scholarly journals On the Analysis of the Illumina 450k Array Data: Probes Ambiguously Mapped to the Human Genome

2012 ◽  
Vol 3 ◽  
Author(s):  
Xu Zhang ◽  
Wenbo Mu ◽  
Wei Zhang
Keyword(s):  
Human Genome ◽  
450K Array ◽  
Array Data ◽  
Illumina 450K ◽  
Illumina 450K Array

scholarly journals Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers

2018 ◽  
Author(s):  
Shicai Fan ◽  
Jianxiong Tang ◽  
Nan Li ◽  
Ying Zhao ◽  
Rizi Ai ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Human Genome ◽  
Therapeutic Targets ◽  
Integrative Analysis ◽  
Axonal Guidance ◽  
Methylation Data ◽  
450K Array ◽  
Diagnosis And Prognosis ◽  
Illumina 450K ◽  
Illumina 450K Array

AbstractThe integration of genomic and DNA methylation data has been demonstrated as a powerful strategy in understanding cancer mechanisms and identifying therapeutic targets. The TCGA consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450K BeadChip (Illumina 450K array) that only covers about 1.5% of CpGs in the human genome. Therefore, increasing the coverage of the DNA methylome would significantly leverage the usage of the TCGA data. Here, we present a new model called EAGLING that can expand the Illumina 450K array data 18 times to cover about 30% of the CpGs in the human genome. We applied it to analyze 13 cancers in TCGA. By integrating the expanded methylation, gene expression and somatic mutation data, we identified the genes showing differential patterns in each of the 13 cancers. Many of the triple-evidenced genes identified in the majority of the cancers are biomarkers or potential biomarkers. Pan-cancer analysis also revealed the pathways in which the triple-evidenced genes are enriched, which include well known ones as well as new ones such as axonal guidance signaling pathway and pathways related to inflammatory processing or inflammation response. Triple-evidenced genes, particularly TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, TRIM59, showed superior predictive power in both tumor diagnosis and prognosis. These results have demonstrated that the integrative analysis using the expanded methylation data is powerful in identifying critical genes/pathways that may serve as new therapeutic targets.

scholarly journals Apolipoprotein E DNA methylation and late-life disease

2018 ◽  
Vol 47 (3) ◽  
pp. 899-907 ◽  
Author(s):  
Ida K Karlsson ◽  
Alexander Ploner ◽  
Yunzhang Wang ◽  
Margaret Gatz ◽  
Nancy L Pedersen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Apolipoprotein E ◽  
Promoter Region ◽  
Allelic Variation ◽  
450K Array ◽  
Blood Draw ◽  
Standard Deviation Increase ◽  
Illumina 450K ◽  
Apoe Locus ◽  
Illumina 450K Array

Abstract Background This study aims to investigate if DNA methylation of the apolipoprotein E (APOE) locus affects the risks of dementia, Alzheimeŕs disease (AD) or cardiovascular disease (CVD). Methods DNA methylation across theAPOE gene has previously been categorized into three distinct regions: a hypermethylated region in the promoter, a hypomethylated region in the first two introns and exons and a hypermethylated region in the 3′exon that also harbours theAPOE ε2 and ε4 alleles. DNA methylation levels in leukocytes were measured using the Illumina 450K array in 447 Swedish twins (mean age 78.1 years). We used logistic regression to investigate whether methylation levels in those regions affect the odds of disease. Results We found that methylation levels in the promoter region were associated with dementia and AD after adjusting for sex, age at blood draw, education, smoking and relatedness among twins [odds ratio (OR) 1.32 per standard deviation increase in methylation levels, 95% confidence interval (CI) 1.08–1.62 for dementia; OR 1.38, 95% CI 1.07–1.78 for AD). We did not detect any difference in methylation levels between CVD cases and controls. Results were similar when comparing within discordant twin pairs, and did not differ as a function ofAPOE genotype. Conclusions We found that higher DNA methylation levels in the promoter region ofAPOE increase the odds of dementia and AD, but not CVD. The effect was independent ofAPOE genotype, indicating that allelic variation and methylation variation inAPOE may act independently to increase the risk of dementia.

