R-universe search: topic:dnamethylation

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TCGAbiolinks:TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.

Maintained by Tiago Chedraoui Silva. Last updated 1 months ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network sequencing survival software bioc bioconductor gdc integrative-analysis tcga tcga-data tcgabiolinks

310 stars 14.47 score 1.6k scripts 6 dependents

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edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 19 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

13.40 score 17k scripts 255 dependents

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minfi:Analyze Illumina Infinium DNA methylation arrays

Tools to analyze & visualize Illumina Infinium methylation arrays.

Maintained by Kasper Daniel Hansen. Last updated 4 months ago.

immunooncology dnamethylation differentialmethylation epigenetics microarray methylationarray multichannel twochannel dataimport normalization preprocessing qualitycontrol

60 stars 12.82 score 996 scripts 27 dependents

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bsseq:Analyze, manage and store whole-genome methylation data

A collection of tools for analyzing and visualizing whole-genome methylation data from sequencing. This includes whole-genome bisulfite sequencing and Oxford nanopore data.

Maintained by Kasper Daniel Hansen. Last updated 3 months ago.

dnamethylation cpp

37 stars 12.26 score 676 scripts 15 dependents

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methylKit:DNA methylation analysis from high-throughput bisulfite sequencing results

methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files.

Maintained by Altuna Akalin. Last updated 29 days ago.

dnamethylation sequencing methylseq genome-biology methylation statistical-analysis visualization curl bzip2 xz-utils zlib cpp

220 stars 11.80 score 578 scripts 3 dependents

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bumphunter:Bump Hunter

Tools for finding bumps in genomic data

Maintained by Tamilselvi Guharaj. Last updated 5 months ago.

dnamethylation epigenetics infrastructure multiplecomparison immunooncology

16 stars 11.61 score 210 scripts 43 dependents

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EpiDISH:Epigenetic Dissection of Intra-Sample-Heterogeneity

EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of blood-tissue of any age, from birth to old-age, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies.

Maintained by Shijie C. Zheng. Last updated 5 months ago.

dnamethylation methylationarray epigenetics differentialmethylation immunooncology

48 stars 10.28 score 166 scripts 4 dependents

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methylumi:Handle Illumina methylation data

This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included.

Maintained by Sean Davis. Last updated 5 months ago.

dnamethylation twochannel preprocessing qualitycontrol cpgisland

9 stars 9.90 score 89 scripts 9 dependents

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sesame:SEnsible Step-wise Analysis of DNA MEthylation BeadChips

Tools For analyzing Illumina Infinium DNA methylation arrays. SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features accurate detection calling, intelligent inference of ethnicity, sex and advanced quality control routines.

Maintained by Wanding Zhou. Last updated 3 months ago.

dnamethylation methylationarray preprocessing qualitycontrol bioinformatics dna-methylation microarray

69 stars 9.08 score 258 scripts 1 dependents

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RTCGA:The Cancer Genome Atlas Data Integration

The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use.

Maintained by Marcin Kosinski. Last updated 5 months ago.

immunooncology software dataimport datarepresentation preprocessing rnaseq survival dnamethylation principalcomponent visualization

51 stars 8.91 score 106 scripts 1 dependents

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TOAST:Tools for the analysis of heterogeneous tissues

This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge.

Maintained by Ziyi Li. Last updated 5 months ago.

dnamethylation geneexpression differentialexpression differentialmethylation microarray genetarget epigenetics methylationarray

11 stars 8.01 score 104 scripts 3 dependents

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wateRmelon:Illumina DNA methylation array normalization and metrics

15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages.

Maintained by Leo C Schalkwyk. Last updated 4 months ago.

dnamethylation microarray twochannel preprocessing qualitycontrol

7.75 score 247 scripts 2 dependents

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pathwayPCA:Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection

pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>; Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.

Maintained by Gabriel Odom. Last updated 5 months ago.

copynumbervariation dnamethylation geneexpression snp transcription geneprediction genesetenrichment genesignaling genetarget genomewideassociation genomicvariation cellbiology epigenetics functionalgenomics genetics lipidomics metabolomics proteomics systemsbiology transcriptomics classification dimensionreduction featureextraction principalcomponent regression survival multiplecomparison pathways

11 stars 7.74 score 42 scripts

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MIRA:Methylation-Based Inference of Regulatory Activity

DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets.

Maintained by John Lawson. Last updated 5 months ago.

immunooncology dnamethylation generegulation genomeannotation systemsbiology functionalgenomics chipseq methylseq sequencing epigenetics coverage

12 stars 7.56 score 7 scripts 1 dependents

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methrix:Fast and efficient summarization of generic bedGraph files from Bisufite sequencing

Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions.

Maintained by Anand Mayakonda. Last updated 5 months ago.

dnamethylation sequencing coverage bedgraph bioinformatics dna-methylation

32 stars 7.53 score 39 scripts 1 dependents

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ELMER:Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes

ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue.

