bio-long-read-sequencing-nanopore-methylation
Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without bisulfite conversion.
Best use case
bio-long-read-sequencing-nanopore-methylation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without bisulfite conversion.
Teams using bio-long-read-sequencing-nanopore-methylation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bio-long-read-sequencing-nanopore-methylation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-long-read-sequencing-nanopore-methylation Compares
| Feature / Agent | bio-long-read-sequencing-nanopore-methylation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without bisulfite conversion.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
## Version Compatibility
Reference examples tested with: methylKit 1.28+, minimap2 2.26+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Nanopore Methylation Calling
**"Call methylation from my Nanopore reads"** → Extract 5mC/6mA modification probabilities from basecalled reads and summarize per-site methylation frequencies.
- CLI: `modkit pileup aligned.bam methylation.bed --ref ref.fa`
## Modern Workflow (modkit)
ONT's modkit is the recommended tool for methylation analysis from basecalled data.
### Extract Methylation from BAM
```bash
# Assumes BAM has MM/ML tags from dorado basecalling
modkit pileup input.bam methylation.bed \
--ref reference.fa \
--cpg \
--combine-strands
```
### Output Format
```
# bedMethyl format
chr1 1000 1001 . 10 + 1000 1001 0,0,0 10 80.5
# Columns: chrom, start, end, name, score, strand, thickStart, thickEnd,
# itemRgb, coverage, percent_modified
```
## Basecalling with Methylation
```bash
# Dorado basecalling with 5mC model
dorado basecaller dna_r10.4.1_e8.2_400bps_sup@v4.2.0 \
pod5_dir/ \
--modified-bases 5mCG \
> calls.bam
# Index and align
samtools fastq calls.bam | \
minimap2 -ax map-ont -y reference.fa - | \
samtools sort -o aligned.bam
samtools index aligned.bam
```
## Region-Specific Analysis
```bash
# CpG islands only
modkit pileup aligned.bam cpg_islands.bed \
--ref reference.fa \
--cpg \
--include-bed cpg_islands.bed
# Promoter regions
modkit pileup aligned.bam promoters.bed \
--ref reference.fa \
--cpg \
--include-bed promoters.bed
```
## Sample Summary
```bash
# Get modification summary statistics
modkit summary aligned.bam
# Output includes:
# - Total reads with modifications
# - Modification types detected
# - Fraction modified per type
```
## Differential Methylation
```bash
# Create BED files for each sample
modkit pileup sample1.bam sample1.bed --ref ref.fa --cpg
modkit pileup sample2.bam sample2.bed --ref ref.fa --cpg
# Compare with methylKit or DSS in R
```
## Quality Considerations
- Minimum coverage: 10x for reliable calls
- Modified base probability threshold: 0.5 default, adjust as needed
- Combine strands for CpG (symmetric methylation)
## Related Skills
- long-read-sequencing/basecalling - Dorado basecalling
- methylation-analysis/methylation-calling - General methylation concepts
- methylation-analysis/dmr-detection - Differential methylationRelated Skills
bio-read-sequences
Read biological sequence files (FASTA, FASTQ, GenBank, EMBL, ABI, SFF) using Biopython Bio.SeqIO. Use when parsing sequence files, iterating multi-sequence files, random access to large files, or high-performance parsing.
bio-read-qc-umi-processing
Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep includes UMIs and accurate molecule counting is needed, such as in single-cell RNA-seq, low-input RNA-seq, or targeted sequencing to distinguish PCR from biological duplicates.
bio-read-qc-quality-reports
Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.
bio-read-qc-quality-filtering
Filter reads by quality scores, length, and N content using Trimmomatic and fastp. Apply sliding window trimming, remove low-quality bases from read ends, and discard reads below thresholds. Use when reads have poor quality tails or require minimum quality for downstream analysis.
bio-read-qc-fastp-workflow
All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.
bio-read-qc-contamination-screening
Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.
bio-read-qc-adapter-trimming
Remove sequencing adapters from FASTQ files using Cutadapt and Trimmomatic. Supports single-end and paired-end reads, Illumina TruSeq, Nextera, and custom adapter sequences. Use when FastQC shows adapter contamination or before alignment of short reads.
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.
bio-methylation-dmr-detection
Differentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types.
bio-methylation-calling
Extract methylation calls from Bismark BAM files using bismark_methylation_extractor. Generates per-cytosine reports for CpG, CHG, and CHH contexts. Use when extracting methylation levels from aligned bisulfite sequencing data for downstream analysis.
bio-methylation-bismark-alignment
Bisulfite sequencing read alignment using Bismark with bowtie2/hisat2. Handles genome preparation and produces BAM files with methylation information. Use when aligning WGBS, RRBS, or other bisulfite-converted sequencing reads to a reference genome.
bio-methylation-based-detection
Analyzes cfDNA methylation patterns for cancer detection using cfMeDIP-seq or bisulfite sequencing with MethylDackel. Identifies cancer-specific methylation signatures and performs tissue-of-origin deconvolution. Use when using methylation biomarkers for early cancer detection or minimal residual disease.