missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform

2015 ◽  
Vol 32 (2) ◽  
pp. 286-288 ◽  
Author(s):  
Belinda Phipson ◽  
Jovana Maksimovic ◽  
Alicia Oshlack
Keyword(s):  
Dna Methylation ◽  
Cost Effective ◽  
R Package ◽  
Supplementary Information ◽  
450K Array ◽  
Illumina Humanmethylation450 ◽  
Bioconductor Project ◽  
Illumina Humanmethylation450 Beadchip ◽  
Differential Variability ◽  
Differential Methylation Analysis

Abstract Summary: DNA methylation is one of the most commonly studied epigenetic modifications due to its role in both disease and development. The Illumina HumanMethylation450 BeadChip is a cost-effective way to profile >450 000 CpGs across the human genome, making it a popular platform for profiling DNA methylation. Here we introduce missMethyl, an R package with a suite of tools for performing normalization, removal of unwanted variation in differential methylation analysis, differential variability testing and gene set analysis for the 450K array. Availability and implementation: missMethyl is an R package available from the Bioconductor project at www.bioconductor.org. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals DiffVar: A new method for detecting differential variability with application to methylation in cancer and aging

2014 ◽  
Author(s):  
Belinda Phipson ◽  
Alicia Oshlack
Keyword(s):  
Empirical Bayes ◽  
Cost Effective ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Model Framework ◽  
Methylation Of Dna ◽  
Illumina Humanmethylation450 ◽  
Illumina Humanmethylation450 Beadchip ◽  
Cancer Genome Atlas ◽  
Differential Variability

Methylation of DNA is known to be essential to development and dramatically altered in cancers. The Illumina HumanMethylation450 BeadChip has been used extensively as a cost-effective way to profile nearly half a million CpG sites across the human genome. Here we present DiffVar, a novel method to test for differential variability between sample groups. DiffVar employs an empirical Bayes model framework that can take into account any experimental design and is robust to outliers. We applied DiffVar to several datasets from The Cancer Genome Atlas, as well as an aging dataset. DiffVar is available in the missMethyl Bioconductor R package.

scholarly journals An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform

Epigenetics ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. 333-346 ◽  
Author(s):  
Francesco Marabita ◽  
Malin Almgren ◽  
Maléne E. Lindholm ◽  
Sabrina Ruhrmann ◽  
Fredrik Fagerström-Billai ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Illumina Humanmethylation450 ◽  
Dna Methylation Profiling ◽  
Illumina Humanmethylation450 Beadchip ◽  
Methylation Profiling

scholarly journals Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229763 ◽  
Author(s):  
Claudia Sala ◽  
Pietro Di Lena ◽  
Danielle Fernandes Durso ◽  
Andrea Prodi ◽  
Gastone Castellani ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Meta Analysis ◽  
Methylation Data ◽  
Illumina Humanmethylation450 ◽  
Illumina Humanmethylation450 Beadchip

scholarly journals Longitudinal DNA methylation dynamics as a practical indicator in clinical epigenetics

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shohei Komaki ◽  
Hideki Ohmomo ◽  
Tsuyoshi Hachiya ◽  
Yoichi Sutoh ◽  
Kanako Ono ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Methylation Status ◽  
Time Period ◽  
Illumina Humanmethylation450 ◽  
Illumina Humanmethylation450 Beadchip ◽  
Underlying Mechanisms ◽  
Clinical Conditions ◽  
Changes Over Time ◽  
Probe Set ◽  
Over Time

Abstract Background One of the fundamental assumptions of DNA methylation in clinical epigenetics is that DNA methylation status can change over time with or without interplay with environmental and clinical conditions. However, little is known about how DNA methylation status changes over time under ordinary environmental and clinical conditions. In this study, we revisited the high frequency longitudinal DNA methylation data of two Japanese males (24 time-points within three months) and characterized the longitudinal dynamics. Results The results showed that the majority of CpGs on Illumina HumanMethylation450 BeadChip probe set were longitudinally stable over the time period of three months. Focusing on dynamic and stable CpGs extracted from datasets, dynamic CpGs were more likely to be reported as epigenome-wide association study (EWAS) markers of various traits, especially those of immune- and inflammatory-related traits; meanwhile, the stable CpGs were enriched in metabolism-related genes and were less likely to be EWAS markers, indicating that the stable CpGs are stable both in the short-term within individuals and under various environmental and clinical conditions. Conclusions This study indicates that CpGs with different stabilities are involved in different functions and traits, and thus, they are potential indicators that can be applied for clinical epigenetic studies to outline underlying mechanisms.

scholarly journals scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

2020 ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Large Scale ◽  
Regulation Of Gene Expression ◽  
R Package ◽  
Genomic Features ◽  
Distinct Cell ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
Differential Variability

AbstractHigh throughput measurements of DNA methylomes at single-cell resolution are a promising resource to quantify the heterogeneity of DNA methylation and uncover its role in gene regulation. However, limitations of the technology result in sparse CpG coverage, effectively posing challenges to robustly quantify genuine DNA methylation heterogeneity. Here we tackle these issues by introducing scMET, a hierarchical Bayesian model which overcomes data sparsity by sharing information across cells and genomic features, resulting in a robust and biologically interpretable quantification of variability. scMET can be used to both identify highly variable features that drive epigenetic heterogeneity and perform differential methylation and differential variability analysis between pre-specified groups of cells. We demonstrate scMET’s effectiveness on some recent large scale single cell methylation datasets, showing that the scMET feature selection approach facilitates the characterisation of epigenetically distinct cell populations. Moreover, we illustrate how scMET variability estimates enable the formulation of novel biological hypotheses on the epigenetic regulation of gene expression in early development. An R package implementation of scMET is publicly available at https://github.com/andreaskapou/scMET.

