Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands

Wei Dai, Jens M. Teodoridis, Janet Graham, Constanze Zeller, Hui-ming Huang, Pearlly Yan, J. Keith Keith, Robert Brown, Jim Paul

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Background: Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA). Results: MLDA was programmed in R (version 2.7.0) and the package is available at CRAN 1. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/ or bisulphite pyrosequencing. Conclusion: MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays.

Original languageEnglish (US)
Article number337
JournalBMC Bioinformatics
Volume9
DOIs
StatePublished - Aug 8 2008
Externally publishedYes

Fingerprint

CpG Islands
Methylation
Discriminant Analysis
Discriminant analysis
Locus
Microarray
Microarrays
Cisplatin
Ovarian Cancer
Chemotherapy
Microarray Data
Linear Regression Model
Promoter
Normalization
Tumor
Likelihood
Maintenance
Enzymes
Genome
DNA Methylation

ASJC Scopus subject areas

  • Medicine(all)
  • Structural Biology
  • Applied Mathematics
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

Cite this

Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands. / Dai, Wei; Teodoridis, Jens M.; Graham, Janet; Zeller, Constanze; Huang, Hui-ming; Yan, Pearlly; Keith, J. Keith; Brown, Robert; Paul, Jim.

In: BMC Bioinformatics, Vol. 9, 337, 08.08.2008.

Research output: Contribution to journalArticle

Dai, Wei ; Teodoridis, Jens M. ; Graham, Janet ; Zeller, Constanze ; Huang, Hui-ming ; Yan, Pearlly ; Keith, J. Keith ; Brown, Robert ; Paul, Jim. / Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands. In: BMC Bioinformatics. 2008 ; Vol. 9.
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abstract = "Background: Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA). Results: MLDA was programmed in R (version 2.7.0) and the package is available at CRAN 1. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/ or bisulphite pyrosequencing. Conclusion: MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays.",
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