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A Nearly Exhaustive Search for CpG Islands on Whole Chromosomes


  • Hsieh Fushing

    (University of California, Davis)

  • Chen Shu-Chun

    (Academia Sinica)

  • Pollard Katherine

    (University of California, San Francisco)


CpG islands are genome subsequences with an unexpectedly high number of CG di-nucleotides. They are typically identified using filtering criteria (e.g., G+C% expected vs. observed CpG ratio and length) and are computed using sliding window methods. Most such studies illusively assume an exhaustive search of CpG islands are achieved on the genome sequence of interest. We devise a Lexis diagram and explicitly show that filtering criteria-based definitions of CpG islands are mathematically incomplete and non-operational. These facts imply that the sliding window methods frequently fail to identify a large percentage of subsequences that meet the filtering criteria. We also demonstrate that an exhaustive search is computationally expensive. We develop the Hierarchical Factor Segmentation (HFS) algorithm, a pattern recognition technique with an adaptive model selection device to overcome the incompleteness and non-operational drawbacks, and to achieve effective computations for identifying CpG-islands. The concept of a CpG island core" is introduced and computed using the HFS algorithm, which is independent from any specific filtering criteria. Upon such a CpG island core," a CpG-island is constructed using a Lexis diagram. This two-step computational approach provides a nearly exhaustive search for CpG islands that can be practically implemented on whole chromosomes. In a simulation study realistically mimicking CpG-island dynamics through a Hidden Markov Model we demonstrate that this approach retains very high sensitivity and specificity, that is, very low rates of false positives and false negatives. Finally, we apply the HFS algorithm to identify CpG island cores on human chromosome 21.

Suggested Citation

  • Hsieh Fushing & Chen Shu-Chun & Pollard Katherine, 2009. "A Nearly Exhaustive Search for CpG Islands on Whole Chromosomes," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-24, May.
  • Handle: RePEc:bpj:ijbist:v:5:y:2009:i:1:n:14

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    References listed on IDEAS

    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    2. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    3. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
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    Cited by:

    1. Hsieh Fushing & Shu-Chun Chen & Travis J. Berge & Oscar Jorda, 2010. "A Chronology of International Business Cycles Through Non-parametric Decoding," Working Papers 1020, University of California, Davis, Department of Economics.
    2. Singer Meromit & Engström Alexander & Schönhuth Alexander & Pachter Lior, 2011. "Determining Coding CpG Islands by Identifying Regions Significant for Pattern Statistics on Markov Chains," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-27, September.

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