Maintained by Tiago Chedraoui Silva. Last updated 5 months ago.

dnamethylation geneexpression motifannotation software generegulation transcription network

7.42 score 176 scripts

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methylSig:MethylSig: Differential Methylation Testing for WGBS and RRBS Data

MethylSig is a package for testing for differentially methylated cytosines (DMCs) or regions (DMRs) in whole-genome bisulfite sequencing (WGBS) or reduced representation bisulfite sequencing (RRBS) experiments. MethylSig uses a beta binomial model to test for significant differences between groups of samples. Several options exist for either site-specific or sliding window tests, and variance estimation.

Maintained by Raymond G. Cavalcante. Last updated 5 months ago.

dnamethylation differentialmethylation epigenetics regression methylseq differential-methylation dna-methylation

18 stars 7.40 score 23 scripts

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shinyMethyl:Interactive visualization for Illumina methylation arrays

Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported.

Maintained by Jean-Philippe Fortin. Last updated 5 months ago.

dnamethylation microarray twochannel preprocessing qualitycontrol methylationarray

5 stars 7.34 score 42 scripts

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missMethyl:Analysing Illumina HumanMethylation BeadChip Data

Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes.

Maintained by Belinda Phipson. Last updated 27 days ago.

normalization dnamethylation methylationarray genomicvariation geneticvariability differentialmethylation genesetenrichment

7.24 score 300 scripts 1 dependents

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mitch:Multi-Contrast Gene Set Enrichment Analysis

mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments.

Maintained by Mark Ziemann. Last updated 4 months ago.

geneexpression genesetenrichment singlecell transcriptomics epigenetics proteomics differentialexpression reactome dnamethylation methylationarray gene-regulation gene-seq-analysis pathway-analysis

16 stars 7.06 score 15 scripts

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DSS:Dispersion shrinkage for sequencing data

DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions.

Maintained by Hao Wu. Last updated 5 months ago.

sequencing rnaseq dnamethylation geneexpression differentialexpression differentialmethylation

7.02 score 248 scripts 5 dependents

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COCOA:Coordinate Covariation Analysis

COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data.

Maintained by John Lawson. Last updated 5 months ago.

epigenetics dnamethylation atacseq dnaseseq methylseq methylationarray principalcomponent genomicvariation generegulation genomeannotation systemsbiology functionalgenomics chipseq sequencing immunooncology dna-methylation pca

10 stars 7.02 score 21 scripts

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NanoMethViz:Visualise methylation data from Oxford Nanopore sequencing

NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features.

Maintained by Shian Su. Last updated 20 days ago.

software longread visualization differentialmethylation dnamethylation epigenetics dataimport zlib cpp

26 stars 6.95 score 11 scripts

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RnBeads:RnBeads

RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.

Maintained by Fabian Mueller. Last updated 2 months ago.

dnamethylation methylationarray methylseq epigenetics qualitycontrol preprocessing batcheffect differentialmethylation sequencing cpgisland immunooncology twochannel dataimport

6.85 score 169 scripts 1 dependents

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MoonlightR:Identify oncogenes and tumor suppressor genes from omics data

Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.

Maintained by Matteo Tiberti. Last updated 5 months ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network survival genesetenrichment networkenrichment

17 stars 6.57 score

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ChAMP:Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC

The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations.

Maintained by Yuan Tian. Last updated 5 months ago.

microarray methylationarray normalization twochannel copynumber dnamethylation

6.50 score 278 scripts

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

coMethDMR identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously.

Maintained by Fernanda Veitzman. Last updated 5 months ago.

dnamethylation epigenetics methylationarray differentialmethylation genomewideassociation

7 stars 6.47 score 42 scripts

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SingleMoleculeFootprinting:Analysis tools for Single Molecule Footprinting (SMF) data

SingleMoleculeFootprinting provides functions to analyze Single Molecule Footprinting (SMF) data. Following the workflow exemplified in its vignette, the user will be able to perform basic data analysis of SMF data with minimal coding effort. Starting from an aligned bam file, we show how to perform quality controls over sequencing libraries, extract methylation information at the single molecule level accounting for the two possible kind of SMF experiments (single enzyme or double enzyme), classify single molecules based on their patterns of molecular occupancy, plot SMF information at a given genomic location.

Maintained by Guido Barzaghi. Last updated 4 days ago.

dnamethylation coverage nucleosomepositioning datarepresentation epigenetics methylseq qualitycontrol sequencing

2 stars 6.46 score 27 scripts

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Moonlight2R:Identify oncogenes and tumor suppressor genes from omics data

The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes.

Maintained by Matteo Tiberti. Last updated 2 months ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network survival genesetenrichment networkenrichment

5 stars 6.41 score 43 scripts

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dmrseq:Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing

This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions.

Maintained by Keegan Korthauer. Last updated 5 months ago.

immunooncology dnamethylation epigenetics multiplecomparison software sequencing differentialmethylation wholegenome regression functionalgenomics

6.39 score 59 scripts 1 dependents

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recountmethylation:Access and analyze public DNA methylation array data compilations

Resources for cross-study analyses of public DNAm array data from NCBI GEO repo, produced using Illumina's Infinium HumanMethylation450K (HM450K) and MethylationEPIC (EPIC) platforms. Provided functions enable download, summary, and filtering of large compilation files. Vignettes detail background about file formats, example analyses, and more. Note the disclaimer on package load and consult the main manuscripts for further info.

