Spectrum Fusion: Using Multiple Mass Spectra for De Novo Peptide Sequencing

2009 ◽  
Vol 16 (8) ◽  
pp. 1169-1182 ◽  
Author(s):  
Ritendra Datta ◽  
Marshall Bern
Keyword(s):  
Mass Spectra ◽  
De Novo ◽  
Peptide Sequencing ◽  
De Novo Peptide Sequencing ◽  
De Novo Peptide

pNovo+: De Novo Peptide Sequencing Using Complementary HCD and ETD Tandem Mass Spectra

2012 ◽  
Vol 12 (2) ◽  
pp. 615-625 ◽  
Author(s):  
Hao Chi ◽  
Haifeng Chen ◽  
Kun He ◽  
Long Wu ◽  
Bing Yang ◽  
...  
Keyword(s):  
Mass Spectra ◽  
De Novo ◽  
Peptide Sequencing ◽  
Tandem Mass ◽  
Tandem Mass Spectra ◽  
De Novo Peptide Sequencing ◽  
De Novo Peptide

ON PREPROCESSING AND ANTISYMMETRY IN DE NOVO PEPTIDE SEQUENCING: IMPROVING EFFICIENCY AND ACCURACY

2008 ◽  
Vol 06 (03) ◽  
pp. 467-492 ◽  
Author(s):  
KANG NING ◽  
NAN YE ◽  
HON WAI LEONG
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Computational Models ◽  
De Novo ◽  
Noise Removal ◽  
Peptide Sequencing ◽  
De Novo Sequencing ◽  
Tandem Mass ◽  
De Novo Peptide Sequencing ◽  
De Novo Peptide

Peptide sequencing plays a fundamental role in proteomics. Tandem mass spectrometry, being sensitive and efficient, is one of the most commonly used techniques in peptide sequencing. Many computational models and algorithms have been developed for peptide sequencing using tandem mass spectrometry. In this paper, we investigate general issues in de novo sequencing, and present results that can be used to improve current de novo sequencing algorithms. We propose a general preprocessing scheme that performs binning, pseudo-peak introduction, and noise removal, and present theoretical and experimental analyses on each of the components. Then, we study the antisymmetry problem and current assumptions related to it, and propose a more realistic way to handle the antisymmetry problem based on analysis of some datasets. We integrate our findings on preprocessing and the antisymmetry problem with some current models for peptide sequencing. Experimental results show that our findings help to improve accuracies for de novo sequencing.

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