Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential metho
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.
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: 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
exomePeak2 differential (Liu 2022)
Transcript-windowed Poisson GLM with GC-bias correction; integrated peak + differential
Sister to QNB from same group; small-N alternative
Less benchmarked than QNB / exomePeak2
edgeR / DESeq2 on peak counts
Generic RNA-seq differential framework applied to featureCounts-on-peaks
peak count matrix + sample sheet
Differential peaks with log2FC + FDR
Familiar; flexible designs; well-tested in RNA-seq
Treats peak counts as RNA counts; loses IP/input pairing structure; defensible only for paired symmetric designs with strong input normalisation
Ratio-of-ratios (heuristic)
Compute per-peak log2 (IP_A / Input_A) - log2 (IP_B / Input_B) per sample, then t-test
per-peak count matrix
per-peak t-test
Transparent; no model assumptions
No multiple-testing correction; ignores overdispersion; not recommended for primary analysis
Decision Tree by Scenario
Scenario
Recommended
Why wrong choices fail
Standard 3-vs-3 paired-design MeRIP differential
exomePeak2 with bam_ip (ctrl) + bam_treated_ip (treat) and matching inputs
QNB usable but designed for smaller N; edgeR/DESeq2 loses IP/input pairing
Very small N (2 vs 2)
QNB (designed for small-sample overdispersion); supplement with exomePeak2 if possible
edgeR / DESeq2 dispersion estimation collapses; exomePeak2 GLM also struggles at N=2
Paired design (patient as blocking factor)
QNB per-pair then aggregate; OR featureCounts-on-peaks -> DESeq2 with ~patient + condition design; exomePeak2 cannot encode patient blocking via its top-level API
Unpaired analysis inflates within-group variance
Interaction design (genotype × treatment)
featureCounts-on-peaks -> DESeq2 / edgeR with interaction term; exomePeak2 top-level API is two-group only
QNB pairwise only; build interaction model from pairwise contrasts manually
Batch confounding (antibody lot, sequencing run, IP day)
Include batch as fixed effect in DESeq2 / edgeR model on featureCounts-on-peaks matrix; OR run exomePeak2 per-batch and meta-analyse
Pooling cross-batch counts without batch term attributes lot-effect to condition
Time-course differential
featureCounts-on-peaks -> DESeq2 / limma with time as numeric covariate; OR pairwise time-point exomePeak2 contrasts
featureCounts-on-peaks -> DESeq2 / edgeR with lot in design; OR run exomePeak2 per-lot then meta-analyse; ideally avoid confounding lot with condition at the experimental-design stage
Lot effect inflates false positives; exomePeak2 top-level API cannot encode lot
Validation of differential calls
Run >=2 differential methods; require concordant direction across replicates AND
log2FC
Visualising differential peaks
Volcano plot with
log2FC
Wanting to test a single gene / locus
Targeted: per-peak boxplot across replicates with condition factor; manual t-test or Wilcoxon at high-coverage peak
Whole-transcriptome differential testing wastes multiple-testing budget for single-locus questions
Methodology evolves; before any high-stakes differential analysis, web-search "exomePeak2 differential mode Bioconductor 3.20" and "MeRIP differential benchmark McIntyre" for current consensus parameters.
exomePeak2 Differential Workflow
Goal: Identify m6A peaks that differ in methylation level between conditions, controlling for transcript-abundance differences (via input normalisation) and GC bias (via internal correction), with an integrated peak-calling + differential pipeline.
Approach: Build TxDb from the matched GTF; pass control IP/input BAM vectors via bam_ip and bam_input AND treatment IP/input BAM vectors via bam_treated_ip and bam_treated_input — populating the treated arms triggers differential mode (there is no separate mode= argument). Output is per-peak log2FC + FDR.
peak_calling_mode accepts 'exon' (transcript-aware, default), 'full_transcript', or 'whole_genome'; the meaning is locus scope, NOT differential-vs-non-differential. For arbitrary covariate adjustment (batch, antibody lot, patient blocking), the exomePeak2 top-level API is insufficient — move counts into DESeq2 / edgeR via the featureCounts-on-peaks route below.