Maintained by Sean K Maden. Last updated 5 months ago.

dnamethylation epigenetics microarray methylationarray experimenthub

9 stars 6.28 score 9 scripts

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lumi:BeadArray Specific Methods for Illumina Methylation and Expression Microarrays

The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays.

Maintained by Lei Huang. Last updated 5 months ago.

microarray onechannel preprocessing dnamethylation qualitycontrol twochannel

6.26 score 294 scripts 5 dependents

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scMET:Bayesian modelling of cell-to-cell DNA methylation heterogeneity

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.

Maintained by Andreas C. Kapourani. Last updated 5 months ago.

immunooncology dnamethylation differentialmethylation differentialexpression geneexpression generegulation epigenetics genetics clustering featureextraction regression bayesian sequencing coverage singlecell bayesian-inference generalised-linear-models heterogeneity hierarchical-models methylation-analysis single-cell cpp

20 stars 6.23 score 42 scripts

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iNETgrate:Integrates DNA methylation data with gene expression in a single gene network

The iNETgrate package provides functions to build a correlation network in which nodes are genes. DNA methylation and gene expression data are integrated to define the connections between genes. This network is used to identify modules (clusters) of genes. The biological information in each of the resulting modules is represented by an eigengene. These biological signatures can be used as features e.g., for classification of patients into risk categories. The resulting biological signatures are very robust and give a holistic view of the underlying molecular changes.

Maintained by Habil Zare. Last updated 5 months ago.

geneexpression rnaseq dnamethylation networkinference network graphandnetwork biomedicalinformatics systemsbiology transcriptomics classification clustering dimensionreduction principalcomponent mrnamicroarray normalization geneprediction kegg survival core-services

74 stars 6.21 score 1 scripts

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knowYourCG:Functional analysis of DNA methylome datasets

KnowYourCG (KYCG) is a supervised learning framework designed for the functional analysis of DNA methylation data. Unlike existing tools that focus on genes or genomic intervals, KnowYourCG directly targets CpG dinucleotides, featuring automated supervised screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait-epigenome associations. KnowYourCG addresses the challenges of data sparsity in various methylation datasets, including low-pass Nanopore sequencing, single-cell DNA methylomes, 5-hydroxymethylation profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies and epigenetic clocks.

Maintained by Goldberg David. Last updated 3 months ago.

epigenetics dnamethylation sequencing singlecell spatial methylationarray zlib

2 stars 6.10 score 4 scripts

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ramwas:Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms

A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) <doi:10.1093/bioinformatics/bty069>.

Maintained by Andrey A Shabalin. Last updated 5 months ago.

dnamethylation sequencing qualitycontrol coverage preprocessing normalization batcheffect principalcomponent differentialmethylation visualization

10 stars 6.08 score 85 scripts

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ENmix:Quality control and analysis tools for Illumina DNA methylation BeadChip

Tools for quanlity control, analysis and visulization of Illumina DNA methylation array data.

Maintained by Zongli Xu. Last updated 16 days ago.

dnamethylation preprocessing qualitycontrol twochannel microarray onechannel methylationarray batcheffect normalization dataimport regression principalcomponent epigenetics multichannel differentialmethylation immunooncology

6.01 score 115 scripts

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biscuiteer:Convenience Functions for Biscuit

A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc.

Maintained by Jacob Morrison. Last updated 5 months ago.

dataimport methylseq dnamethylation

6 stars 5.98 score 16 scripts

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epialleleR:Fast, Epiallele-Aware Methylation Caller and Reporter

Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Among other things, this package can also extract and visualise methylation patterns and assess allele specificity of methylation.

Maintained by Oleksii Nikolaienko. Last updated 25 days ago.

dnamethylation epigenetics methylseq longread bioconductor dna-methylation epiallele next-generation-sequencing samtools curl bzip2 xz-utils zlib cpp

4 stars 5.94 score 5 scripts

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REMP:Repetitive Element Methylation Prediction

Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE.

Maintained by Yinan Zheng. Last updated 5 months ago.

dnamethylation microarray methylationarray sequencing genomewideassociation epigenetics preprocessing multichannel twochannel differentialmethylation qualitycontrol dataimport

2 stars 5.94 score 18 scripts

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Repitools:Epigenomic tools

Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc.

Maintained by Mark Robinson. Last updated 5 months ago.

dnamethylation geneexpression methylseq

5.90 score 267 scripts

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BEclear:Correction of batch effects in DNA methylation data

Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers.

Maintained by Livia Rasp. Last updated 5 months ago.

batcheffect dnamethylation software preprocessing statisticalmethod batch-effects bioconductor-package dna-methylation latent-factor-model methylation missing-data missing-values stochastic-gradient-descent cpp

4 stars 5.90 score 11 scripts

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deconvR:Simulation and Deconvolution of Omic Profiles

This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BSmeth2Probe, to make mapping WGBS data to their probe IDs easier.

