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EpiMethEx: a tool for large-scale integrated analysis in methylation hotspots linked to genetic regulation.
Candido, Saverio; Parasiliti Palumbo, Giuseppe Alessandro; Pennisi, Marzio; Russo, Giulia; Sgroi, Giuseppe; Di Salvatore, Valentina; Libra, Massimo; Pappalardo, Francesco.
Afiliación
  • Candido S; Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia, 97, Catania, 95123, Italy.
  • Parasiliti Palumbo GA; Department of Mathematics and Computer Science, University of Catania, Viale A. Doria, 6, Catania, 95125, Italy.
  • Pennisi M; Department of Mathematics and Computer Science, University of Catania, Viale A. Doria, 6, Catania, 95125, Italy.
  • Russo G; Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia, 97, Catania, 95123, Italy.
  • Sgroi G; Department of Mathematics and Computer Science, University of Catania, Viale A. Doria, 6, Catania, 95125, Italy.
  • Di Salvatore V; Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia, 97, Catania, 95123, Italy.
  • Libra M; Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia, 97, Catania, 95123, Italy.
  • Pappalardo F; Department of Drug Sciences, University of Catania, Viale A. Doria, 6, Catania, 95125, Italy. francesco.pappalardo@unict.it.
BMC Bioinformatics ; 19(Suppl 13): 385, 2019 Feb 04.
Article en En | MEDLINE | ID: mdl-30717649
BACKGROUND: DNA methylation is an epigenetic mechanism of genomic regulation involved in the maintenance of homeostatic balance. Dysregulation of DNA methylation status is one of the driver alterations occurring in neoplastic transformation and cancer progression. The identification of methylation hotspots associated to gene dysregulation may contribute to discover new prognostic and diagnostic biomarkers, as well as, new therapeutic targets. RESULTS: We present EpiMethEx (Epigenetic Methylation and Expression), a R package to perform a large-scale integrated analysis by cyclic correlation analyses between methylation and gene expression data. For each gene, samples are segmented according to the expression levels to select genes that are differentially expressed. This stratification allows to identify CG methylation probesets modulated among gene-stratified samples. Subsequently, the methylation probesets are grouped by their relative position in gene sequence to identify wide genomic methylation events statically related to genetic modulation. CONCLUSIONS: The beta-test study showed that the global methylation analysis was in agreement with scientific literature. In particular, this analysis revealed a negative association between promoter hypomethylation and overexpression in a wide number of genes. Less frequently, this overexpression was sustained by intragenic hypermethylation events.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Regulación Neoplásica de la Expresión Génica / Biología Computacional / Metilación de ADN / Epigénesis Genética Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Regulación Neoplásica de la Expresión Génica / Biología Computacional / Metilación de ADN / Epigénesis Genética Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2019 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido