Nanopore methylation

Version Compatibility

Reference examples tested with: modkit 0.3+, dorado 1.0+, minimap2 2.28+, samtools 1.19+, pb-CpG-tools 2.3+.

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

• CLI: <tool> --version then <tool> --help to confirm flags

Inputs that determine what is even possible - record them:

• The basecaller MODIFICATION model (e.g. 5mCG_5hmCG) fixes which mods can ever be piled up; it must be requested at basecall time and cannot be added later.
• The MM/ML tags must survive every fastq/alignment step or methylation is silently lost.
• modkit auto-estimates the pass threshold from the data (per run); fix it for cross-sample comparisons.

If code throws an error, introspect the installed tool (modkit pileup --help, modkit --help) and adapt the example to the actual API rather than retrying.

Nanopore Methylation

"Call methylation from my long reads" -> First confirm the MM/ML tags exist and survived alignment, then pile them into per-site bedMethyl - because methylation is a basecalling decision, not something that can be added now.

• CLI: modkit pileup aligned.bam out.bed --ref ref.fa --cpg --combine-strands

The Single Most Important Modern Insight -- Methylation Is a Basecalling Decision, and the Tags Silently Die in Alignment

Two facts gate the entire skill:

1. If the reads were not basecalled with a modification model, the signal is already gone. Mods are inferred from raw signal at basecall time (ONT Remora model in Dorado; PacBio kinetics model in jasmine) and written into the unaligned BAM as MM/ML tags. A plain BAM or a FASTQ cannot yield methylation - there is no post-hoc tool. The only fix is re-basecalling from POD5 (dorado basecaller sup,5mCG_5hmCG pod5/). The agent's FIRST move is to check the tags exist: samtools view in.bam | head | grep -o 'MM:Z:[^\t]*'.
2. The MM/ML tags silently die in a normal alignment workflow. samtools fastq drops auxiliary tags unless given -T MM,ML; minimap2 ignores them unless given -y; hard-clipping breaks MM's per-base skip counting unless -Y is set. Miss any one and the aligned BAM still sorts, indexes, and looks fine, but modkit pileup returns an empty/all-canonical bedMethyl with no error. Use dorado aligner (carries tags natively) or samtools fastq -T MM,ML | minimap2 -y -Y, and re-grep for MM:Z AFTER alignment.

End-to-End modBAM Pipeline

# 1. MODS-BASECALL (from POD5; the only step that can ever produce methylation)
dorado basecaller sup,5mCG_5hmCG pod5/ > calls.bam      # unaligned BAM, has MM/ML

# 2. TAG-PRESERVING ALIGN (route a is simplest)
dorado aligner ref.mmi calls.bam > aligned.bam                                  # a) native
samtools fastq -T MM,ML calls.bam | minimap2 -y -Y -ax lr:hq ref.fa - \
  | samtools sort -o aligned.bam && samtools index aligned.bam                  # b) manual
samtools view aligned.bam | head | grep -q 'MM:Z' && echo 'tags survived'       # verify!

# 3. PILEUP -> bedMethyl (auto-thresholds at the 10th percentile of ML; NOT 0.5)
modkit pileup aligned.bam out.bed --ref ref.fa --cpg --combine-strands
bgzip out.bed && tabix -p bed out.bed.gz

# 4. (optional) DIFFERENTIAL methylation, long-read native
modkit dmr pair -a A.bed.gz -b B.bed.gz --ref ref.fa --regions cpgislands.bed -o dmr.tsv

MM / ML Tag Spec (what the numbers mean)

• MM:Z encodes modification positions: <canonical base><strand><mod code><. or ?>,<skip counts>;. Mod codes: m=5mC, h=5hmC, a=6mA, c=4mC. The ./? modifier is load-bearing: . = skipped bases are implicitly canonical (count toward the unmodified denominator); ? = skipped bases are no-call/unknown (land in Nnocall, outside the denominator). Misreading ? as . inflates the canonical denominator and deflates methylation.
• ML:B:C is a uint8 per call: value N means probability in [N/256, (N+1)/256), so 255 is ~0.998, never exactly 1.0. Do not threshold == 1.0.