QNB Beta-Binomial for Small-N Designs
Goal: Test differential m6A at pre-called peaks using a quad-negative-binomial model that handles small-N overdispersion better than generic GLM frameworks.
Approach: Count reads in each IP and Input BAM at each peak using featureCounts or summarizeOverlaps; pass the four count matrices (ip1, ip2, input1, input2 — ip/input per group) to qnbtest().
Verify QNB argument names against ?qnbtest for the installed version; older tutorials may show different signatures.
RADAR Reproducibility-Aware Differential
Goal: Test differential m6A using a Poisson-NB framework with TMM normalisation and explicit replicate-variance modeling; useful when replicate variability is a known issue.
Approach: RADAR's documented workflow is countReads -> normalizeLibrary -> adjustExprLevel -> filterBins -> diffIP -> reportResult. CRITICAL: RADAR expects matched BAMs in bamFolder named <sample>.input.bam and <sample>.m6A.bam per replicate; the generic IP / Input naming used elsewhere must be re-conformed or symlinked. variable() is set with a data.frame, NOT a bare factor.
reportResult thresholds (cutoff = p-value cutoff; Beta_cutoff = effect-size cutoff in beta units) are RADAR-specific; convert to the project's standard reporting thresholds downstream. The aligned_radar/ directory should contain BAMs with RADAR's expected naming (<sample>.input.bam, <sample>.m6A.bam).
Volcano Plot of Differential Peaks
Goal: Visualise the differential peak set with effect size on the x-axis and statistical significance on the y-axis; flag peaks passing |log2FC| and FDR thresholds.
Approach: Standard ggplot2 volcano with colour-coded significance and threshold lines.
The caption is intentional: MeRIP differential reports CHANGES IN ENRICHMENT RATIO, NOT changes in absolute stoichiometry. For stoichiometry claims, cross-validate with GLORI / SAC-seq / m6Anet.
Per-Method Failure Modes
Reporting "hyper-methylation" without orthogonal calibration
Trigger: "Peak X shows hyper-methylation in treatment" inferred from MeRIP IP fold-change alone.
Mechanism: MeRIP IP fold-change conflates per-molecule methylation stoichiometry, transcript abundance, and IP efficiency variation between libraries. An IP fold-change increase can reflect any or all of these.
Symptom: Reported m6A "hyper-methylation" tracks RNA-seq expression changes between conditions; reverse-direction effects when properly normalised against input.
Fix: Use "increased / decreased enrichment" terminology for MeRIP-only studies. Reserve "hyper- / hypo-methylated" for studies with absolute quantification orthogonal validation (GLORI, SAC-seq, MAZTER-seq, m6Anet per-read). For high-stakes claims at named loci, run GLORI on a subset of conditions.
Effect-size threshold absence
Trigger: Reporting "1,500 differential m6A peaks" with FDR < 0.05 (uncorrected p-value or naive multiple testing) and no effect-size filter.
Mechanism: With sufficient sequencing depth, MeRIP-seq has high statistical power to detect very small (~1.1-1.2x) IP-ratio changes that lie within antibody / technical noise. McIntyre 2020 Sci Rep 10:6590 showed these changes do not replicate.
Symptom: Differential peak set has many peaks with small effect sizes; replication in an independent study recovers <30% of original calls.
Fix: Apply effect-size filter (|log2FC| >= 0.5 minimum, often >= 1) AND adjusted p-value (FDR < 0.05) AND replicate-direction concordance. Differential peaks should be reported with effect size, not just p-value. Report effect-size distribution alongside peak count.
Underpowered N=2 design
Trigger: Differential m6A study with N=2 IP and N=2 input per condition.
Mechanism: Per McIntyre 2020 and many subsequent benchmarks, MeRIP replicate variance is high; N=2 estimates of dispersion are unreliable; differential calls are unstable.
Symptom: Many "differential" peaks; volcano plot dense; small fraction replicates in held-out replicate.
