scholarly journals Features-Based Deisotoping Method for Tandem Mass Spectra

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Zheng Yuan ◽  
Jinhong Shi ◽  
Wenjun Lin ◽  
Bolin Chen ◽  
Fang-Xiang Wu
Keyword(s):  
Mass Spectra ◽  
Protein Identification ◽  
Score Function ◽  
Tandem Mass ◽  
Isotopic Cluster ◽  
Tandem Mass Spectra ◽  
Fragment Ions ◽  
The Relationship ◽  
Cluster Graphs

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.

Intensity-based protein identification by machine learning from a library of tandem mass spectra

2004 ◽  
Vol 22 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Joshua E Elias ◽  
Francis D Gibbons ◽  
Oliver D King ◽  
Frederick P Roth ◽  
Steven P Gygi
Keyword(s):  
Machine Learning ◽  
Mass Spectra ◽  
Protein Identification ◽  
Tandem Mass ◽  
Tandem Mass Spectra

Predicting Molecular Formulas of Fragment Ions with Isotope Patterns in Tandem Mass Spectra

2005 ◽  
Vol 2 (3) ◽  
pp. 217-230 ◽  
Author(s):  
Jingfen Zhang ◽  
Wen Gao ◽  
Jinjin Cai ◽  
Simin He ◽  
Rong Zeng ◽  
...  
Keyword(s):  
Mass Spectra ◽  
Tandem Mass ◽  
Tandem Mass Spectra ◽  
Fragment Ions

scholarly journals Understanding the limit of open search in the identification of peptides with post-translational modifications — A simulation-based study

2018 ◽  
Author(s):  
Jiaan Dai ◽  
Fengchao Yu ◽  
Ning Li ◽  
Weichuan Yu
Keyword(s):  
Analytical Model ◽  
Mass Spectra ◽  
Mass Spectrometry Data ◽  
Supplementary Information ◽  
Necessary Condition ◽  
Tandem Mass ◽  
Search Methods ◽  
Post Translational Modifications ◽  
Tandem Mass Spectra ◽  
The Relationship

AbstractMotivationAnalyzing tandem mass spectrometry data to recognize peptides in a sample is the fundamental task in computational proteomics. Traditional peptide identification algorithms perform well when identifying unmodified peptides. However, when peptides have post-translational modifications (PTMs), these methods cannot provide satisfactory results. Recently, Chick et al., 2015 and Yu et al., 2016 proposed the spectrum-based and tag-based open search methods, respectively, to identify peptides with PTMs. While the performance of these two methods is promising, the identification results vary greatly with respect to the quality of tandem mass spectra and the number of PTMs in peptides. This motivates us to systematically study the relationship between the performance of open search methods and quality parameters of tandem mass spectrum data, as well as the number of PTMs in peptides.ResultsThrough large-scale simulations, we obtain the performance trend when simulated tandem mass spectra are of different quality. We propose an analytical model to describe the relationship between the probability of obtaining correct identifications and the spectrum quality as well as the number of PTMs. Based on the analytical model, we can quantitatively describe the necessary condition to effectively apply open search methods.AvailabilitySource codes of the simulation are available at http://bioinformatics.ust.hk/[email protected] or [email protected] informationSupplementary data are available at Bioinformatics online.

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