scholarly journals Peak Identification and Quantification by Proteomic Mass Spectrogram Decomposition

2021 ◽  
Author(s):  
Pasrawin Taechawattananant ◽  
Kazuyoshi Yoshii ◽  
Yasushi Ishihama
Keyword(s):  
Peak Identification ◽  
Mass Spectrogram ◽  
Identification And Quantification

scholarly journals Peak identification and quantification by proteomic mass spectrogram decomposition

2020 ◽  
Author(s):  
Pasrawin Taechawattananant ◽  
Kazuyoshi Yoshii ◽  
Yasushi Ishihama
Keyword(s):  
Mass Spectra ◽  
Protein Identification ◽  
Proteome Analysis ◽  
Matrix Decomposition ◽  
Peptide Ions ◽  
Group Sparsity ◽  
Mass Spectrogram ◽  
Joint Identification ◽  
Increasing Demand ◽  
Identification And Quantification

AbstractRecent advances in liquid chromatography/mass spectrometry (LC/MS) technology have notably improved the sensitivity, resolution, and speed of proteome analysis, resulting in increasing demand for more sophisticated algorithms to interpret complex mass spectrograms. Here, we propose a novel statistical method that we call proteomic mass spectrogram decomposition (ProtMSD) for joint identification and quantification of peptides and proteins. Given the proteomic mass spectrogram and the reference mass spectra of all possible peptide ions associated with proteins as a dictionary, our method directly estimates the temporal intensity curves of those peptide ions, i.e., the chromatograms, under a group sparsity constraint without using the conventional careful pre-processing (e.g., thresholding and peak picking). We show that the accuracy of protein identification was significantly improved by using the protein-peptide hierarchical relationships, the isotopic distribution profiles and predicted retention times of peptide ions and the pre-learned mass spectra of noise. In the analysis of E. coli cell lysate, our ProtMSD showed excellent agreement (3277 peptide ions (94.79%) and 493 proteins (98.21%)) with the conventional cascading approach to identification and quantification based on Mascot and Skyline. This is the first attempt to use a matrix decomposition technique as a tool for LC/MS-based joint proteome identification and quantification.

Nonparametric Models for Proteomic Peak Identification and Quantification

2009 ◽  
pp. 293-308 ◽  
Author(s):  
Merlise A. Clyde ◽  
Leanna L. House ◽  
Robert L. Wolpert ◽  
Marina Vannucci
Keyword(s):  
Nonparametric Models ◽  
Peak Identification ◽  
Identification And Quantification

Development of a Rapid Survey Method of Sampling and Analysis for Asbestos in Ambient Air

1972 ◽  
Vol 30 ◽  
pp. 630-631 ◽  
Author(s):  
R. E. Heffelfinger ◽  
C. W. Melton ◽  
D. L. Kiefer ◽  
W. M. Henry ◽  
R. J. Thompson
Keyword(s):  
Neutron Activation ◽  
Ambient Air ◽  
Survey Method ◽  
Potential Hazard ◽  
Transmission Microscopy ◽  
Comparative Analyses ◽  
Asbestos Fibers ◽  
Controlled Conditions ◽  
Unequivocal Identification ◽  
Identification And Quantification

A methodology has been developed and demonstrated which is capable of determining total amounts of asbestos fibers and fibrils in air ranging from as low as fractional nanograms per cubic meter (ng/m3) of air to several micrograms/m3. The method involves the collection of samples on an absolute filter and provides an unequivocal identification and quantification of the total asbestos contents including fibrils in the collected samples.The developed method depends on the trituration under controlled conditions to reduce the fibers to fibrils, separation of the asbestos fibrils from other collected air particulates (beneficiation), and the use of transmission microscopy for identification and quantification. Its validity has been tested by comparative analyses by neutron activation techniques. It can supply the data needed to set emissions criteria and to serve as a basis for assessing the potential hazard for asbestos pollution to the populace.

HPLC-DAD/ESI-MS identification and quantification of polar constituents in six Stachys taxa from Balkan peninsula

Planta Medica ◽  
2011 ◽  
Vol 77 (12) ◽  
Author(s):  
A Karioti ◽  
J Kukic Markovic ◽  
S Petrovic ◽  
M Niketic ◽  
A Bilia
Keyword(s):  
Balkan Peninsula ◽  
Esi Ms ◽  
Identification And Quantification

Identification And Quantification Of Chemical Constituents From Indian Bee Propolis Extracts Using HPTLC And UHPLC-DAD-MS

Planta Medica ◽  
2016 ◽  
Vol 82 (05) ◽  
Author(s):  
S Sagi ◽  
B Avula ◽  
MH Masoodi ◽  
A Farooq Wali ◽  
IA Khan
Keyword(s):  
Chemical Constituents ◽  
Identification And Quantification
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