Fix: Minimum N=3 per condition (per condition per IP/input arm = 12 BAMs for a 2-condition study); N=4-5 preferred for high-stakes claims. Underpowered studies should report effect-size-only filtered subsets (the most extreme peaks) and acknowledge the noise floor explicitly.
Batch confounded with condition (antibody lot, IP day)
Trigger: Control samples processed in batch 1 with antibody lot A; treatment samples processed in batch 2 with antibody lot B.
Mechanism: Anti-m6A antibody lots have batch-to-batch variability in pulldown efficiency and m6A-vs-m6Am cross-reactivity. Pooling cross-batch counts in a differential model attributes batch-effect to condition.
Symptom: "Differential" peaks at high-abundance transcripts; effect sizes track batch rather than condition; reanalysis with batch in the model removes most differential peaks.
Fix: Include antibody_lot / batch / prep_day as a fixed effect in a DESeq2 / edgeR model on featureCounts-on-peaks counts; OR run exomePeak2 separately per lot and meta-analyse the per-lot differential peak sets; OR re-design the experiment to avoid lot-condition confounding. exomePeak2's top-level API does NOT accept arbitrary covariates — DESeq2 / edgeR is the route when covariate handling is required.
exomePeak2 invoked with a fabricated mode= argument
Trigger:exomePeak2(..., mode='differential') OR exomePeak2(..., mode='diff_peak') returns "unused argument" error.
Mechanism: exomePeak2 has NO mode= argument. Differential is triggered by populating bam_treated_ip and bam_treated_input alongside the standard bam_ip and bam_input (control arm). The peak_calling_mode argument is unrelated — it accepts 'exon' | 'full_transcript' | 'whole_genome' and controls locus scope, not differential-vs-non-differential.
Fix: Populate the four BAM-vector arguments (bam_ip, bam_input, bam_treated_ip, bam_treated_input); drop any mode= reference; consult ?exomePeak2 for the authoritative signature in the installed version.
Treating peak count matrices like RNA count matrices in edgeR/DESeq2
Trigger: Compute featureCounts at peaks, build a count matrix, run edgeR / DESeq2 on the matrix as if peaks were genes.
Mechanism: Peak counts reflect both IP enrichment AND transcript abundance. Generic RNA-seq DE on peak counts mixes the two; size-factor normalisation on IP-only counts loses the input-pair information.
Fix: edgeR / DESeq2 on peak counts is defensible ONLY for paired symmetric designs where input is modelled as an offset (per-sample size factor on input counts AND per-sample size factor on IP counts, then differential on the ratio). For most uses, exomePeak2 / QNB / RADAR's purpose-built models are more appropriate.
Counting reads at peaks WITHOUT featureCounts strand-awareness
Trigger:featureCounts(...) invoked without strandSpecific= flag; or with wrong strand setting.
Mechanism: Strand-aware counting matters when the protocol is stranded (most modern MeRIP is unstranded; some are reverse-stranded). Wrong strand counts include antisense reads as if they were sense.
Fix: Verify protocol strandedness from sequencing-core notes or by inspecting featureCounts summary at a few transcripts. Pass strandSpecific=0 (unstranded), 1 (forward), or 2 (reverse) explicitly.
Reconciliation: When Differential Methods Disagree
Pattern
Likely cause
Action
exomePeak2 calls a differential peak; QNB does not
Different dispersion estimates; QNB more conservative at low coverage
Trust intersection; report concordant set as high-confidence
Most "differential" peaks fall at high-abundance housekeeping transcripts
Expression / IP-efficiency confound
Check log2FC vs input log2FC; if correlated, batch effect or expression-driven
RADAR vs exomePeak2 disagree on direction
Different normalisation (TMM vs internal)
Inspect normalisation diagnostic; choose method aligned with experimental design
Differential peaks anti-correlated with RNA-seq DE
Expression conflated with methylation in the differential model
Re-run with stronger input normalisation; consider per-peak ratio normalisation
Single differential peak survives across all methods
High-confidence call
Orthogonally validate (GLORI / SAC-seq) at the named locus
Differential calls scatter randomly across genome
Underpowered; technical noise dominates
Increase N; apply stricter effect-size filter; report null result honestly
Cross-condition peak overlap < 50% before differential
Conditions are biologically very different; OR antibody lot effect
Inspect cross-replicate concordance; check antibody lot metadata
Volcano shows extreme outliers at low-coverage peaks
Per-peak variance dominated by Poisson sampling
Filter peaks by minimum coverage (>=30 reads in IP AND input) before differential
Yes — antibody_lot included as fixed effect in DESeq2 model on featureCounts-on-peaks counts (exomePeak2's top-level API does not accept covariates); per-lot exomePeak2 + meta-analysis when DESeq2 path infeasible; lot-condition confounding assessed
"Does the differential signal track expression changes?"
