Package: MethReg 1.15.0

Tiago Silva

MethReg: Assessing the regulatory potential of DNA methylation regions or sites on gene transcription

Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis.

Authors:Tiago Silva [aut, cre], Lily Wang [aut]

MethReg_1.15.0.tar.gz
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MethReg.pdf |MethReg.html✨
MethReg/json (API)
NEWS

# Install 'MethReg' in R:
install.packages('MethReg', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/transbioinfolab/methreg/issues

Datasets:
  • clinical - TCGA-COAD clinical matrix for 38 samples retrieved from GDC using TCGAbiolinks
  • dna.met.chr21 - TCGA-COAD DNA methylation matrix (beta-values) for 38 samples (only chr21) retrieved from GDC using TCGAbiolinks
  • gene.exp.chr21.log2 - TCGA-COAD gene expression matrix (log2 (FPKM-UQ + 1)) for 38 samples (only chromosome 21) retrieved from GDC using TCGAbiolinks

On BioConductor:MethReg-1.15.0(bioc 3.20)MethReg-1.14.0(bioc 3.19)

bioconductor-package

24 exports 1.08 score 166 dependencies

Last updated 2 months agofrom:aeb2a7e481

Exports:cor_dnam_target_genecor_tf_target_genecreate_triplet_distance_basedcreate_triplet_regulon_basedexport_results_to_tablefilter_dnam_by_quant_difffilter_exp_by_quant_mean_FCfilter_genes_zero_expressionget_human_tfsget_met_probes_infoget_promoter_avgget_region_target_geneget_residualsget_tf_ESget_tf_in_regioninteraction_modelmake_dnam_semake_exp_semake_granges_from_namesmake_names_from_grangesmethReg_analysisplot_interaction_modelreadRemap2022stratified_model

Dependencies:abindannotateAnnotationDbiAnnotationHubaskpassbackportsBHBiobaseBiocFileCacheBiocGenericsBiocIOBiocManagerBiocParallelBiocVersionBiostringsbitbit64bitopsblobbootbroomBSgenomecachemcarcarDatacaToolsclicliprCNErcodetoolscolorspacecorrplotcowplotcpp11crayoncurlDBIdbplyrDelayedArrayDerivDirichletMultinomialdoBydplyrExperimentHubfansifarverfastmapfilelockformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangesggplot2ggpubrggrepelggsciggsignifglueGO.dbgridExtragtablegtoolshmshttrIRangesisobandJASPAR2024jsonliteKEGGRESTlabelinglambda.rlatticelifecyclelme4magrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellnlmenloptrnnetnumDerivopensslopenxlsxpbkrtestpillarpkgconfigplogrplyrpngpolynompoweRlawpracmapreprocessCoreprettyunitsprogresspsclpurrrpwalignquantregR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppEigenRCurlreadrreshape2restfulrRhtslibrjsonrlangRsamtoolsRSQLiterstatixrtracklayerS4ArraysS4VectorsscalesseqLogosesamesesameDatasfsmiscsnowSparseArraySparseMstringistringrSummarizedExperimentsurvivalsysTFBSToolsTFMPvaluetibbletidyrtidyselecttzdbUCSC.utilsutf8vctrsviridisLitevroomwheatmapwithrXMLxtableXVectoryamlzipzlibbioc

MethReg: estimating regulatory potential of DNA methylation in gene transcription

Tiago Chedraoui Silva, Lily Wang

Rendered fromMethReg.Rmdusingknitr::rmarkdownon Jul 01 2024.

Last update: 2023-11-03
Started: 2020-08-28

Readme and manuals

Help Manual

Help pageTopics
TCGA-COAD clinical matrix for 38 samples retrieved from GDC using TCGAbiolinksclinical
Evaluate correlation of DNA methylation region and target gene expressioncor_dnam_target_gene
Evaluate correlation of TF expression and target gene expressioncor_tf_target_gene
Map DNAm to target genes using distance approaches, and TF to the DNAm region using JASPAR2024 TFBS.create_triplet_distance_based
Map TF and target genes using regulon databases or any user provided target-tf. Maps TF to the DNAm region with TFBS using JASPAR2020 TFBS.create_triplet_regulon_based
TCGA-COAD DNA methylation matrix (beta-values) for 38 samples (only chr21) retrieved from GDC using TCGAbiolinksdna.met.chr21
Format MethReg results table and export to XLSX fileexport_results_to_table
Select regions with variations in DNA methylation levels above a thresholdfilter_dnam_by_quant_diff
Select genes with variations above a thresholdfilter_exp_by_quant_mean_FC
Remove genes with gene expression level equal to 0 in a substantial percentage of the samplesfilter_genes_zero_expression
TCGA-COAD gene expression matrix (log2 (FPKM-UQ + 1)) for 38 samples (only chromosome 21) retrieved from GDC using TCGAbiolinksgene.exp.chr21.log2
Access human TF from Lambert et al 2018get_human_tfs
Get HM450/EPIC manifest files from Sesame packageget_met_probes_info
Summarize promoter DNA methylation beta values by mean.get_promoter_avg
Obtain target genes of input regions based on distanceget_region_target_gene
Get residuals from regression modelget_residuals
Calculate enrichment scores for each TF across all samples using dorothea and viper.get_tf_ES
Get human TFs for regions by either scanning it with motifmatchr using JASPAR 2024 database or overlapping with TF chip-seq from user inputget_tf_in_region
Fits linear models with interaction to triplet data (Target, TF, DNAm), where DNAm is a binary variable (samples in Q1 or Q4)interaction_model
Transform DNA methylation array into a summarized Experiment objectmake_dnam_se
Transform gene expression matrix into a Summarized Experiment objectmake_exp_se
Create a Granges object from a genmic region stringmake_granges_from_names
Create region name from Grangesmake_names_from_granges
Wrapper for MethReg functionsmethReg_analysis
Plot interaction model resultsplot_interaction_model
Plot stratified model resultsplot_stratified_model
Access REMAP2022 non-redundant peaksreadRemap2022
Fits linear models to triplet data (Target, TF, DNAm) for samples with high DNAm or low DNAm separately, and annotates TF (activator/repressor) and DNam effect over TF activity (attenuate, enhance).stratified_model