--- name: bio-workflows-methylation-pipeline description: End-to-end bisulfite sequencing workflow from FASTQ to differentially methylated regions. Covers Bismark alignment, methylation calling, and DMR detection with methylKit. Use when analyzing bisulfite sequencing data. tool_type: mixed primary_tool: Bismark workflow: true depends_on: - read-qc/fastp-workflow - methylation-analysis/bismark-alignment - methylation-analysis/methylation-calling - methylation-analysis/methylkit-analysis - methylation-analysis/dmr-detection qc_checkpoints: - after_qc: "Q30 >80%, adapter content removed" - after_alignment: "Mapping efficiency >50%, bisulfite conversion >99%" - after_calling: "Coverage distribution reasonable, no biased positions" measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # Methylation Pipeline Complete workflow from bisulfite sequencing FASTQ to differentially methylated regions. ## Workflow Overview ``` FASTQ files | v [1. QC & Trimming] -----> fastp/Trim Galore | v [2. Alignment] ---------> Bismark | v [3. Deduplication] -----> deduplicate_bismark | v [4. Methylation Calling] -> bismark_methylation_extractor | v [5. Analysis] -----------> methylKit (R) | v [6. DMR Detection] ------> methylKit/DSS | v Differentially methylated regions ``` ## Primary Path: Bismark + methylKit ### Step 1: Quality Control ```bash # Trim Galore recommended for bisulfite data (handles adapter bias) trim_galore --paired --fastqc \ -o trimmed/ \ sample_R1.fastq.gz sample_R2.fastq.gz # Or fastp with conservative settings fastp -i sample_R1.fastq.gz -I sample_R2.fastq.gz \ -o trimmed/sample_R1.fq.gz -O trimmed/sample_R2.fq.gz \ --detect_adapter_for_pe \ --qualified_quality_phred 20 \ --length_required 35 \ --html qc/sample_fastp.html ``` ### Step 2: Bismark Alignment ```bash # Prepare genome (once) bismark_genome_preparation --bowtie2 genome/ # Align bismark --genome genome/ \ -1 trimmed/sample_R1_val_1.fq.gz \ -2 trimmed/sample_R2_val_2.fq.gz \ -o aligned/ \ --parallel 4 \ --temp_dir tmp/ # Output: sample_R1_val_1_bismark_bt2_pe.bam ``` **QC Checkpoint:** Check Bismark report - Mapping efficiency >50% (BS-seq has lower rates) - Bisulfite conversion rate >99% ### Step 3: Deduplication ```bash deduplicate_bismark \ --bam \ -p \ -o deduplicated/ \ aligned/sample_R1_val_1_bismark_bt2_pe.bam ``` ### Step 4: Methylation Calling ```bash bismark_methylation_extractor \ --paired-end \ --comprehensive \ --bedGraph \ --cytosine_report \ --genome_folder genome/ \ -o methylation/ \ deduplicated/sample_R1_val_1_bismark_bt2_pe.deduplicated.bam # Generate summary report bismark2report bismark2summary ``` ### Step 5: Analysis with methylKit ```r library(methylKit) # Read methylation calls files <- list( 'methylation/control_1.CpG_report.txt', 'methylation/control_2.CpG_report.txt', 'methylation/treated_1.CpG_report.txt', 'methylation/treated_2.CpG_report.txt' ) sample_ids <- c('control_1', 'control_2', 'treated_1', 'treated_2') treatment <- c(0, 0, 1, 1) # Read cytosine reports meth_obj <- methRead( location = as.list(files), sample.id = as.list(sample_ids), assembly = 'hg38', treatment = treatment, context = 'CpG', pipeline = 'bismarkCytosineReport' ) # Filter by coverage meth_filtered <- filterByCoverage(meth_obj, lo.count = 10, hi.perc = 99.9) # Normalize coverage meth_norm <- normalizeCoverage(meth_filtered) # Merge samples (keep sites covered in all) meth_merged <- unite(meth_norm, destrand = TRUE) # Sample statistics getMethylationStats(meth_obj[[1]], plot = TRUE) getCoverageStats(meth_obj[[1]], plot = TRUE) ``` ### Step 6: DMR Detection ```r # Calculate differential methylation (per CpG) diff_meth <- calculateDiffMeth(meth_merged) # Get significant DMCs dmc <- getMethylDiff(diff_meth, difference = 25, qvalue = 0.01) # Tile into regions (DMRs) tiles <- tileMethylCounts(meth_merged, win.size = 1000, step.size = 1000) diff_tiles <- calculateDiffMeth(tiles) dmr <- getMethylDiff(diff_tiles, difference = 25, qvalue = 0.01) # Export write.csv(as.data.frame(dmc), 'dmc_results.csv') write.csv(as.data.frame(dmr), 'dmr_results.csv') # Annotate with genomic features library(genomation) gene_obj <- readTranscriptFeatures('genes.bed') annotateWithGeneParts(as(dmr, 'GRanges'), gene_obj) ``` ## Parameter Recommendations | Step | Parameter | Value | |------|-----------|-------| | Trim Galore | default | Recommended for BS-seq | | Bismark | --parallel | 4 (per sample parallelization) | | methylKit | lo.count | 10 (minimum coverage) | | methylKit | difference | 25 (% methylation difference) | | methylKit | qvalue | 0.01 | | DMR tiles | win.size | 500-1000 bp | ## Troubleshooting | Issue | Likely Cause | Solution | |-------|--------------|----------| | Low mapping rate | Normal for BS-seq | Expect 40-70% | | Low conversion | Failed bisulfite treatment | Check spike-in controls | | Few DMRs | Low coverage, small differences | Increase sequencing, relax thresholds | | Biased positions | M-bias | Trim 10bp from read ends | ## Complete Pipeline Script ```bash #!/bin/bash set -e THREADS=4 GENOME="genome/" SAMPLES="control_1 control_2 treated_1 treated_2" OUTDIR="methylation_results" mkdir -p ${OUTDIR}/{trimmed,aligned,deduplicated,methylation,qc} # Step 1: QC for sample in $SAMPLES; do trim_galore --paired --fastqc -o ${OUTDIR}/trimmed/ \ ${sample}_R1.fastq.gz ${sample}_R2.fastq.gz done # Step 2: Alignment for sample in $SAMPLES; do bismark --genome ${GENOME} \ -1 ${OUTDIR}/trimmed/${sample}_R1_val_1.fq.gz \ -2 ${OUTDIR}/trimmed/${sample}_R2_val_2.fq.gz \ -o ${OUTDIR}/aligned/ \ --parallel ${THREADS} --temp_dir tmp/ done # Step 3: Deduplication for sample in $SAMPLES; do deduplicate_bismark --bam -p \ -o ${OUTDIR}/deduplicated/ \ ${OUTDIR}/aligned/${sample}_R1_val_1_bismark_bt2_pe.bam done # Step 4: Methylation calling for sample in $SAMPLES; do bismark_methylation_extractor --paired-end --comprehensive \ --bedGraph --cytosine_report \ --genome_folder ${GENOME} \ -o ${OUTDIR}/methylation/ \ ${OUTDIR}/deduplicated/${sample}_R1_val_1_bismark_bt2_pe.deduplicated.bam done bismark2report echo "Pipeline complete. Run R script for DMR analysis." ``` ## Related Skills - methylation-analysis/bismark-alignment - Bismark parameters - methylation-analysis/methylation-calling - Calling details - methylation-analysis/methylkit-analysis - methylKit functions - methylation-analysis/dmr-detection - DMR algorithms --- Source attribution: GPTomics Original project: https://github.com/GPTomics/bioSkills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Mega AI Hub resource metadata Creator / publisher: GPTomics Original project: https://github.com/GPTomics/bioSkills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Data & Analytics, Education & Research Roles: Business Intelligence Analyst, Data Scientist, Researcher Specialties: Frontend, Playwright Tools: Bismark, Fastp, Methylkit Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: fe8d48f1aac934c4580a0bafe16d7a8af1ac973f47eee849e5261d1dd4a20c8a Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.