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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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Visualization of the CpG island prediction results. A: GC% chart shows the GC content distribution in the input sequence. B: O/E ratio chart shows the O/E ratio distribution in the input sequence. C: TSS chart shows the probability of the predicted CpG island overlapping with a transcription start site. D: CpG chart shows the CpG nucleic and CpG island distribution
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Figure 3: Visualization of the CpG island prediction results. A: GC% chart shows the GC content distribution in the input sequence. B: O/E ratio chart shows the O/E ratio distribution in the input sequence. C: TSS chart shows the probability of the predicted CpG island overlapping with a transcription start site. D: CpG chart shows the CpG nucleic and CpG island distribution

Mentions: To clearly determine the distribution of CpG islands, the CpGPAP platform generates a graphic visualization once the CpG islands prediction results are complete. The design of the chart is mainly based on the GGF criteria; we thus focused on GC content (GC%), O/E ratio and CpG island length design. The prediction results can be divided into four main types of CpG island-related information. (1) GC% charts are calculated from the input sequence with a calculation processed every 50 bp on average; (2) O/E ratio charts are calculated through the same process as GC%; (3) the predicted probability of being over the transcription start site chart is obtained by providing the CpGProD (http://pbil.univ-lyon1.fr/software/cpgprod.html); and (4) the distribution of CpG charts shows the predicted CpG islands resulting in the predicted genome sequence position, including the CpG island overlap input sequence position, the number of CpG islands and all connections to the CpG nucleic position. All of the above results are shown in Figure 3. Theoretically, in the large-scale computational analysis of CpG islands, the CpGPAP platform can accept any sequence input and dataset size. However, to avoid data transfer errors, we limited the "show charts" function to display graphic sequence information of 50 kb or less. The graphic visualization allows researchers to set related parameters accurately and obtain better prediction results. A stand-alone version is also available for download with no input sequence size limitation.


CpGPAP: CpG island predictor analysis platform.

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

Visualization of the CpG island prediction results. A: GC% chart shows the GC content distribution in the input sequence. B: O/E ratio chart shows the O/E ratio distribution in the input sequence. C: TSS chart shows the probability of the predicted CpG island overlapping with a transcription start site. D: CpG chart shows the CpG nucleic and CpG island distribution
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Visualization of the CpG island prediction results. A: GC% chart shows the GC content distribution in the input sequence. B: O/E ratio chart shows the O/E ratio distribution in the input sequence. C: TSS chart shows the probability of the predicted CpG island overlapping with a transcription start site. D: CpG chart shows the CpG nucleic and CpG island distribution
Mentions: To clearly determine the distribution of CpG islands, the CpGPAP platform generates a graphic visualization once the CpG islands prediction results are complete. The design of the chart is mainly based on the GGF criteria; we thus focused on GC content (GC%), O/E ratio and CpG island length design. The prediction results can be divided into four main types of CpG island-related information. (1) GC% charts are calculated from the input sequence with a calculation processed every 50 bp on average; (2) O/E ratio charts are calculated through the same process as GC%; (3) the predicted probability of being over the transcription start site chart is obtained by providing the CpGProD (http://pbil.univ-lyon1.fr/software/cpgprod.html); and (4) the distribution of CpG charts shows the predicted CpG islands resulting in the predicted genome sequence position, including the CpG island overlap input sequence position, the number of CpG islands and all connections to the CpG nucleic position. All of the above results are shown in Figure 3. Theoretically, in the large-scale computational analysis of CpG islands, the CpGPAP platform can accept any sequence input and dataset size. However, to avoid data transfer errors, we limited the "show charts" function to display graphic sequence information of 50 kb or less. The graphic visualization allows researchers to set related parameters accurately and obtain better prediction results. A stand-alone version is also available for download with no input sequence size limitation.

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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