Maintained by Irem B. Gündüz. Last updated 5 months ago.

dnamethylation regression geneexpression rnaseq singlecell statisticalmethod transcriptomics bioconductor-package deconvolution dna-methylation omics

10 stars 5.78 score 15 scripts

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BPRMeth:Model higher-order methylation profiles

The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles.

Maintained by Chantriolnt-Andreas Kapourani. Last updated 5 months ago.

immunooncology dnamethylation geneexpression generegulation epigenetics genetics clustering featureextraction regression rnaseq bayesian kegg sequencing coverage singlecell openblas cpp

5.75 score 94 scripts 1 dependents

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DAMEfinder:Finds DAMEs - Differential Allelicly MEthylated regions

'DAMEfinder' offers functionality for taking methtuple or bismark outputs to calculate ASM scores and compute DAMEs. It also offers nice visualization of methyl-circle plots.

Maintained by Stephany Orjuela. Last updated 5 months ago.

dnamethylation differentialmethylation coverage

10 stars 5.70 score 9 scripts

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GeoTcgaData:Processing Various Types of Data on GEO and TCGA

Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.

Maintained by Erqiang Hu. Last updated 5 months ago.

geneexpression differentialexpression rnaseq copynumbervariation microarray software dnamethylation differentialmethylation snp atacseq methylationarray

25 stars 5.68 score 19 scripts

bioc

planet:Placental DNA methylation analysis tools

This package contains R functions to predict biological variables to from placnetal DNA methylation data generated from infinium arrays. This includes inferring ethnicity/ancestry, gestational age, and cell composition from placental DNA methylation array (450k/850k) data.

Maintained by Victor Yuan. Last updated 2 months ago.

software differentialmethylation epigenetics microarray methylationarray dnamethylation cpgisland ancestry dna-methylation-data genetics inference machine-learning placenta

4 stars 5.64 score 12 scripts 1 dependents

bioc

methyLImp2:Missing value estimation of DNA methylation data

This package allows to estimate missing values in DNA methylation data. methyLImp method is based on linear regression since methylation levels show a high degree of inter-sample correlation. Implementation is parallelised over chromosomes since probes on different chromosomes are usually independent. Mini-batch approach to reduce the runtime in case of large number of samples is available.

Maintained by Anna Plaksienko. Last updated 2 months ago.

dnamethylation microarray software methylationarray regression imputation methylation missing-value-imputation

6 stars 5.62 score 3 scripts

bioc

methylclock:Methylclock - DNA methylation-based clocks

This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. Chronological DNAm age (in years) : Horvath's clock, Hannum's clock, BNN, Horvath's skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's clocks. Biological DNAm clocks : Levine's clock and Telomere Length's clock.

Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.

dnamethylation biologicalquestion preprocessing statisticalmethod normalization cpp

39 stars 5.52 score 28 scripts

bioc

conumee:Enhanced copy-number variation analysis using Illumina DNA methylation arrays

This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays.

Maintained by Volker Hovestadt. Last updated 5 months ago.

copynumbervariation dnamethylation methylationarray microarray normalization preprocessing qualitycontrol software

5.48 score 30 scripts

bioc

bigmelon:Illumina methylation array analysis for large experiments

Methods for working with Illumina arrays using gdsfmt.

Maintained by Leonard C. Schalkwyk. Last updated 5 months ago.

dnamethylation microarray twochannel preprocessing qualitycontrol methylationarray dataimport cpgisland

5.47 score 21 scripts

bioc

GEM:GEM: fast association study for the interplay of Gene, Environment and Methylation

Tools for analyzing EWAS, methQTL and GxE genome widely.

Maintained by Hong Pan. Last updated 5 months ago.

methylseq methylationarray genomewideassociation regression dnamethylation snp geneexpression gui

5.43 score 27 scripts

bioc

cfTools:Informatics Tools for Cell-Free DNA Study

The cfTools R package provides methods for cell-free DNA (cfDNA) methylation data analysis to facilitate cfDNA-based studies. Given the methylation sequencing data of a cfDNA sample, for each cancer marker or tissue marker, we deconvolve the tumor-derived or tissue-specific reads from all reads falling in the marker region. Our read-based deconvolution algorithm exploits the pervasiveness of DNA methylation for signal enhancement, therefore can sensitively identify a trace amount of tumor-specific or tissue-specific cfDNA in plasma. cfTools provides functions for (1) cancer detection: sensitively detect tumor-derived cfDNA and estimate the tumor-derived cfDNA fraction (tumor burden); (2) tissue deconvolution: infer the tissue type composition and the cfDNA fraction of multiple tissue types for a plasma cfDNA sample. These functions can serve as foundations for more advanced cfDNA-based studies, including cancer diagnosis and disease monitoring.

Maintained by Ran Hu. Last updated 5 months ago.

software biomedicalinformatics epigenetics sequencing methylseq dnamethylation differentialmethylation cpp

7 stars 5.32 score 2 scripts

bioc

EnMCB:Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models

Creation of the correlated blocks using DNA methylation profiles. Machine learning models can be constructed to predict differentially methylated blocks and disease progression.