bedMethyl Columns (modkit, 18 columns)

Cols 1-9 are tab-delimited BED9; cols 10-18 are space-delimited (a parsing gotcha). The ones that matter:

Col	Name	Meaning
10	Nvalid_cov	Nmod + Ncanonical + Nother_mod (the denominator; this is "coverage" for QC)
11	percent_modified	(Nmod / Nvalid_cov) * 100 (a percent, 0-100)
12	Nmod	passing calls of this modification
13	Ncanonical	passing calls of the canonical base
14	Nother_mod	passing calls of a different mod on the same base (5hmC in a 5mC row)
16	Nfail	calls below the pass threshold (excluded from Nvalid_cov)
18	Nnocall	aligned canonical base with no mod call (e.g. ?-skipped)

For count-based DMR (DSS/methylKit) hand over Nmod and Nvalid_cov, never percent_modified.

Decision Tree by Scenario

Scenario	Recommended	Why
ONT 5mC for mammals	dorado ...sup,5mCG_5hmCG -> modkit pileup --cpg --combine-strands	mammalian 5mC is overwhelmingly CpG
Compare ONT to WGBS/array	modkit pileup --combine-mods (or --preset traditional)	WGBS conflates 5mC+5hmC; combine to match
Study 5hmC biology	keep 5mC and 5hmC split; ideally add oxBS/TAB-seq	bisulfite cannot separate them
Plants (CHG/CHH) or bacterial 6mA/4mC	all-context model + --motif (not --cpg)	methylation is not CpG-restricted there
Allele-specific methylation / imprinting	phase + haplotag -> modkit pileup --partition-tag HP	one read carries SNV phase AND methylation
PacBio HiFi 5mC	ccs --hifi-kinetics -> jasmine -> pb-CpG-tools (or modkit)	primrose is deprecated; Revio does 5mC on-instrument
Differential methylation statistics	modkit dmr (native) or export to -> methylation-analysis	DSS/methylKit for dispersion modeling
RNA modifications (m6A etc.)	-> epitranscriptomics	direct-RNA mods are out of scope here

Phased Allele-Specific Methylation

# Order is strict: align (tags preserved) -> phase+haplotag -> pileup partitioned by HP
# 1-2. Clair3/DeepVariant -> whatshap/longphase phase + haplotag (adds HP:i:1/2) -> haplotype-phasing
modkit pileup aligned.haplotagged.bam asm_out/ --ref ref.fa --cpg --combine-strands --partition-tag HP
# --partition-tag writes one UNCOMPRESSED bedMethyl per HP value into asm_out/, named by the tag
# value (e.g. 1.bed, 2.bed). bgzip + tabix each before dmr, which requires indexed inputs:
bgzip asm_out/1.bed && tabix -p bed asm_out/1.bed.gz
bgzip asm_out/2.bed && tabix -p bed asm_out/2.bed.gz
modkit dmr pair -a asm_out/1.bed.gz -b asm_out/2.bed.gz --ref ref.fa -o asm.tsv

Each haplotype gets ~half the coverage, so the per-site 10x floor effectively wants ~20x total. Imprinted loci (one haplotype ~fully methylated) are the canonical positive control.

Per-Method Failure Modes

No methylation in a plain BAM

Trigger: BAM basecalled without a mods model. Mechanism: mods are a basecall-time decision. Symptom: no MM:Z tags; modkit returns nothing. Fix: re-basecall from POD5 with a mods model; there is no post-hoc tool.

Tags died in alignment (the #1 silent killer)

Trigger: samtools fastq | minimap2 without -T MM,ML/-y. Mechanism: fastq export and minimap2 drop the tags. Symptom: valid aligned BAM, empty bedMethyl, no error. Fix: dorado aligner, or samtools fastq -T MM,ML | minimap2 -y -Y; verify MM:Z after alignment.

