A review of computational algorithms for CpG islands detection

Abstract

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.