Maintained by Xin Yu. Last updated 5 months ago.

normalization dnamethylation methylationarray supportvectormachine

9 stars 5.26 score 2 scripts

bioc

omicsPrint:Cross omic genetic fingerprinting

omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation).

Maintained by Davy Cats. Last updated 5 months ago.

qualitycontrol genetics epigenetics transcriptomics dnamethylation transcription geneticvariability immunooncology

5.20 score 32 scripts

bioc

methylCC:Estimate the cell composition of whole blood in DNA methylation samples

A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing).

Maintained by Stephanie C. Hicks. Last updated 5 months ago.

microarray sequencing dnamethylation methylationarray methylseq wholegenome

19 stars 5.18 score 8 scripts

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HELP:Tools for HELP data analysis

The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications.

Maintained by Reid F. Thompson. Last updated 5 months ago.

cpgisland dnamethylation microarray twochannel dataimport qualitycontrol preprocessing visualization

5.18 score 76 scripts

bioc

MEDIPS:DNA IP-seq data analysis

MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis.

Maintained by Lukas Chavez. Last updated 5 months ago.

dnamethylation cpgisland differentialexpression sequencing chipseq preprocessing qualitycontrol visualization microarray genetics coverage genomeannotation copynumbervariation sequencematching

5.17 score 74 scripts

bioc

CTexploreR:Explores Cancer Testis Genes

The CTexploreR package re-defines the list of Cancer Testis/Germline (CT) genes. It is based on publicly available RNAseq databases (GTEx, CCLE and TCGA) and summarises CT genes' main characteristics. Several visualisation functions allow to explore their expression in different types of tissues and cancer cells, or to inspect the methylation status of their promoters in normal tissues.

Maintained by Axelle Loriot. Last updated 5 months ago.

transcriptomics epigenetics differentialexpression geneexpression dnamethylation experimenthubsoftware dataimport bioconductor

5.02 score 2 scripts

bioc

shinyepico:ShinyÉPICo

ShinyÉPICo is a graphical pipeline to analyze Illumina DNA methylation arrays (450k or EPIC). It allows to calculate differentially methylated positions and differentially methylated regions in a user-friendly interface. Moreover, it includes several options to export the results and obtain files to perform downstream analysis.

Maintained by Octavio Morante-Palacios. Last updated 5 months ago.

differentialmethylation dnamethylation microarray preprocessing qualitycontrol

5 stars 5.00 score 1 scripts

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Harman:The removal of batch effects from datasets using a PCA and constrained optimisation based technique

Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user.

Maintained by Jason Ross. Last updated 5 months ago.

batcheffect microarray multiplecomparison principalcomponent normalization preprocessing dnamethylation transcription software statisticalmethod cpp

4.97 score 31 scripts 1 dependents

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spiky:Spike-in calibration for cell-free MeDIP

spiky implements methods and model generation for cfMeDIP (cell-free methylated DNA immunoprecipitation) with spike-in controls. CfMeDIP is an enrichment protocol which avoids destructive conversion of scarce template, making it ideal as a "liquid biopsy," but creating certain challenges in comparing results across specimens, subjects, and experiments. The use of synthetic spike-in standard oligos allows diagnostics performed with cfMeDIP to quantitatively compare samples across subjects, experiments, and time points in both relative and absolute terms.

Maintained by Tim Triche. Last updated 5 months ago.

differentialmethylation dnamethylation normalization preprocessing qualitycontrol sequencing

2 stars 4.90 score 3 scripts

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Melissa:Bayesian clustering and imputationa of single cell methylomes

Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach.

Maintained by C. A. Kapourani. Last updated 5 months ago.

immunooncology dnamethylation geneexpression generegulation epigenetics genetics clustering featureextraction regression rnaseq bayesian kegg sequencing coverage singlecell

4.90 score 7 scripts

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methylscaper:Visualization of Methylation Data

methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning.

Maintained by Bacher Rhonda. Last updated 5 months ago.

dnamethylation epigenetics sequencing visualization singlecell nucleosomepositioning

1 stars 4.90 score 3 scripts

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MethylSeekR:Segmentation of Bis-seq data

This is a package for the discovery of regulatory regions from Bis-seq data

Maintained by Lukas Burger. Last updated 5 months ago.

sequencing methylseq dnamethylation

4.83 score 34 scripts

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BiSeq:Processing and analyzing bisulfite sequencing data

The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples.

Maintained by Katja Hebestreit. Last updated 5 months ago.

genetics sequencing methylseq dnamethylation

4.78 score 30 scripts

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mist:Differential Methylation Analysis for scDNAm Data

mist (Methylation Inference for Single-cell along Trajectory) is a hierarchical Bayesian framework for modeling DNA methylation trajectories and performing differential methylation (DM) analysis in single-cell DNA methylation (scDNAm) data. It estimates developmental-stage-specific variations, identifies genomic features with drastic changes along pseudotime, and, for two phenotypic groups, detects features with distinct temporal methylation patterns. mist uses Gibbs sampling to estimate parameters for temporal changes and stage-specific variations.