scholarly journals Association between DNA Methylation Levels in Brain Tissue and Late-Life Depression in Community-Based Participants

2020 ◽  
Author(s):  
Anke Hüls ◽  
Chloe Robins ◽  
Karen N. Conneely ◽  
Philip L. De Jager ◽  
David A. Bennett ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Brain Tissue ◽  
Molecular Mechanisms ◽  
Late Life ◽  
P Value ◽  
Molecular Heterogeneity ◽  
Late Life Depression ◽  
Community Based ◽  
Illumina Humanmethylation450 ◽  
Illumina Humanmethylation450 Beadchip

AbstractObjectiveMajor depressive disorder (MDD) arises from a combination of genetic and environmental risk factors and DNA methylation is one of the molecular mechanisms through which these factors can manifest. However, little is known about the epigenetic signature of MDD in brain tissue. This study aimed to investigate associations between brain tissue-based DNA methylation and late-life MDD.MethodsWe performed a brain epigenome-wide association study (EWAS) of late-life MDD in 608 participants from the Religious Order Study and the Rush Memory and Aging Project (ROS/MAP) using DNA methylation profiles of the dorsal lateral prefrontal cortex (dPFC) generated using the Illumina HumanMethylation450 Beadchip array. We also conducted an EWAS of MDD in each sex separately.ResultsWe found epigenome-wide significant associations between brain-tissue-based DNA methylation and late-life MDD. The most significant and robust association was found with altered methylation levels in the YOD1 locus (cg25594636, p-value=2.55 × 10−11; cg03899372, p-value=3.12 × 10−09; cg12796440, p-value=1.51 × 10−08, cg23982678, p-value=7.94 × 10−08). Analysis of differentially methylated regions (DMR, p-value=5.06 × 10−10) further confirmed this locus. Other significant loci include UGT8 (cg18921206, p-value=1.75 × 10−08), FNDC3B (cg20367479, p-value=4.97 × 10−08) and SLIT2 (cg10946669, p-value=8.01 × 10−08). Notably, brain-tissue based methylation levels were strongly associated with late-life MDD in men more than in women.ConclusionsWe identified altered methylation in the YOD1, UGT8, FNDC3B and SLIT2 loci as new epigenetic factors associated with late-life MDD. Furthermore, our study highlights the sex-specific molecular heterogeneity of MDD.

scholarly journals NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data

2021 ◽  
Author(s):  
Federico Agostinis ◽  
Chiara Romualdi ◽  
Gabriele Sales ◽  
Davide Risso
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
R Package ◽  
Batch Effect ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Sequencing Data ◽  
Bioconductor Project ◽  
Single Cell Rna Sequencing

Summary: We present NewWave, a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA sequencing data. To achieve scalability, NewWave uses mini-batch optimization and can work with out-of-memory data, enabling users to analyze datasets with millions of cells. Availability and implementation: NewWave is implemented as an open-source R package available through the Bioconductor project at https://bioconductor.org/packages/NewWave/ Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals InterMineR: an R package for InterMine databases

2019 ◽  
Vol 35 (17) ◽  
pp. 3206-3207 ◽  
Author(s):  
Konstantinos A Kyritsis ◽  
Bing Wang ◽  
Julie Sullivan ◽  
Rachel Lyne ◽  
Gos Micklem
Keyword(s):  
Data Retrieval ◽  
R Package ◽  
Supplementary Information ◽  
Programming Environment ◽  
Biological Databases ◽  
Statistical Tools ◽  
Bioconductor Project ◽  
Term Enrichment ◽  
R Programming ◽  
Downstream Analysis

Abstract Summary InterMineR is a package designed to provide a flexible interface between the R programming environment and biological databases built using the InterMine platform. The package offers access to the flexible query builder and the library of term enrichment tools of the InterMine framework, as well as interoperability with other Bioconductor packages. This facilitates automation of data retrieval tasks as well as downstream analysis with existing statistical tools in the R environment. Availability and implementation InterMineR is free and open source, released under the LGPL licence and available from the Bioconductor project and Github (https://bioconductor.org/packages/release/bioc/html/InterMineR.html, https://github.com/intermine/interMineR). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals coMethDMR: accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies with continuous phenotypes

2019 ◽  
Vol 47 (17) ◽  
pp. e98-e98 ◽  
Author(s):  
Lissette Gomez ◽  
Gabriel J Odom ◽  
Juan I Young ◽  
Eden R Martin ◽  
Lizhong Liu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Type I Error ◽  
Association Studies ◽  
Cost Effective ◽  
R Package ◽  
Genomic Region ◽  
Type I ◽  
Differentially Methylated Regions ◽  
Accurate Identification ◽  
Significance Threshold

Abstract Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

scholarly journals coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies

2019 ◽  
Author(s):  
Lissette Gomez ◽  
Gabriel J. Odom ◽  
Juan I. Young ◽  
Eden R. Martin ◽  
Lizhong Liu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Type I Error ◽  
Association Studies ◽  
Cost Effective ◽  
R Package ◽  
Genomic Region ◽  
Type I ◽  
Differentially Methylated Regions ◽  
Accurate Identification ◽  
Significance Threshold

ABSTRACTRecent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

Sign in / Sign up

Export Citation Format

Share Document