Agent skill

bio-methylation-methylkit

DNA methylation analysis with methylKit in R. Import Bismark coverage files, filter by coverage, normalize samples, and perform statistical comparisons. Use when analyzing single-base methylation patterns, comparing samples, or preparing data for DMR detection.

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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-methylation-methylkit

SKILL.md

Version Compatibility

Reference examples tested with: Bismark 0.24+, methylKit 1.28+

Before using code patterns, verify installed versions match. If versions differ:

  • R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

methylKit Analysis

"Analyze methylation patterns across my samples" → Import per-cytosine methylation data, filter by coverage, normalize across samples, and test for differential methylation at individual CpG sites.

  • R: methylKit::methRead() → filterByCoverage() → normalizeCoverage() → calculateDiffMeth()

Read Bismark Coverage Files

r
library(methylKit)

file_list <- list('sample1.bismark.cov.gz', 'sample2.bismark.cov.gz',
                   'sample3.bismark.cov.gz', 'sample4.bismark.cov.gz')
sample_ids <- c('ctrl_1', 'ctrl_2', 'treat_1', 'treat_2')
treatment <- c(0, 0, 1, 1)  # 0 = control, 1 = treatment

meth_obj <- methRead(
    location = as.list(file_list),
    sample.id = as.list(sample_ids),
    treatment = treatment,
    assembly = 'hg38',
    context = 'CpG',
    pipeline = 'bismarkCoverage'
)

Read Bismark cytosine Report

r
meth_obj <- methRead(
    location = as.list(file_list),
    sample.id = as.list(sample_ids),
    treatment = treatment,
    assembly = 'hg38',
    context = 'CpG',
    pipeline = 'bismarkCytosineReport'
)

Basic Statistics

r
# Coverage statistics
getMethylationStats(meth_obj[[1]], plot = TRUE, both.strands = FALSE)

# Coverage per sample
getCoverageStats(meth_obj[[1]], plot = TRUE, both.strands = FALSE)

Filter by Coverage

r
# Remove CpGs with very low or very high coverage
meth_filtered <- filterByCoverage(
    meth_obj,
    lo.count = 10,        # Minimum 10 reads
    lo.perc = NULL,
    hi.count = NULL,
    hi.perc = 99.9        # Remove top 0.1% (likely PCR artifacts)
)

Normalize Coverage

r
# Normalize coverage between samples (recommended)
meth_norm <- normalizeCoverage(meth_filtered, method = 'median')

Merge Samples (Unite)

r
# Find common CpGs across all samples
meth_united <- unite(meth_norm, destrand = TRUE)  # Combine strands

# Allow some missing data
meth_united <- unite(meth_norm, destrand = TRUE, min.per.group = 2L)

Visualize Samples

r
# Correlation between samples
getCorrelation(meth_united, plot = TRUE)

# PCA of samples
PCASamples(meth_united, screeplot = TRUE)
PCASamples(meth_united)

# Clustering
clusterSamples(meth_united, dist = 'correlation', method = 'ward.D', plot = TRUE)

Differential Methylation (Single CpGs)

r
# Calculate differential methylation
diff_meth <- calculateDiffMeth(
    meth_united,
    overdispersion = 'MN',     # Use shrinkage
    test = 'Chisq',
    mc.cores = 4
)

# Get significant differentially methylated CpGs
dmcs <- getMethylDiff(diff_meth, difference = 25, qvalue = 0.01)

# Hyper vs hypomethylated
dmcs_hyper <- getMethylDiff(diff_meth, difference = 25, qvalue = 0.01, type = 'hyper')
dmcs_hypo <- getMethylDiff(diff_meth, difference = 25, qvalue = 0.01, type = 'hypo')

Tile-Based Analysis (Regions)

Goal: Detect differentially methylated regions by aggregating single CpG data into fixed-size genomic windows.

Approach: Tile individual CpG measurements into 1kb windows, unite common tiles across samples, and run differential methylation testing on the aggregated tiles.

r
# Aggregate CpGs into tiles/windows
tiles <- tileMethylCounts(meth_obj, win.size = 1000, step.size = 1000)
tiles_united <- unite(tiles, destrand = TRUE)

# Differential methylation on tiles
diff_tiles <- calculateDiffMeth(tiles_united, overdispersion = 'MN', mc.cores = 4)
dmrs <- getMethylDiff(diff_tiles, difference = 25, qvalue = 0.01)

Export Results

r
# To data frame
diff_df <- getData(dmcs)
write.csv(diff_df, 'dmcs_results.csv', row.names = FALSE)

# To BED file
library(genomation)
dmcs_gr <- as(dmcs, 'GRanges')
export(dmcs_gr, 'dmcs.bed', format = 'BED')

Annotate with Genomic Features

r
library(genomation)

gene_obj <- readTranscriptFeatures('genes.bed')

annotated <- annotateWithGeneParts(as(dmcs, 'GRanges'), gene_obj)

# Or with annotatr
library(annotatr)
annotations <- build_annotations(genome = 'hg38', annotations = 'hg38_basicgenes')
dmcs_annotated <- annotate_regions(regions = as(dmcs, 'GRanges'), annotations = annotations)

Reorganize for Multi-Group Comparison

r
# For more than 2 groups
meth_obj <- reorganize(
    meth_united,
    sample.ids = c('A1', 'A2', 'B1', 'B2', 'C1', 'C2'),
    treatment = c(0, 0, 1, 1, 2, 2)
)

Pool Replicates

r
# Combine biological replicates
meth_pooled <- pool(meth_united, sample.ids = c('control', 'treatment'))

Key Functions

Function Purpose
methRead Read methylation files
filterByCoverage Remove low/high coverage
normalizeCoverage Normalize between samples
unite Find common CpGs
calculateDiffMeth Statistical test
getMethylDiff Filter significant results
tileMethylCounts Region-level analysis
PCASamples PCA visualization
getCorrelation Sample correlation

Key Parameters for calculateDiffMeth

Parameter Default Description
overdispersion none MN (shrinkage) or shrinkMN
test Chisq Chisq, F, fast.fisher
mc.cores 1 Parallel cores
slim TRUE Remove unused columns

Related Skills

  • bismark-alignment - Generate input BAM files
  • methylation-calling - Extract coverage files
  • dmr-detection - Advanced DMR methods
  • pathway-analysis/go-enrichment - Functional annotation

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