Functional classification of proteins using mass spectrometry data and exploration of their frequency of identification in proteomic analysis

2009 ◽  
Author(s):  
Panagiotis Bougioukos
Keyword(s):  
Mass Spectrometry ◽  
Proteomic Analysis ◽  
Mass Spectrometry Data ◽  
Functional Classification

scholarly journals Functional Proteomic Analysis of Human Nucleolus

2002 ◽  
Vol 13 (11) ◽  
pp. 4100-4109 ◽  
Author(s):  
Alexander Scherl ◽  
Yohann Couté ◽  
Catherine Déon ◽  
Aleth Callé ◽  
Karine Kindbeiter ◽  
...  
Keyword(s):  
Proteomic Analysis ◽  
Ribosome Biogenesis ◽  
Molecular Mechanisms ◽  
Convincing Evidence ◽  
Biological Processes ◽  
Functional Classification ◽  
Control Of Gene Expression ◽  
Nucleolar Proteins ◽  
Nuclear Domain

The notion of a “plurifunctional” nucleolus is now well established. However, molecular mechanisms underlying the biological processes occurring within this nuclear domain remain only partially understood. As a first step in elucidating these mechanisms we have carried out a proteomic analysis to draw up a list of proteins present within nucleoli of HeLa cells. This analysis allowed the identification of 213 different nucleolar proteins. This catalog complements that of the 271 proteins obtained recently by others, giving a total of ∼350 different nucleolar proteins. Functional classification of these proteins allowed outlining several biological processes taking place within nucleoli. Bioinformatic analyses permitted the assignment of hypothetical functions for 43 proteins for which no functional information is available. Notably, a role in ribosome biogenesis was proposed for 31 proteins. More generally, this functional classification reinforces the plurifunctional nature of nucleoli and provides convincing evidence that nucleoli may play a central role in the control of gene expression. Finally, this analysis supports the recent demonstration of a coupling of transcription and translation in higher eukaryotes.

scholarly journals Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis

2010 ◽  
Vol 28 (1) ◽  
pp. 83-89 ◽  
Author(s):  
Noelle M Griffin ◽  
Jingyi Yu ◽  
Fred Long ◽  
Phil Oh ◽  
Sabrina Shore ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Proteomic Analysis ◽  
Mass Spectrometry Data ◽  
Label Free ◽  
Complex Mass

A clustering based hybrid system for biomarker selection and sample classification of mass spectrometry data

2010 ◽  
Vol 73 (13-15) ◽  
pp. 2317-2331 ◽  
Author(s):  
Pengyi Yang ◽  
Zili Zhang ◽  
Bing B. Zhou ◽  
Albert Y. Zomaya
Keyword(s):  
Mass Spectrometry ◽  
Hybrid System ◽  
Mass Spectrometry Data ◽  
Sample Classification ◽  
Biomarker Selection

Feature selection and classification of noisy proteomics mass spectrometry data based on one-bit perturbed compressed sensing

2020 ◽  
Vol 36 (16) ◽  
pp. 4423-4431
Author(s):  
Wenbo Xu ◽  
Yan Tian ◽  
Siye Wang ◽  
Yupeng Cui
Keyword(s):  
Mass Spectrometry ◽  
Feature Selection ◽  
Compressed Sensing ◽  
Extreme Case ◽  
Classification Performance ◽  
Mass Spectrometry Data ◽  
Practical Significance ◽  
Supplementary Information ◽  
Prohibitive Cost

Abstract Motivation The classification of high-throughput protein data based on mass spectrometry (MS) is of great practical significance in medical diagnosis. Generally, MS data are characterized by high dimension, which inevitably leads to prohibitive cost of computation. To solve this problem, one-bit compressed sensing (CS), which is an extreme case of quantized CS, has been employed on MS data to select important features with low dimension. Though enjoying remarkably reduction of computation complexity, the current one-bit CS method does not consider the unavoidable noise contained in MS dataset, and does not exploit the inherent structure of the underlying MS data. Results We propose two feature selection (FS) methods based on one-bit CS to deal with the noise and the underlying block-sparsity features, respectively. In the first method, the FS problem is modeled as a perturbed one-bit CS problem, where the perturbation represents the noise in MS data. By iterating between perturbation refinement and FS, this method selects the significant features from noisy data. The second method formulates the problem as a perturbed one-bit block CS problem and selects the features block by block. Such block extraction is due to the fact that the significant features in the first method usually cluster in groups. Experiments show that, the two proposed methods have better classification performance for real MS data when compared with the existing method, and the second one outperforms the first one. Availability and implementation The source code of our methods is available at: https://github.com/tianyan8023/OBCS. Supplementary information Supplementary data are available at Bioinformatics online.

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