Maintained by Daoyu Duan. Last updated 2 months ago.

epigenetics differentialmethylation dnamethylation singlecell software

4.73 score 12 scripts

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methylPipe:Base resolution DNA methylation data analysis

Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data.

Maintained by Mattia Furlan. Last updated 5 months ago.

methylseq dnamethylation coverage sequencing

4.73 score 1 scripts 1 dependents

bioc

MethylMix:MethylMix: Identifying methylation driven cancer genes

MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8.

Maintained by Olivier Gevaert. Last updated 5 months ago.

dnamethylation statisticalmethod differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network

4.72 score 26 scripts

xinghuq

DA:Discriminant Analysis for Evolutionary Inference

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.

Maintained by Xinghu Qin. Last updated 4 years ago.

biomedicalinformatics chipseq clustering coverage dnamethylation differentialexpression differentialmethylation software differentialsplicing epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways qualitycontrol rnaseq regression sage sequencing systemsbiology timecourse transcription transcriptomics dapc discriminant-analysis ecological kernel kernel-local kernel-principle-components population-structure-inference principal-components

1 stars 4.70 score 1 scripts

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scmeth:Functions to conduct quality control analysis in methylation data

Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report.

Maintained by Divy Kangeyan. Last updated 5 months ago.

dnamethylation qualitycontrol preprocessing singlecell immunooncology bioconductor-package methylation single-cell-methylation

4.70 score 5 scripts

bioc

ramr:Detection of Rare Aberrantly Methylated Regions in Array and NGS Data

ramr is an R package for detection of epimutations (i.e., infrequent aberrant DNA methylation events) in large data sets obtained by methylation profiling using array or high-throughput methylation sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), to generate sets of all possible regions to be used as reference sets for enrichment analysis, and to generate biologically relevant test data sets for performance evaluation of AMR/DMR search algorithms.

Maintained by Oleksii Nikolaienko. Last updated 23 days ago.

dnamethylation differentialmethylation epigenetics methylationarray methylseq aberrant-methylation bioconductor dna-methylation epimutation methylation-microarrays next-generation-sequencing cpp openmp

4.65 score 5 scripts

bioc

methodical:Discovering genomic regions where methylation is strongly associated with transcriptional activity

DNA methylation is generally considered to be associated with transcriptional silencing. However, comprehensive, genome-wide investigation of this relationship requires the evaluation of potentially millions of correlation values between the methylation of individual genomic loci and expression of associated transcripts in a relatively large numbers of samples. Methodical makes this process quick and easy while keeping a low memory footprint. It also provides a novel method for identifying regions where a number of methylation sites are consistently strongly associated with transcriptional expression. In addition, Methodical enables housing DNA methylation data from diverse sources (e.g. WGBS, RRBS and methylation arrays) with a common framework, lifting over DNA methylation data between different genome builds and creating base-resolution plots of the association between DNA methylation and transcriptional activity at transcriptional start sites.

Maintained by Richard Heery. Last updated 2 months ago.

dnamethylation methylationarray transcription genomewideassociation software openjdk

4.65 score 14 scripts

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methylInheritance:Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect

Permutation analysis, based on Monte Carlo sampling, for testing the hypothesis that the number of conserved differentially methylated elements, between several generations, is associated to an effect inherited from a treatment and that stochastic effect can be dismissed.

Maintained by Astrid Deschênes. Last updated 5 months ago.

biologicalquestion epigenetics dnamethylation differentialmethylation methylseq software immunooncology statisticalmethod wholegenome sequencing analysis bioconductor bioinformatics cpg differentially-methylated-elements inheritance monte-carlo-sampling permutation

4.60 score 1 scripts

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methInheritSim:Simulating Whole-Genome Inherited Bisulphite Sequencing Data

Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset.

Maintained by Pascal Belleau. Last updated 5 months ago.

biologicalquestion epigenetics dnamethylation differentialmethylation methylseq software immunooncology statisticalmethod wholegenome sequencing bisulphite-sequencing inheritance methylation simulation

1 stars 4.60 score 1 scripts

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MethylAid:Visual and interactive quality control of large Illumina DNA Methylation array data sets

A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored.

Maintained by L.J.Sinke. Last updated 5 months ago.

dnamethylation methylationarray microarray twochannel qualitycontrol batcheffect visualization gui

4.51 score 16 scripts

bioc

EpiMix:EpiMix: an integrative tool for the population-level analysis of DNA methylation

EpiMix is a comprehensive tool for the integrative analysis of high-throughput DNA methylation data and gene expression data. EpiMix enables automated data downloading (from TCGA or GEO), preprocessing, methylation modeling, interactive visualization and functional annotation.To identify hypo- or hypermethylated CpG sites across physiological or pathological conditions, EpiMix uses a beta mixture modeling to identify the methylation states of each CpG probe and compares the methylation of the experimental group to the control group.The output from EpiMix is the functional DNA methylation that is predictive of gene expression. EpiMix incorporates specialized algorithms to identify functional DNA methylation at various genetic elements, including proximal cis-regulatory elements of protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs.

