ArticleBMC bioinformatics2014
DMRforPairs: identifying differentially methylated regions between unique samples using array based methylation profiles.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evaluation of the pooled sample method in Infinium MethylationEPIC BeadChip array by comparison with individual samples.Clinical epigenetics · 2023Article
- Comparative epigenetic analysis of tumour initiating cells and syngeneic EPSC-derived neural stem cells in glioblastoma.Nature communications · 2021Article
- Gene set enrichment analysis for genome-wide DNA methylation data.Genome biology · 2021Article
- Monozygotic Twins Discordant for Immunoglobulin A Nephropathy Display Differences in DNA Methylation and Gene Expression.Kidney diseases (Basel, Switzerland) · 2021Article
- Genome wide methylation profiling of selected matched soft tissue sarcomas identifies methylation changes in metastatic and recurrent disease.Scientific reports · 2021Article
- Whole-Genome Methylation Study of Congenital Lung Malformations in Children.Frontiers in oncology · 2021Article
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- Hodgkin Lymphoma Monozygotic Triplets Reveal Divergences in DNA Methylation Signatures.Frontiers in oncology · 2020Article
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- BioMethyl: an R package for biological interpretation of DNA methylation data.Bioinformatics (Oxford, England) · 2019Article
- Genome wide DNA methylation profiles provide clues to the origin and pathogenesis of germ cell tumors.PloS one · 2015Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArray based methylation profiling is a cost-effective solution to study the association between genome methylation and human disease & development. Available tools to analyze the Illumina Infinium HumanMethylation450 BeadChip focus on comparing methylation levels per locus. Other tools combine multiple probes into a range, identifying differential methylated regions (DMRs). These tools all require groups of samples to compare. However, comparison of unique, individual samples is essential in situations where larger sample sizes are not possible.
resultsDMRforPairs was designed to compare regional methylation status between unique samples. It identifies probe dense genomic regions and quantifies/tests their (difference in) methylation level between the samples. As a proof of concept, DMRforPairs is applied to public data from four human cell lines: two lymphoblastoid cell lines from healthy individuals and the cancer cell lines A431 and MCF7 (including 2 technical replicates each). DMRforPairs identified an increasing number of DMRs related to the sample phenotype when biological similarity of the samples decreased. DMRs identified by DMRforPairs were related to the biological origin of the cell lines.
conclusionTo our knowledge, DMRforPairs is the first tool to identify and visualize relevant and significant differentially methylated regions between unique samples.
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