Prediction of CpG-island function: CpG clustering vs. sliding-window methods
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AuthorHackenberg , Michael; Barturen, Guillermo; Carpena, Pedro; Luque-Escamilla, Pedro Luis; Previti, Christopher; Oliver, José Luis
AlgorithmsAlu elementsCluster analysisConserved sequenceCpG islandsDna methylationEvolutionPromoter regionsGenetics
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]
SponsorshipWe 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.
[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.