Maintained by Yuanning Zheng. Last updated 5 months ago.

software epigenetics preprocessing dnamethylation geneexpression differentialmethylation

1 stars 4.48 score 7 scripts 1 dependents

bioc

methylMnM:detect different methylation level (DMR)

To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation.

Maintained by Yan Zhou. Last updated 5 months ago.

software dnamethylation sequencing

4.38 score 2 scripts 1 dependents

samarafk

MLML2R:Maximum Likelihood Estimation of DNA Methylation and Hydroxymethylation Proportions

Maximum likelihood estimates (MLE) of the proportions of 5-mC and 5-hmC in the DNA using information from BS-conversion, TAB-conversion, and oxBS-conversion methods. One can use information from all three methods or any combination of two of them. Estimates are based on Binomial model by Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and Kiihl et al. (2019) <doi:10.1515/sagmb-2018-0031>.

Maintained by Samara Kiihl. Last updated 5 years ago.

software methylationarray epigenetics dnamethylation microarray twochannel onechannel

2 stars 4.34 score 22 scripts

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MEDME:Modelling Experimental Data from MeDIP Enrichment

MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments

Maintained by Mattia Pelizzola. Last updated 5 months ago.

microarray cpgisland dnamethylation

4.30 score 2 scripts

bioc

MAGAR:MAGAR: R-package to compute methylation Quantitative Trait Loci (methQTL) from DNA methylation and genotyping data

"Methylation-Aware Genotype Association in R" (MAGAR) computes methQTL from DNA methylation and genotyping data from matched samples. MAGAR uses a linear modeling stragety to call CpGs/SNPs that are methQTLs. MAGAR accounts for the local correlation structure of CpGs.

Maintained by Michael Scherer. Last updated 5 months ago.

regression epigenetics dnamethylation snp geneticvariability methylationarray microarray cpgisland methylseq sequencing mrnamicroarray preprocessing copynumbervariation twochannel immunooncology differentialmethylation batcheffect qualitycontrol dataimport network clustering graphandnetwork

4.30 score 3 scripts

bioc

SOMNiBUS:Smooth modeling of bisulfite sequencing

This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture.

Maintained by Kathleen Klein. Last updated 3 months ago.

dnamethylation regression epigenetics differentialmethylation sequencing functionalprediction

1 stars 4.30 score 3 scripts

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BEAT:BEAT - BS-Seq Epimutation Analysis Toolkit

Model-based analysis of single-cell methylation data

Maintained by Kemal Akman. Last updated 5 months ago.

immunooncology genetics methylseq software dnamethylation epigenetics

4.30 score 3 scripts

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regionalpcs:Summarizing Regional Methylation with Regional Principal Components Analysis

Functions to summarize DNA methylation data using regional principal components. Regional principal components are computed using principal components analysis within genomic regions to summarize the variability in methylation levels across CpGs. The number of principal components is chosen using either the Marcenko-Pasteur or Gavish-Donoho method to identify relevant signal in the data.

Maintained by Tiffany Eulalio. Last updated 5 months ago.

dnamethylation differentialmethylation statisticalmethod software methylationarray

2 stars 4.30 score 4 scripts

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MassArray:Analytical Tools for MassArray Data

This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis.

Maintained by Reid F. Thompson. Last updated 5 months ago.

immunooncology dnamethylation snp massspectrometry genetics dataimport visualization

4.30 score 1 scripts

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PoDCall:Positive Droplet Calling for DNA Methylation Droplet Digital PCR

Reads files exported from 'QX Manager or QuantaSoft' containing amplitude values from a run of ddPCR (96 well plate) and robustly sets thresholds to determine positive droplets for each channel of each individual well. Concentration and normalized concentration in addition to other metrics is then calculated for each well. Results are returned as a table, optionally written to file, as well as optional plots (scatterplot and histogram) for both channels per well written to file. The package includes a shiny application which provides an interactive and user-friendly interface to the full functionality of PoDCall.

Maintained by Hans Petter Brodal. Last updated 2 months ago.

classification epigenetics ddpcr differentialmethylation cpgisland dnamethylation

4.30 score 5 scripts

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epimutacions:Robust outlier identification for DNA methylation data

The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations.

Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.

dnamethylation biologicalquestion preprocessing statisticalmethod normalization cpp

4.23 score 28 scripts

bioc

betaHMM:A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data

A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.

Maintained by Koyel Majumdar. Last updated 3 months ago.

dnamethylation differentialmethylation immunooncology biomedicalinformatics methylationarray software multiplecomparison sequencing spatial coverage genetarget hiddenmarkovmodel microarray

4.18 score

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methimpute:Imputation-guided re-construction of complete methylomes from WGBS data

This package implements functions for calling methylation for all cytosines in the genome.

Maintained by Aaron Taudt. Last updated 5 months ago.

immunooncology software dnamethylation epigenetics hiddenmarkovmodel sequencing coverage cpp openmp

4.11 score 13 scripts

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DMRcaller:Differentially Methylated Regions caller

Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects.

