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Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis

BACKGROUND: The need for efficient algorithms to uncover biologically relevant phosphorylation motifs has become very important with rapid expansion of the proteomic sequence database along with a plethora of new information on phosphorylation sites. Here we present a novel unsupervised method, call...

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Autores principales: Chen, Yi-Cheng, Aguan, Kripamoy, Yang, Chu-Wen, Wang, Yao-Tsung, Pal, Nikhil R., Chung, I-Fang
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3102080/
https://www.ncbi.nlm.nih.gov/pubmed/21647451
http://dx.doi.org/10.1371/journal.pone.0020025
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author Chen, Yi-Cheng
Aguan, Kripamoy
Yang, Chu-Wen
Wang, Yao-Tsung
Pal, Nikhil R.
Chung, I-Fang
author_facet Chen, Yi-Cheng
Aguan, Kripamoy
Yang, Chu-Wen
Wang, Yao-Tsung
Pal, Nikhil R.
Chung, I-Fang
author_sort Chen, Yi-Cheng
collection PubMed
description BACKGROUND: The need for efficient algorithms to uncover biologically relevant phosphorylation motifs has become very important with rapid expansion of the proteomic sequence database along with a plethora of new information on phosphorylation sites. Here we present a novel unsupervised method, called Motif Finder (in short, F-Motif) for identification of phosphorylation motifs. F-Motif uses clustering of sequence information represented by numerical features that exploit the statistical information hidden in some foreground data. Furthermore, these identified motifs are then filtered to find “actual” motifs with statistically significant motif scores. RESULTS AND DISCUSSION: We have applied F-Motif to several new and existing data sets and compared its performance with two well known state-of-the-art methods. In almost all cases F-Motif could identify all statistically significant motifs extracted by the state-of-the-art methods. More importantly, in addition to this, F-Motif uncovers several novel motifs. We have demonstrated using clues from the literature that most of these new motifs discovered by F-Motif are indeed novel. We have also found some interesting phenomena. For example, for CK2 kinase, the conserved sites appear only on the right side of S. However, for CDK kinase, the adjacent site on the right of S is conserved with residue P. In addition, three different encoding methods, including a novel position contrast matrix (PCM) and the simplest binary coding, are used and the ability of F-motif to discover motifs remains quite robust with respect to encoding schemes. CONCLUSIONS: An iterative algorithm proposed here uses exploratory data analysis to discover motifs from phosphorylated data. The effectiveness of F-Motif has been demonstrated using several real data sets as well as using a synthetic data set. The method is quite general in nature and can be used to find other types of motifs also. We have also provided a server for F-Motif at http://f-motif.classcloud.org/, http://bio.classcloud.org/f-motif/ or http://ymu.classcloud.org/f-motif/.
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spelling pubmed-31020802011-06-06 Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis Chen, Yi-Cheng Aguan, Kripamoy Yang, Chu-Wen Wang, Yao-Tsung Pal, Nikhil R. Chung, I-Fang PLoS One Research Article BACKGROUND: The need for efficient algorithms to uncover biologically relevant phosphorylation motifs has become very important with rapid expansion of the proteomic sequence database along with a plethora of new information on phosphorylation sites. Here we present a novel unsupervised method, called Motif Finder (in short, F-Motif) for identification of phosphorylation motifs. F-Motif uses clustering of sequence information represented by numerical features that exploit the statistical information hidden in some foreground data. Furthermore, these identified motifs are then filtered to find “actual” motifs with statistically significant motif scores. RESULTS AND DISCUSSION: We have applied F-Motif to several new and existing data sets and compared its performance with two well known state-of-the-art methods. In almost all cases F-Motif could identify all statistically significant motifs extracted by the state-of-the-art methods. More importantly, in addition to this, F-Motif uncovers several novel motifs. We have demonstrated using clues from the literature that most of these new motifs discovered by F-Motif are indeed novel. We have also found some interesting phenomena. For example, for CK2 kinase, the conserved sites appear only on the right side of S. However, for CDK kinase, the adjacent site on the right of S is conserved with residue P. In addition, three different encoding methods, including a novel position contrast matrix (PCM) and the simplest binary coding, are used and the ability of F-motif to discover motifs remains quite robust with respect to encoding schemes. CONCLUSIONS: An iterative algorithm proposed here uses exploratory data analysis to discover motifs from phosphorylated data. The effectiveness of F-Motif has been demonstrated using several real data sets as well as using a synthetic data set. The method is quite general in nature and can be used to find other types of motifs also. We have also provided a server for F-Motif at http://f-motif.classcloud.org/, http://bio.classcloud.org/f-motif/ or http://ymu.classcloud.org/f-motif/. Public Library of Science 2011-05-25 /pmc/articles/PMC3102080/ /pubmed/21647451 http://dx.doi.org/10.1371/journal.pone.0020025 Text en Chen et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Chen, Yi-Cheng
Aguan, Kripamoy
Yang, Chu-Wen
Wang, Yao-Tsung
Pal, Nikhil R.
Chung, I-Fang
Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis
title Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis
title_full Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis
title_fullStr Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis
title_full_unstemmed Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis
title_short Discovery of Protein Phosphorylation Motifs through Exploratory Data Analysis
title_sort discovery of protein phosphorylation motifs through exploratory data analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3102080/
https://www.ncbi.nlm.nih.gov/pubmed/21647451
http://dx.doi.org/10.1371/journal.pone.0020025
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