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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/31193

Title: Prediction of CpG-island function: CpG clustering vs. sliding-window methods
Authors: Hackenberg, Michael
Barturen, Guillermo
Carpena, Pedro
Luque-Escamilla, Pedro Luis
Previti, Christopher
Oliver, José Luis
Issue Date: 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.
Sponsorship: We acknowledge the Spanish Government (Grant No. BIO2008-01353) and the Spanish Junta de Andalucía (Grant Nos. P06-FQM1858 and P07-FQM3163) financial support. MH acknowledges financial support from the 'Juan de la Cierva' grant from the Spanish Government. GB acknowledges financial support from the 'Programa de formación de investigadores del Departamento de Educación, Universidades e Investigación' grant from the Basque Country Government.
Publisher: Biomed Central
Keywords: Algorithms
Alu elements
Cluster analysis
Conserved sequence
CpG islands
Dna methylation
Promoter regions
URI: http://hdl.handle.net/10481/31193
ISSN: 1471-2164
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Hackenberg, M.; et al. Prediction of CpG-island function: CpG clustering vs. sliding-window methods. BMC Genomics, 11: 327 (2010). [http://hdl.handle.net/10481/31193]
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