CpG-Discover: a machine learning approach for CpG islands identification from human DNA sequence

  • Authors:

  • Man Lan;Yu Xu;Lin Li;Fei Wang;Ying Zuo;Yuan Chen;Chew Lim Tan;Jian Su

  • Affiliations:

  • Department of Computer Science and Technology, East China Normal University, Shanghai, China and Institute for Infocomm Research, South Tower, Singapore;Department of Computer Science and Technology, East China Normal University;Department of Computer Science and Technology, East China Normal University;Department of Computer Science and Technology, East China Normal University;Department of Computer Science and Technology, East China Normal University;Department of Computer Science and Technology, East China Normal University;School of Computing, National University of Singapore, Singapore;Institute for Infocomm Research, South Tower, Singapore

  • Venue:
  • IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
  • Year:
  • 2009

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Abstract

CpG islands (CGIs) playa fundamental role in genome analysis as genomic markers and tumor markers. Identification of potential CGIs has contributed not only to the prediction of promoters of most house-keeping genes and many tissue-specific genes but also to the understanding of the epigenetic causes of cancer. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human intervention for search purpose. In this paper, we implement a HMM-based CGIs identification system, namely CpG-Discover. Experiments have been conducted on the EMBL human DNA database and in comparison with other widely-used tools. The controlled experimental results indicate that our system is a promising tool and has the capability of locating CGIs accurately. In addition, our system has significant differences from other tools in that it avoids the disadvantages of using sliding windows and it reduces the large amount of human intervention needed to search for or to combine potential CGIs (such as, the thresholds of initial density or distance seed). Therefore, given annotated training data set, our system has the adaptability to find other specific nucleotides sequences in DNA.