CpGPAP: CpG island predictor analysis platform

A review of computational algorithms for CpG islands detection

CpG islands are generally known as the epigenetic regulatory regions in accordance with histone modifications, methylation, and promoter activity. There is a significant need for the exact mapping of DNA methylation in CpG islands to understand the diverse biological functions. However, the precise identification of CpG islands from the whole genome through experimental and computational approaches is still challenging. Numerous computational methods are being developed to detect the CpG-enriched regions, effectively, to reduce the time and cost of the experiments. Here, we review some of the latest computational CpG detection methods that utilize clustering, patterns and physical-distance like parameters for CpG island detection. The comparative analyses of the methods relying on different principles and parameters allow prioritizing the algorithms for specific CpG associated datasets to achieve higher accuracy and sensitivity. A number of computational tools based on the window, Hidden Markov Model, density and distance-/length-based algorithms are being applied on human or mammalian genomes for accurate CpG detection. Comparative analyses of CpG island detection algorithms facilitate to prefer the method according to the target genome and required parameters to attain higher accuracy, specificity, and performance. There is still a need for efficient computational CpG detection methods with lower false-positive results. This review provides a better understanding about the principles of tools that will assist to prioritize and develop the algorithms for accurate CpG islands detection.

Enhanced sensitivity of CpG island search and primer design based on predicted CpG island position

DNA methylation has important biological roles, such as gene expression regulation, as well as practical applications in forensics, such as in body fluid identification and age estimation. DNA methylation often occurs in the CpG site, and methylation within the CpG islands affects various cellular functions and is related to tissue-specific identification. Several programs have been developed to identify CpG islands; however, the size, location, and number of predicted CpG islands are not identical due to different search algorithms. In addition, they only provide structural information for predicted CpG islands without experimental information, such as primer design. We developed an analysis pipeline package, CpGPNP, to integrate CpG island prediction and primer design. CpGPNP predicts CpG islands more accurately and sensitively than other programs, and designs primers easily based on the predicted CpG island locations. The primer design function included standard, bisulfite, and methylation-specific PCR to identify the methylation of particular CpG sites. In this study, we performed CpG island prediction on all chromosomes and compared CpG island search performance of CpGPNP with other CpG island prediction programs. In addition, we compared the position of primers designed for a specific region within the predicted CpG island using other bisulfite PCR primer programs. The primers designed by CpGPNP were used to experimentally verify the amplification of the target region of markers for body fluid identification and age estimation. CpGPNP is freely available at http://forensicdna.kr/cpgpnp/.

GaussianCpG: a Gaussian model for detection of CpG island in human genome sequences

Background

As crucial markers in identifying biological elements and processes in mammalian genomes, CpG islands (CGI) play important roles in DNA methylation, gene regulation, epigenetic inheritance, gene mutation, chromosome inactivation and nuclesome retention. The generally accepted criteria of CGI rely on: (a) %G+C content is ≥ 50%, (b) the ratio of the observed CpG content and the expected CpG content is ≥ 0.6, and (c) the general length of CGI is greater than 200 nucleotides. Most existing computational methods for the prediction of CpG island are programmed on these rules. However, many experimentally verified CpG islands deviate from these artificial criteria. Experiments indicate that in many cases %G+C is < 50%, CpG _obs/CpG _exp varies, and the length of CGI ranges from eight nucleotides to a few thousand of nucleotides. It implies that CGI detection is not just a straightly statistical task and some unrevealed rules probably are hidden.

Results

A novel Gaussian model, GaussianCpG, is developed for detection of CpG islands on human genome. We analyze the energy distribution over genomic primary structure for each CpG site and adopt the parameters from statistics of Human genome. The evaluation results show that the new model can predict CpG islands efficiently by balancing both sensitivity and specificity over known human CGI data sets. Compared with other models, GaussianCpG can achieve better performance in CGI detection.

Conclusions

Our Gaussian model aims to simplify the complex interaction between nucleotides. The model is computed not by the linear statistical method but by the Gaussian energy distribution and accumulation. The parameters of Gaussian function are not arbitrarily designated but deliberately chosen by optimizing the biological statistics. By using the pseudopotential analysis on CpG islands, the novel model is validated on both the real and artificial data sets.