scholarly journals Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array

2013 ◽  
Vol 6 (1) ◽  
pp. 26 ◽  
Author(s):  
Roderick C Slieker ◽  
Steffan D Bos ◽  
Jelle J Goeman ◽  
Judith VMG Bovée ◽  
Rudolf P Talens ◽  
...  
Keyword(s):  
Differentially Methylated Regions ◽  
450K Array ◽  
Tissue Specific ◽  
Illumina 450K ◽  
Illumina 450K Array

scholarly journals Agreement in DNA methylation levels from the Illumina 450K array across batches, tissues, and time

Epigenetics ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. 19-32 ◽  
Author(s):  
Marie Forest ◽  
Kieran J. O'Donnell ◽  
Greg Voisin ◽  
Helene Gaudreau ◽  
Julia L. MacIsaac ◽  
...  
Keyword(s):  
Dna Methylation ◽  
450K Array ◽  
Illumina 450K ◽  
Illumina 450K Array

scholarly journals A data-driven approach to preprocessing Illumina 450K methylation array data

BMC Genomics ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 293 ◽  
Author(s):  
Ruth Pidsley ◽  
Chloe C Y Wong ◽  
Manuela Volta ◽  
Katie Lunnon ◽  
Jonathan Mill ◽  
...  
Keyword(s):  
Data Driven ◽  
Methylation Array ◽  
Array Data ◽  
Illumina 450K ◽  
Data Driven Approach ◽  
450K Methylation

scholarly journals A cross-package Bioconductor workflow for analysing methylation array data

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1281 ◽  
Author(s):  
Jovana Maksimovic ◽  
Belinda Phipson ◽  
Alicia Oshlack
Keyword(s):  
Human Genome ◽  
Differential Methylation ◽  
Statistical Testing ◽  
Analysis Pipeline ◽  
Methylation Array ◽  
Array Data ◽  
Cell Type Composition ◽  
Type Composition ◽  
Differential Variability ◽  
Differential Methylation Analysis

Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

scholarly journals IGHV-associated methylation signatures more accurately predict clinical outcomes of chronic lymphocytic leukemia patients than IGHV mutation load

Haematologica ◽  
2021 ◽  
pp. 0-0
Author(s):  
Dianna Hussmann ◽  
Anna Starnawska ◽  
Louise Kristensen ◽  
Iben Daugaard ◽  
Astrid Thomsen ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Clinical Outcomes ◽  
Lymphocytic Leukemia ◽  
Mutation Load ◽  
450K Array ◽  
Mutation Status ◽  
Classification Procedure ◽  
Array Data ◽  
Ighv Gene ◽  
Classification Procedures

Currently, no molecular biomarker indexes are used in standard care to make treatment decisions at diagnosis of chronic lymphocytic leukemia (CLL). We used Infinium MethylationEPIC array data from diagnostic blood samples of 114 CLL patients, and developed a patient stratification procedure based on methylation signatures associated with mutation load of the IGHV gene. This procedure allowed us to predict the time to treatment (TTT) with HR 8.34 (95% CI, 4.54-15.30), as opposed to HR 4.35 (95% CI, 2.60-7.28) for IGHV mutation status. Detailed evaluation of 17 discrepant cases between the two classification procedures showed that these cases were incorrectly classified using IGHV status. Moreover, methylation-based classification stratified patients with different overall survival (OS) (HR, 1.82; 95% CI, 1.07-3.09), which was not possible using IGHV status. Furthermore, we assessed the performance of the developed classification procedure using published HumanMethylation450 array data for 159 patients for which TTT, OS and relapse were available. Despite that 450K array methylation data did not contain all biomarkers used in our classification procedure, methylation signatures again stratified patients with significantly better accuracy than IGHV mutation load regarding all available clinical outcomes. Thus, stratification using IGHV-associated methylation signatures may provide improved prognostic power than IGHV mutation status.

scholarly journals A cross-package Bioconductor workflow for analysing methylation array data

2016 ◽  
Author(s):  
Jovana Maksimovic ◽  
Belinda Phipson ◽  
Alicia Oshlack
Keyword(s):  
Human Genome ◽  
Differential Methylation ◽  
Statistical Testing ◽  
Analysis Pipeline ◽  
Methylation Array ◽  
Array Data ◽  
Cell Type Composition ◽  
Type Composition ◽  
Differential Variability ◽  
Differential Methylation Analysis

AbstractMethylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

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