M6a Differential

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/epitranscriptomics/m6a-differentialThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment…

---
name: bio-epitranscriptomics-m6a-differential
description: Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).
tool_type: r
primary_tool: exomePeak2
---

## Version Compatibility

Reference examples tested with: exomePeak2 1.14+ (Bioconductor 3.18+), QNB 1.1.11 (GitHub `lzcyzm/QNB`), MeTDiff (GitHub, bundled with MeTPeak), RADAR 0.2.4+ (GitHub `scottzijiezhang/RADAR`), DESeq2 1.42+, edgeR 4.0+, GenomicFeatures 1.54+, ggplot2 3.5+, GenomicAlignments 1.38+, Rsubread 2.16+.

Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('exomePeak2')` then `?exomePeak2` to verify parameters
- R: `packageVersion('QNB')` then `?qnbtest` to confirm argument signature

If R throws `unused argument` or `argument is missing`, the API moved between Bioconductor minor releases; consult `?exomePeak2` directly. QNB function signature has been stable since 2017 but the GitHub source has occasional changes; pin the commit SHA.

exomePeak2's differential interface is NOT a `mode=` argument — populate the `bam_treated_ip=` and `bam_treated_input=` arguments alongside the standard `bam_ip=` / `bam_input=` (control arm) to trigger paired differential calling. `peak_calling_mode` is a separate argument controlling locus scope (`exon` | `full_transcript` | `whole_genome`). Verify against `?exomePeak2` and Bioconductor 3.20+ release notes. QNB is on GitHub only; install via `devtools::install_github('lzcyzm/QNB')`. RADAR is on GitHub only; install via `devtools::install_github('scottzijiezhang/RADAR')`; its differential workflow is `countReads -> normalizeLibrary -> adjustExprLevel -> filterBins -> diffIP -> reportResult`.

# Differential m6A Analysis

**"Compare m6A methylation between my conditions"** -> Quantify how much each m6A peak's IP/input enrichment shifts between conditions, after normalising for transcript-abundance changes (which all show up in input) and for IP-efficiency drift (which the design matrix and within-run replicates control for). Then apply effect-size filtering to distinguish real biology from the technical noise floor that all MeRIP differential methods inherit (McIntyre 2020 *Sci Rep* 10:6590: between-study m6A peak overlap is ~45% median; differential calls within that noise envelope routinely fail to replicate). Critically: a higher MeRIP signal in condition A vs B can mean (1) more transcripts of the peak-bearing gene, (2) more methylation per transcript, OR (3) higher IP efficiency in batch A — distinguishing requires careful normalisation or an orthogonal absolute-stoichiometry method.

- R: `exomePeak2::exomePeak2(bam_ip, bam_input, bam_treated_ip, bam_treated_input, ...)` -- integrated peak + differential GLM (modern default)
- R: `QNB::qnbtest(control_ip, treated_ip, control_input, treated_input)` -- beta-binomial for small N (Liu 2017 *BMC Bioinformatics* 18:387)
- R: `RADAR::diffIP()` then `reportResult()` -- Poisson-NB on peak windows with TMM normalisation (Zhang 2019 *Genome Biol* 20:294)
- R: `MeTDiff::metdiff()` -- HMM-based differential paired with MeTPeak
- R: edgeR / DESeq2 on featureCounts-on-peaks matrix -- defensible for paired symmetric designs with strong input normalisation; also the route for arbitrary batch / lot covariates exomePeak2's API does not accept

## The Single Most Important Modern Insight -- IP fold-change between conditions conflates stoichiometry change with expression change and IP efficiency drift

A higher m6A peak signal in condition A vs B can mean ANY of: (1) more transcripts of the peak-bearing gene (expression up; the input increases proportionally, so the ratio should not change — but residual normalisation noise leaks through), (2) more methylation per transcript (stoichiometry up; the real biology of interest), (3) higher IP efficiency in batch A (technical; antibody lot, IP day, RNA prep). Differential MeRIP WITHOUT per-window input-normalisation OR an orthogonal stoichiometry-aware method (GLORI Liu 2023 *Nat Biotechnol* 41:355; SAC-seq Hu 2022 *Nat Biotechnol* 40:1210; MAZTER-seq Garcia-Campos 2019 *Cell* 178:731; m6Anet per-read modification rate) cannot separate the three. exomePeak2 differential mode, QNB, RADAR, and MeTDiff all implement per-window IP/input ratio modelling, but each has different default normalisations and the choice matters. Equally critical: McIntyre 2020 *Sci Rep* 10:6590 showed that "differential" m6A peaks from MeRIP-seq routinely do not replicate between independent studies in nominally identical conditions. The empirical noise floor is high; effect-size filtering (|log2FC| >= 0.5 minimum, often >= 1) AND replicate-direction concordance (the change is consistent in direction across replicates) AND minimum N=3 per condition are needed for differential calls to survive replication. For any absolute stoichiometry claim ("this peak is 80% methylated in tumour vs 20% in normal"), require an orthogonal stoichiometry method, not MeRIP alone.

## Algorithmic Taxonomy

| Tool / mode | Mechanism | Inputs | Output | Strength | Fails when |
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Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about M6a Differential

• github.com/GPTomics/bioSkills/tree/main/epitranscriptomics/m6a-d — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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