# Methylation (long-read) **TL;DR:** JBrowse reads DNA methylation straight from the MM/ML tags nanopore and PacBio basecallers write. This tutorial follows one dataset, HG002 nanopore reads over an imprinting center, from per-read calls to an aggregate profile to the two parental alleles pulled apart. ## Prerequisites - nothing to install to read along: every figure loads hosted data - for your own data, long reads whose BAM or CRAM already carries `MM`/`ML` modification tags, which modern ONT and PacBio basecallers write by default, plus a JBrowse instance to load them into (the [web quickstart](https://jbrowse.org/jb2-staging/docs/quickstart_web), or the [desktop quickstart](https://jbrowse.org/jb2-staging/docs/quickstart_desktop), which opens a local modBAM with no hosting step) - [modkit](https://github.com/nanoporetech/modkit/releases) for the aggregate section only, a single-binary download from its releases page ## Where the data comes from Both files are region slices of public [ONT open data](https://labs.epi2me.io/dataindex/) on the `ont-open-data` S3 bucket. - the HP1 bedMethyl from the `wf-human-variation` sup run on HG002, restricted to the SNRPN locus and to `m` (5mC) rows: https://ont-open-data.s3.amazonaws.com/giab_2025.01/analysis/wf-human-variation/sup/HG002/PAW70337/output/SAMPLE.wf_mods.1.bedmethyl.gz - the HP2 bedMethyl from that same run: https://ont-open-data.s3.amazonaws.com/giab_2025.01/analysis/wf-human-variation/sup/HG002/PAW70337/output/SAMPLE.wf_mods.2.bedmethyl.gz - the reads, from the HG002 sup basecalls, sliced to the same locus and haplotagged with `whatshap haplotag` against the phased SNP calls from that same `wf-human-variation` run: https://ont-open-data.s3.amazonaws.com/giab_2023.05/analysis/hg002/sup/PAO83395.pass.cram - the HP1 slice the figures actually load, rehosted so they load without the S3 round trip: https://jbrowse.org/demos/methylation/HG002_SNRPN_hp1.modkit.bed.gz - the haplotagged read slice beside it: https://jbrowse.org/demos/methylation/HG002_SNRPN_5mC_haplotagged.bam ## The SNRPN imprinting center At this locus on chr15, one parental allele is methylated and the other is not. That makes it a dataset with its own control: the views have to come out as two populations, and the reads and the aggregate profile have to agree on which allele is which. ## Per-read methylation from the alignments Load the modBAM as an `AlignmentsTrack`. Its `assemblyNames` must match an assembly already configured in JBrowse (see the [assemblies configuration guide](https://jbrowse.org/jb2-staging/docs/config_guides/assemblies)), and the `.bai` index sits beside the file: ```json addtrack { "type": "AlignmentsTrack", "trackId": "HG002_snrpn_5mC_reads", "name": "HG002 ONT reads (5mC, haplotagged)", "assemblyNames": ["hg38"], "adapter": { "type": "BamAdapter", "uri": "https://jbrowse.org/demos/methylation/HG002_SNRPN_5mC_haplotagged.bam" } } ``` Set **Color by... → Modifications** from the track menu and each read is painted with its own 5mC calls. Two modes are offered: one paints only the positions the MM tag reports as modified, the other (IGV's "2-color" scheme) also fills in every CpG the tag left implicit, so an unmethylated region reads as solid blue. The [alignments track guide](https://jbrowse.org/jb2-staging/docs/user_guides/alignments_track#modifications-and-methylation) covers both modes, the probability threshold, and the cytosine-context submenu. The pileup over the CpG island is an interleaved mix of methylated and unmethylated reads. Splitting it by allele is one setting, two sections below. ## Aggregate methylation with modkit bedMethyl [modkit pileup](https://nanoporetech.github.io/modkit/) collapses the per-read calls into a bedMethyl file, one row per CpG per modification type, carrying the fraction of reads that were modified. It is the compact form of the same information, and stays fast at whole-genome zoom. ```bash modkit pileup sample.bam output.bedmethyl --ref reference.fa --preset traditional bgzip output.bedmethyl tabix -p bed output.bedmethyl.gz ``` `--preset traditional` collapses 5mC and 5hmC into a single 5mC fraction (bisulfite-equivalent). Omit it to keep separate rows per modification type (`m` for 5mC, `h` for 5hmC). Passing `--partition-tag HP` writes one file per haplotype, which is what this dataset uses. Because bedMethyl is a BED file with a numeric score column, it loads through a `BedTabixAdapter` in a `MultiQuantitativeTrack` (see the [multi-quantitative track config guide](https://jbrowse.org/jb2-staging/docs/config_guides/multiquantitative_track)). JBrowse reads the modification type from the `name` column and gives each type its own subtrack: ```json addtrack { "type": "MultiQuantitativeTrack", "trackId": "HG002_snrpn_modkit_hp1", "name": "HG002 methylation HP1 (modkit)", "assemblyNames": ["hg38"], "adapter": { "type": "BedTabixAdapter", "uri": "https://jbrowse.org/demos/methylation/HG002_SNRPN_hp1.modkit.bed.gz" } } ``` The Y axis is percent methylation, each CpG a vertical bar. ## Splitting the alleles apart Each long read is a single DNA molecule, so reads that carry an `HP` haplotype tag (from WhatsHap, HiPhase, or ONT's `wf-human-variation`) can be separated by allele. Pick **Group by... → Tag...** from the track menu and enter `HP`. The dialog scans the reads in view, reports the values it found, and offers to color reads by the same tag, with that box arriving **checked**; uncheck it to keep the methylation coloring. The pileup then stacks into one band per haplotype, computed in the browser, one band methylated over the island and the other not.