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Open Access Research article

Prediction of CpG-island function: CpG clustering vs. sliding-window methods

Michael Hackenberg12*, Guillermo Barturen12, Pedro Carpena34, Pedro L Luque-Escamilla5, Christopher Previti6 and José L Oliver12*

Author Affiliations

1 Dpto. de Genética, Facultad de Ciencias, Universidad de Granada, Campus de Fuentenueva s/n, 18071, Granada, Spain

2 Lab. de Bioinformática, Centro de Investigación Biomédica, PTS, Avda. del Conocimiento s/n, 18100, Granada, Spain

3 Dpto. de Física Aplicada II, E.T.S.I. de Telecomunicación, Universidad de Málaga 29071-Malaga, Spain

4 Division of Sleep Medicine, Brigham and Woman's Hospital, Harvard Medical School, Boston, MA 02115, USA

5 Dpto. de Ingeniería Mecánica y Minera, EPS Jaén-Universidad de Jaén, Campus Las Lagunillas s/n A3-008, 23071-Jaén, Spain

6 Computational Biology Unit, Bergen Center for Computational Science & Sars Centre for Marine Molecular Biology, University of Bergen, Thormøhlensgt 55, 5008 Bergen, Norway

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BMC Genomics 2010, 11:327  doi:10.1186/1471-2164-11-327

Published: 26 May 2010

Abstract

Background

Unmethylated stretches of CpG dinucleotides (CpG islands) are an outstanding property of mammal genomes. Conventionally, these regions are detected by sliding window approaches using %G + C, CpG observed/expected ratio and length thresholds as main parameters. Recently, clustering methods directly detect clusters of CpG dinucleotides as a statistical property of the genome sequence.

Results

We compare sliding-window to clustering (i.e. CpGcluster) predictions by applying new ways to detect putative functionality of CpG islands. Analyzing the co-localization with several genomic regions as a function of window size vs. statistical significance (p-value), CpGcluster shows a higher overlap with promoter regions and highly conserved elements, at the same time showing less overlap with Alu retrotransposons. The major difference in the prediction was found for short islands (CpG islets), often exclusively predicted by CpGcluster. Many of these islets seem to be functional, as they are unmethylated, highly conserved and/or located within the promoter region. Finally, we show that window-based islands can spuriously overlap several, differentially regulated promoters as well as different methylation domains, which might indicate a wrong merge of several CpG islands into a single, very long island. The shorter CpGcluster islands seem to be much more specific when concerning the overlap with alternative transcription start sites or the detection of homogenous methylation domains.

Conclusions

The main difference between sliding-window approaches and clustering methods is the length of the predicted islands. Short islands, often differentially methylated, are almost exclusively predicted by CpGcluster. This suggests that CpGcluster may be the algorithm of choice to explore the function of these short, but putatively functional CpG islands.