Maintained by Nicolae Radu Zabet. Last updated 5 months ago.

differentialmethylation dnamethylation software sequencing coverage

4.08 score 8 scripts

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MethPed:A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes

Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro).

Maintained by Helena Carén. Last updated 5 months ago.

immunooncology dnamethylation classification epigenetics

4.00 score 1 scripts

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hummingbird:Bayesian Hidden Markov Model for the detection of differentially methylated regions

A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups.

Maintained by Eleni Adam. Last updated 5 months ago.

hiddenmarkovmodel bayesian dnamethylation biomedicalinformatics sequencing geneexpression differentialexpression differentialmethylation cpp

4.00 score 1 scripts

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GAprediction:Prediction of gestational age with Illumina HumanMethylation450 data

[GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data.

Maintained by Jon Bohlin. Last updated 5 months ago.

immunooncology dnamethylation epigenetics regression biomedicalinformatics

4.00 score

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HIREewas:Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies

In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R.

Maintained by Xiangyu Luo. Last updated 5 months ago.

dnamethylation differentialmethylation featureextraction

4.00 score 1 scripts

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MPFE:Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data

Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate.

Maintained by Conrad Burden. Last updated 5 months ago.

highthroughputsequencingdata dnamethylation methylseq

3.78 score 1 scripts

bioc

les:Identifying Differential Effects in Tiling Microarray Data

The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes.

Maintained by Julian Gehring. Last updated 5 months ago.

microarray differentialexpression chipchip dnamethylation transcription

3.78 score 3 scripts 1 dependents

bioc

cbaf:Automated functions for comparing various omic data from cbioportal.org

This package contains functions that allow analysing and comparing omic data across various cancers/cancer subgroups easily. So far, it is compatible with RNA-seq, microRNA-seq, microarray and methylation datasets that are stored on cbioportal.org.

Maintained by Arman Shahrisa. Last updated 5 months ago.

software assaydomain dnamethylation geneexpression transcription microarray researchfield biomedicalinformatics comparativegenomics epigenetics genetics transcriptomics

3.78 score 1 scripts

bioc

borealis:Bisulfite-seq OutlieR mEthylation At singLe-sIte reSolution

Borealis is an R library performing outlier analysis for count-based bisulfite sequencing data. It detectes outlier methylated CpG sites from bisulfite sequencing (BS-seq). The core of Borealis is modeling Beta-Binomial distributions. This can be useful for rare disease diagnoses.

Maintained by Garrett Jenkinson. Last updated 5 months ago.

sequencing coverage dnamethylation differentialmethylation

3.73 score 27 scripts

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funtooNorm:Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit

Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type.

Maintained by Kathleen Klein. Last updated 5 months ago.

dnamethylation preprocessing normalization

3.70 score

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mCSEA:Methylated CpGs Set Enrichment Analysis

Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions.

Maintained by Jordi Martorell-Marugán. Last updated 4 months ago.

immunooncology differentialmethylation dnamethylation epigenetics genetics genomeannotation methylationarray microarray multiplecomparison twochannel

3.38 score 15 scripts

bioc

normalize450K:Preprocessing of Illumina Infinium 450K data

Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values.

Maintained by Jonathan Alexander Heiss. Last updated 5 months ago.

normalization dnamethylation microarray twochannel preprocessing methylationarray

3.30 score 1 scripts

bioc

qsea:IP-seq data analysis and vizualization

qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples.

Maintained by Matthias Lienhard. Last updated 5 months ago.

sequencing dnamethylation cpgisland chipseq preprocessing normalization qualitycontrol visualization copynumbervariation chiponchip differentialmethylation

3.30 score 7 scripts

bioc

TurboNorm:A fast scatterplot smoother suitable for microarray normalization

A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available.

Maintained by Maarten van Iterson. Last updated 5 months ago.

microarray onechannel twochannel preprocessing dnamethylation cpgisland methylationarray normalization

3.30 score 1 scripts

bioc

MBAmethyl:Model-based analysis of DNA methylation data

This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences.

Maintained by Tao Wang. Last updated 5 months ago.

dnamethylation methylationarray

3.30 score 1 scripts

bioc

ARRmNormalization:Adaptive Robust Regression normalization for Illumina methylation data

Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay.

Maintained by Jean-Philippe Fortin. Last updated 5 months ago.

dnamethylation twochannel preprocessing microarray

3.30 score 1 scripts

bioc

iCheck:QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data

QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data.

Maintained by Weiliang Qiu. Last updated 5 months ago.

geneexpression differentialexpression microarray preprocessing dnamethylation onechannel twochannel qualitycontrol

3.00 score 1 scripts

bioc

TransView:Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets

This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq.

Maintained by Julius Muller. Last updated 2 months ago.

immunooncology dnamethylation geneexpression transcription microarray sequencing chipseq rnaseq methylseq dataimport visualization clustering multiplecomparison curl bzip2 xz-utils zlib

2.60 score