductionmentioning

confidence: 99%

“…The second approach is to employ spectral alignment algorithms [3], [4], [6], [8]–[10] to align a top-down MS/MS spectrum from a modified proteoform against unmodified database protein sequences. While spectral alignment is efficient to align a spectrum against a protein sequence, it is extremely time-consuming to align thousands of query spectra against all protein sequences in a large database.…”

Section: Introductionmentioning

confidence: 99%

A spectrum graph-based protein sequence filtering algorithm for proteoform identification by top-down mass spectrometry

Yang

Zhu

Kou

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Database search is the main approach for identifying proteoforms using top-down tandem mass spectra. However, it is extremely slow to align a query spectrum against all protein sequences in a large database when the target proteoform that produced the spectrum contains post-translational modifications and/or mutations. As a result, efficient and sensitive protein sequence filtering algorithms are essential for speeding up database search. In this paper, we propose a novel filtering algorithm, which generates spectrum graphs from subspectra of the query spectrum and searches them against the protein database to find good candidates. Compared with the sequence tag and gaped tag approaches, the proposed method circumvents the step of tag extraction, thus simplifying data processing. Experimental results on real data showed that the proposed method achieved both high speed and high sensitivity in protein sequence filtration.

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A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 35 publications

References 29 publications

A graph-based approach for proteoform identification and quantification using top-down homogeneous multiplexed tandem mass spectra

A graph-based approach for proteoform identification and quantification using top-down homogeneous multiplexed tandem mass spectra

A graph-based filtering method for top-down mass spectral identification

A spectrum graph-based protein sequence filtering algorithm for proteoform identification by top-down mass spectrometry

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