Comparative (Computational) Analysis of the DNA Methylation Status of Trinucleotide Repeat Expansion Diseases
Previous studies have examined DNA methylation in different trinucleotide repeat diseases. We have combined this data and used a pattern searching algorithm to identify motifs in the DNA surrounding aberrantly methylated CpGs found in the DNA of patients with one of the three trinucleotide repeat (...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Nucleic Acids |
| Online Access: | http://dx.doi.org/10.1155/2013/689798 |
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| author | Mohammadmersad Ghorbani Simon J. E. Taylor Mark A. Pook Annette Payne |
| author_facet | Mohammadmersad Ghorbani Simon J. E. Taylor Mark A. Pook Annette Payne |
| author_sort | Mohammadmersad Ghorbani |
| collection | DOAJ |
| description | Previous studies have examined DNA methylation in different trinucleotide repeat diseases. We have combined this data and used a pattern searching algorithm to identify motifs in the DNA surrounding aberrantly methylated CpGs found in the DNA of patients with one of the three trinucleotide repeat (TNR) expansion diseases: fragile X syndrome (FRAXA), myotonic dystrophy type I (DM1), or Friedreich’s ataxia (FRDA). We examined sequences surrounding both the variably methylated (VM) CpGs, which are hypermethylated in patients compared with unaffected controls, and the nonvariably methylated CpGs which remain either always methylated (AM) or never methylated (NM) in both patients and controls. Using the J48 algorithm of WEKA analysis, we identified that two patterns are all that is necessary to classify our three regions CCGG* which is found in VM and not in AM regions and AATT* which distinguished between NM and VM + AM using proportional frequency. Furthermore, comparing our software with MEME software, we have demonstrated that our software identifies more patterns than MEME in these short DNA sequences. Thus, we present evidence that the DNA sequence surrounding CpG can influence its susceptibility to be de novo methylated in a disease state associated with a trinucleotide repeat. |
| format | Article |
| id | doaj-art-cee0d1e2c74242dc9ec553b7f17f7287 |
| institution | Kabale University |
| issn | 2090-0201 2090-021X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
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| series | Journal of Nucleic Acids |
| spelling | doaj-art-cee0d1e2c74242dc9ec553b7f17f72872025-02-03T05:46:05ZengWileyJournal of Nucleic Acids2090-02012090-021X2013-01-01201310.1155/2013/689798689798Comparative (Computational) Analysis of the DNA Methylation Status of Trinucleotide Repeat Expansion DiseasesMohammadmersad Ghorbani0Simon J. E. Taylor1Mark A. Pook2Annette Payne3Department of Information Systems and Computing, Brunel University, Uxbridge Middlesex UB8 3PH, UKDepartment of Information Systems and Computing, Brunel University, Uxbridge Middlesex UB8 3PH, UKDivision of Biosciences, School of Health Sciences & Social Care, Brunel University, Uxbridge Middlesex UB8 3PH, UKDepartment of Information Systems and Computing, Brunel University, Uxbridge Middlesex UB8 3PH, UKPrevious studies have examined DNA methylation in different trinucleotide repeat diseases. We have combined this data and used a pattern searching algorithm to identify motifs in the DNA surrounding aberrantly methylated CpGs found in the DNA of patients with one of the three trinucleotide repeat (TNR) expansion diseases: fragile X syndrome (FRAXA), myotonic dystrophy type I (DM1), or Friedreich’s ataxia (FRDA). We examined sequences surrounding both the variably methylated (VM) CpGs, which are hypermethylated in patients compared with unaffected controls, and the nonvariably methylated CpGs which remain either always methylated (AM) or never methylated (NM) in both patients and controls. Using the J48 algorithm of WEKA analysis, we identified that two patterns are all that is necessary to classify our three regions CCGG* which is found in VM and not in AM regions and AATT* which distinguished between NM and VM + AM using proportional frequency. Furthermore, comparing our software with MEME software, we have demonstrated that our software identifies more patterns than MEME in these short DNA sequences. Thus, we present evidence that the DNA sequence surrounding CpG can influence its susceptibility to be de novo methylated in a disease state associated with a trinucleotide repeat.http://dx.doi.org/10.1155/2013/689798 |
| spellingShingle | Mohammadmersad Ghorbani Simon J. E. Taylor Mark A. Pook Annette Payne Comparative (Computational) Analysis of the DNA Methylation Status of Trinucleotide Repeat Expansion Diseases Journal of Nucleic Acids |
| title | Comparative (Computational) Analysis of the DNA Methylation Status of Trinucleotide Repeat Expansion Diseases |
| title_full | Comparative (Computational) Analysis of the DNA Methylation Status of Trinucleotide Repeat Expansion Diseases |
| title_fullStr | Comparative (Computational) Analysis of the DNA Methylation Status of Trinucleotide Repeat Expansion Diseases |
| title_full_unstemmed | Comparative (Computational) Analysis of the DNA Methylation Status of Trinucleotide Repeat Expansion Diseases |
| title_short | Comparative (Computational) Analysis of the DNA Methylation Status of Trinucleotide Repeat Expansion Diseases |
| title_sort | comparative computational analysis of the dna methylation status of trinucleotide repeat expansion diseases |
| url | http://dx.doi.org/10.1155/2013/689798 |
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