Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification Using Top‐Down Mass Spectrometry

Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification... Complex proteoforms contain various primary structural alterations resulting from variations in genes, RNA, and proteins. Top‐down mass spectrometry is commonly used for analyzing complex proteoforms because it provides whole sequence information of the proteoforms. Proteoform identification by top‐down mass spectral database search is a challenging computational problem because the types and/or locations of some alterations in target proteoforms are in general unknown. Although spectral alignment and mass graph alignment algorithms have been proposed for identifying proteoforms with unknown alterations, they are extremely slow to align millions of spectra against tens of thousands of protein sequences in high throughput proteome level analyses. Many software tools in this area combine efficient protein sequence filtering algorithms and spectral alignment algorithms to speed up database search. As a result, the performance of these tools heavily relies on the sensitivity and efficiency of their filtering algorithms. Here, we propose two efficient approximate spectrum‐based filtering algorithms for proteoform identification. We evaluated the performances of the proposed algorithms and four existing ones on simulated and real top‐down mass spectrometry data sets. Experiments showed that the proposed algorithms outperformed the existing ones for complex proteoform identification. In addition, combining the proposed filtering algorithms and mass graph alignment algorithms identified many proteoforms missed by ProSightPC in proteome‐level proteoform analyses. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proteomics Wiley

Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification Using Top‐Down Mass Spectrometry

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim
ISSN
1615-9853
eISSN
1615-9861
D.O.I.
10.1002/pmic.201700306
Publisher site
See Article on Publisher Site

Abstract

Complex proteoforms contain various primary structural alterations resulting from variations in genes, RNA, and proteins. Top‐down mass spectrometry is commonly used for analyzing complex proteoforms because it provides whole sequence information of the proteoforms. Proteoform identification by top‐down mass spectral database search is a challenging computational problem because the types and/or locations of some alterations in target proteoforms are in general unknown. Although spectral alignment and mass graph alignment algorithms have been proposed for identifying proteoforms with unknown alterations, they are extremely slow to align millions of spectra against tens of thousands of protein sequences in high throughput proteome level analyses. Many software tools in this area combine efficient protein sequence filtering algorithms and spectral alignment algorithms to speed up database search. As a result, the performance of these tools heavily relies on the sensitivity and efficiency of their filtering algorithms. Here, we propose two efficient approximate spectrum‐based filtering algorithms for proteoform identification. We evaluated the performances of the proposed algorithms and four existing ones on simulated and real top‐down mass spectrometry data sets. Experiments showed that the proposed algorithms outperformed the existing ones for complex proteoform identification. In addition, combining the proposed filtering algorithms and mass graph alignment algorithms identified many proteoforms missed by ProSightPC in proteome‐level proteoform analyses.

Journal

ProteomicsWiley

Published: Jan 1, 2018

Keywords: ; ;

References

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