Cross-checked log2FC vs input log2FC per peak; only peaks with strong IP/input ratio shift reported as differential
"Was orthogonal validation done?"
Top hits orthogonally validated via GLORI / m6Anet / per-locus assay
"Why exomePeak2 over QNB?"
exomePeak2 default for standard 3-vs-3; QNB used for small-N sensitivity analyses; both reported as concordance check
Per-window IP/input ratio normalisation; exomePeak2 internal; alternative TMM (RADAR) compared
"How does this replicate in independent studies?"
Cross-study peak overlap reported; differential subset checked against published m6A-Atlas
"Why not edgeR / DESeq2?"
Generic RNA-seq DE on peak counts loses IP/input pairing structure; used only for sensitivity analysis with paired symmetric design
References
Dominissini D, Moshitch-Moshkovitz S, Schwartz S et al (2012) Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485(7397):201-206. doi:10.1038/nature11112
Meyer KD, Saletore Y, Zumbo P, Elemento O, Mason CE, Jaffrey SR (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons. Cell 149(7):1635-1646. doi:10.1016/j.cell.2012.05.003
Liu L, Zhang SW, Huang Y, Meng J (2017) QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model. BMC Bioinformatics 18(1):387. doi:10.1186/s12859-017-1808-4
Cui X, Meng J, Zhang S, Chen Y, Huang Y (2016) A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data. Bioinformatics 32(12):i378-i385. doi:10.1093/bioinformatics/btw281
Liu L, Zhang SW, Gao F et al (2016) DRME: count-based differential RNA methylation analysis at small sample size scenario. Anal Biochem 499:15-23. doi:10.1016/j.ab.2016.01.014
Zhang Z, Zhan Q, Eckert M et al (2019) RADAR: differential analysis of MeRIP-seq data with a random effect model. Genome Biol 20(1):294. doi:10.1186/s13059-019-1915-9
Meng J, Lu Z, Liu H et al (2014) A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. Methods 69(3):274-281. doi:10.1016/j.ymeth.2014.06.008
Liu J, Zhang Z, Meng J et al (2022) exomePeak2: a peak calling and differential analysis tool for MeRIP-Seq with bias awareness. NAR Genom Bioinform 4(3):lqac046. doi:10.1093/nargab/lqac046
McIntyre ABR, Gokhale NS, Cerchietti L, Jaffrey SR, Horner SM, Mason CE (2020) Limits in the detection of m6A changes using MeRIP/m6A-seq. Sci Rep 10(1):6590. doi:10.1038/s41598-020-63355-3
Liu C, Sun H, Yi Y et al (2023) Absolute quantification of single-base m6A methylation in the mammalian transcriptome using GLORI. Nat Biotechnol 41(3):355-366. doi:10.1038/s41587-022-01487-9
Hu L, Liu S, Peng Y et al (2022) m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome. Nat Biotechnol 40(8):1210-1219. doi:10.1038/s41587-022-01243-z
Garcia-Campos MA, Edelheit S, Toth U et al (2019) Deciphering the m6A code via antibody-independent quantitative profiling. Cell 178(3):731-747.e16. doi:10.1016/j.cell.2019.06.013
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139-140. doi:10.1093/bioinformatics/btp616
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. doi:10.1186/s13059-014-0550-8
Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30(7):923-930. doi:10.1093/bioinformatics/btt656