Methylation fraction looks too low

Trigger: misreading ? (no-call) as . (canonical). Mechanism: unscored bases counted as unmethylated. Symptom: deflated percent_modified; large Nnocall. Fix: check the MM modifier and Nnocall; modkit update-tags to convert styles if needed.

5mC understated vs WGBS

Trigger: comparing ONT-5mC-only to bisulfite. Mechanism: WGBS reads 5mC+5hmC together. Symptom: ONT looks lower by the 5hmC fraction. Fix: --combine-mods/--preset traditional to combine before comparing.

Cross-sample thresholds not comparable

Trigger: relying on modkit's auto-threshold per sample. Mechanism: the 10th-percentile cut is data-dependent. Symptom: sample-specific thresholds confound a DMR. Fix: fix a common --filter-threshold/--mod-thresholds across samples.

Quantitative Thresholds

Threshold	Source	Rationale
Nvalid_cov >= 10 per CpG	field standard	below it, single-site fractions are noisy (~20x total for phased)
modkit pass = 10th percentile of ML	modkit docs	discards the lowest-confidence ~10%; improves WGBS concordance
R10 ONT vs WGBS r ~ 0.84-0.95	benchmarks	adequate-depth site-level concordance (R10 > R9)
ML 255 ~ 0.998 (not 1.0)	SAM spec	uint8 bin [255/256, 1.0); never test == 1.0
no --min-coverage flag on pileup	modkit API	filter bedMethyl on Nvalid_cov post-hoc instead

Common Errors

Error / symptom	Cause	Solution
Empty bedMethyl / Nvalid_cov 0	tags dropped or never present	grep MM:Z; re-basecall or re-align preserving tags
modkit pileup --min-coverage unknown flag	no such flag	filter on Nvalid_cov (col 10) after pileup
modkit extract in.bam out.tsv errors	needs a subcommand	modkit extract full / modkit extract calls
modkit dmr fails on raw .bed	inputs must be indexed	bgzip + tabix -p bed first
Sparse bedMethyl with --combine-strands	records lack MN tags (old basecaller or hard-clipped)	basecall with current Dorado and align with -Y (no hard-clip)
Methylation lower than expected	? read as .; or 5hmC excluded vs WGBS	check Nnocall; --combine-mods to match WGBS
primrose not found (PacBio)	deprecated/archived	use jasmine (or Revio on-instrument 5mC)

References

• Simpson JT, Workman RE, Zuzarte PC, et al. 2017. Detecting DNA cytosine methylation using nanopore sequencing. Nat Methods 14:407-410.
• Yuen ZW-S, Srivastava A, Daniel R, et al. 2021. Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing (METEORE). Nat Commun 12:3438.
• Tse OYO, Jiang P, Cheng SH, et al. 2021. Genome-wide detection of cytosine methylation by single molecule real-time sequencing. PNAS 118(5):e2019768118.
• Cheetham SW, Kindlova M, Ewing AD. 2022. Methylartist: tools for visualizing modified bases from nanopore sequence data. Bioinformatics 38(11):3109-3112.
• SAM Optional Fields Specification (SAMtags): MM/ML base-modification tags. samtools/hts-specs.

Related Skills

• basecalling - The upstream gate: methylation must be requested at basecall time
• long-read-alignment - Carry MM/ML through with -y -Y (or use dorado aligner)
• haplotype-phasing - Phase + haplotag the BAM for allele-specific methylation
• clair3-variants - SNVs to phase before allele-specific methylation
• methylation-analysis/dmr-detection - DMR statistics downstream of bedMethyl
• methylation-analysis/methylkit-analysis - methylKit differential methylation
• epitranscriptomics/m6anet-analysis - Direct-RNA m6A (out of scope here)
• workflows/methylation-pipeline - End-to-end methylation pipeline