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CpGPAP: CpG island predictor analysis platform.

Chuang LY, Yang CH, Lin MC, Yang CH - BMC Genet. (2012)

Bottom Line: Genomic islands play an important role in medical, methylation and biological studies.CpGPAP is a web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences.These features allow the user to easily view CpG island results and download the relevant island data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan.

ABSTRACT

Background: Genomic islands play an important role in medical, methylation and biological studies. To explore the region, we propose a CpG islands prediction analysis platform for genome sequence exploration (CpGPAP).

Results: CpGPAP is a web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences. The prediction algorithms supported in CpGPAP include complementary particle swarm optimization (CPSO), a complementary genetic algorithm (CGA) and other methods (CpGPlot, CpGProD and CpGIS) found in the literature. The CpGPAP platform is easy to use and has three main features (1) selection of the prediction algorithm; (2) graphic visualization of results; and (3) application of related tools and dataset downloads. These features allow the user to easily view CpG island results and download the relevant island data. CpGPAP is freely available at http://bio.kuas.edu.tw/CpGPAP/.

Conclusions: The platform's supported algorithms (CPSO and CGA) provide a higher sensitivity and a higher correlation coefficient when compared to CpGPlot, CpGProD, CpGIS, and CpGcluster over an entire chromosome.

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CpGPAP platform flowchart. A: Selection of the optimization algorithm for predicting CpG islands. B: Parameter settings for the optimization algorithm, CpG island related parameters and input sequence
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Figure 1: CpGPAP platform flowchart. A: Selection of the optimization algorithm for predicting CpG islands. B: Parameter settings for the optimization algorithm, CpG island related parameters and input sequence

Mentions: The input module of the CpGPAP platform contains three main functions. First, the prediction algorithm, i.e., CPSO, CGA, CpGPlot, CpGProD or CpGIS is selected (Figure 1A). Then, the optimization algorithm's parameters are set, which include algorithm-related and CpG island-related parameters (CpG island length, GC content and O/E ratio). FASTA sequences with the four nucleotides adenine (A), thymine (T), cytosine (C) and guanine (G) are accepted as input sequences (Figure 1B).


CpGPAP: CpG island predictor analysis platform.

Chuang LY, Yang CH, Lin MC, Yang CH - BMC Genet. (2012)

CpGPAP platform flowchart. A: Selection of the optimization algorithm for predicting CpG islands. B: Parameter settings for the optimization algorithm, CpG island related parameters and input sequence
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3313849&req=5

Figure 1: CpGPAP platform flowchart. A: Selection of the optimization algorithm for predicting CpG islands. B: Parameter settings for the optimization algorithm, CpG island related parameters and input sequence
Mentions: The input module of the CpGPAP platform contains three main functions. First, the prediction algorithm, i.e., CPSO, CGA, CpGPlot, CpGProD or CpGIS is selected (Figure 1A). Then, the optimization algorithm's parameters are set, which include algorithm-related and CpG island-related parameters (CpG island length, GC content and O/E ratio). FASTA sequences with the four nucleotides adenine (A), thymine (T), cytosine (C) and guanine (G) are accepted as input sequences (Figure 1B).

Bottom Line: Genomic islands play an important role in medical, methylation and biological studies.CpGPAP is a web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences.These features allow the user to easily view CpG island results and download the relevant island data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Chemical Engineering, Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan.

ABSTRACT

Background: Genomic islands play an important role in medical, methylation and biological studies. To explore the region, we propose a CpG islands prediction analysis platform for genome sequence exploration (CpGPAP).

Results: CpGPAP is a web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences. The prediction algorithms supported in CpGPAP include complementary particle swarm optimization (CPSO), a complementary genetic algorithm (CGA) and other methods (CpGPlot, CpGProD and CpGIS) found in the literature. The CpGPAP platform is easy to use and has three main features (1) selection of the prediction algorithm; (2) graphic visualization of results; and (3) application of related tools and dataset downloads. These features allow the user to easily view CpG island results and download the relevant island data. CpGPAP is freely available at http://bio.kuas.edu.tw/CpGPAP/.

Conclusions: The platform's supported algorithms (CPSO and CGA) provide a higher sensitivity and a higher correlation coefficient when compared to CpGPlot, CpGProD, CpGIS, and CpGcluster over an entire chromosome.

Show MeSH