ArticleBMC bioinformatics2018
A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The applications of DNA methylation as a biomarker in kidney transplantation: a systematic review.Clinical epigenetics · 2022Pooled it
- Article
- Alzheimer's Disease DNA Methylation Index (AD-DMI) and its Association with Late-Life Cognitive Function.NPJ dementia · 2026Article
- Multi-omics driven computational framework for cancer molecular subtype classification.Scientific reports · 2025Article
- A Comprehensive Analysis of Novel Variations Associated with Bile Duct Cancer: Insights into Expression, Methylation, and 3D Protein Structure.International journal of molecular sciences · 2025Article
- Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication.International journal of molecular sciences · 2025Article
- 5G-exposed human skin cells do not respond with altered gene expression and methylation profiles.PNAS nexus · 2025Article
- Epigenetic insights into neuropsychiatric and cognitive symptoms in Parkinson's disease: A DNA co-methylation network analysis.NPJ Parkinson's disease · 2025Article
- Longitudinal DNA methylation profiles in saliva of offspring from mothers with gestational diabetes: associations with early childhood growth patterns.Cardiovascular diabetology · 2025Article
- DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches.Frontiers in bioinformatics · 2025Article
- Epigenetic profiling of preterm birth: a dual-tissue methylation patterns using long-read sequencing.Frontiers in epigenetics and epigenomics · 2025Article
- A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework.NPJ systems biology and applications · 2024Article
- Computational deconvolution of DNA methylation data from mixed DNA samples.Briefings in bioinformatics · 2024Review
- A supervised learning method for classifying methylation disorders.BMC bioinformatics · 2024Article
- A novel approach toward optimal workflow selection for DNA methylation biomarker discovery.BMC bioinformatics · 2024Article
- Differential methylation region detection via an array-adaptive normalized kernel-weighted model.PloS one · 2024Article
- Functional impact of multi-omic interactions in lung cancer.Frontiers in genetics · 2024Article
- A novel family of beta mixture models for the differential analysis of DNA methylation data: An application to prostate cancer.PloS one · 2024Article
- A comprehensive characterisation of phaeochromocytoma and paraganglioma tumours through histone protein profiling, DNA methylation and transcriptomic analysis genome wide.Clinical epigenetics · 2023Article
- Detecting aberrant DNA methylation in Illumina DNA methylation arrays: a toolbox and recommendations for its use.Epigenetics · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundDNA methylation has been identified to be widely associated to complex diseases. Among biological platforms to profile DNA methylation in human, the Illumina Infinium HumanMethylation450 BeadChip (450K) has been accepted as one of the most efficient technologies. However, challenges exist in analysis of DNA methylation data generated by this technology due to widespread biases.
resultsHere we proposed a generalized framework for evaluating data analysis methods for Illumina 450K array. This framework considers the following steps towards a successful analysis: importing data, quality control, within-array normalization, correcting type bias, detecting differentially methylated probes or regions and biological interpretation.
conclusionsWe evaluated five methods using three real datasets, and proposed outperform methods for the Illumina 450K array data analysis. Minfi and methylumi are optimal choice when analyzing small dataset. BMIQ and RCP are proper to correcting type bias and the normalized result of them can be used to discover DMPs. R package missMethyl is suitable for GO term enrichment analysis and biological interpretation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.