Orchestrates an end-to-end MeRIP-seq / m6A-seq analysis from raw FASTQ to differential m6A peak calls and metagene plots, chaining fastp adapter trimming, STAR splice-aware alignment (NO deduplication for non-UMI MeRIP), deepTools replicate-concordance + IP-enrichment QC, PreSeq saturation curves, exomePeak2 (transcript-aware, GC-bias-aware negative-binomial GLM) peak calling, optional MACS3 broad-peak cross-check, DRACH motif confirmation as a sanity check (NOT a per-peak filter), exomePeak2 differential calling via the four-BAM-vector interface (bam_ip + bam_input control; bam_treated_ip + bam_treated_input treatment), ChIPseeker annotation, and the canonical Guitar metagene with stop-codon enrichment as the biological QC anchor. Use when running a complete MeRIP analysis from raw reads, when chaining the constituent epitranscriptomics skills (merip-preprocessing -> m6a-peak-calling -> m6a-differential -> modification-visualization), or when wrapping the pipeline in Snakemake / Nextflow.
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.
Orchestrates an end-to-end MeRIP-seq / m6A-seq analysis from raw FASTQ to differential m6A peak calls and metagene plots, chaining fastp adapter trimming, STAR splice-aware alignment (NO deduplication for non-UMI MeRIP), deepTools replicate-concordance + IP-enrichment QC, PreSeq saturation curves, exomePeak2 (transcript-aware, GC-bias-aware negative-binomial GLM) peak calling, optional MACS3 broad-peak cross-check, DRACH motif confirmation as a sanity check (NOT a per-peak filter), exomePeak2 differential calling via the four-BAM-vector interface (bam_ip + bam_input control; bam_treated_ip + bam_treated_input treatment), ChIPseeker annotation, and the canonical Guitar metagene with stop-codon enrichment as the biological QC anchor. Use when running a complete MeRIP analysis from raw reads, when chaining the constituent epitranscriptomics skills (merip-preprocessing -> m6a-peak-calling -> m6a-differential -> modification-visualization), or when wrapping the pipeline in Snakemake / Nextflow.
[{"after_align":"Properly-paired >=85%; NO deduplication for non-UMI MeRIP"},{"after_qc":"Replicate Spearman >=0.85 IP-IP (10kb bins); plotFingerprint IP-vs-input JS >=0.5"},{"after_peaks":"DRACH enrichment P-value <1e-50 on the peak SET (sanity check, never a per-peak filter)"},{"after_metagene":"Guitar metagene shows the stop-codon/3'UTR-proximal peak; else STOP (IP failure)"}]
Version Compatibility
Reference examples tested with: STAR 2.7.11+, samtools 1.19+, fastp 0.23+, deepTools 3.5+, PreSeq 3.2+, exomePeak2 1.14.x (Bioconductor 3.18 ONLY -- deprecated in Bioc 3.19, removed in 3.20; on current Bioc install from the Bioc 3.18 archive or use a successor), MACS3 3.0+, ChIPseeker 1.38+, Guitar 2.18+, BSgenome.Hsapiens.UCSC.hg38 1.4+, TxDb.Hsapiens.UCSC.hg38.knownGene 3.18+, HOMER 4.11+.
Before using code patterns, verify installed versions match. If versions differ:
R: packageVersion('<pkg>') then ?function_name to verify parameters
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.
exomePeak2 has NO mode= or experiment_design= argument; differential is triggered by populating bam_treated_ip + bam_treated_input. MeTPeak defaults are WINDOW_WIDTH=50, SLIDING_STEP=50, FRAGMENT_LENGTH=100. MACS3 default --keep-dup is 1 and MUST be overridden to all for non-UMI MeRIP. Guitar txTxdb= is the modern argument name (older releases used txdb=).
MeRIP-seq End-to-End Pipeline
"Analyze my MeRIP-seq data from FASTQ to differential m6A peaks" -> Orchestrate read alignment (STAR splice-aware to GENOME), IP-enrichment QC (deepTools plotFingerprint, replicate Spearman, PreSeq saturation), m6A peak calling (exomePeak2 transcript-aware default, MACS3 broad as cross-check), DRACH motif sanity check (HOMER), exomePeak2 differential via the four-BAM-vector interface, ChIPseeker feature annotation, and Guitar transcript-feature metagene confirming canonical stop-codon enrichment. Defer per-skill deep treatment to epitranscriptomics/merip-preprocessing, epitranscriptomics/m6a-peak-calling, epitranscriptomics/m6a-differential, and epitranscriptomics/modification-visualization.
This is a workflow skill: it owns the chaining decisions and hand-offs, not the internals of any one step.
The governing principle
MeRIP-seq inverts several DNA-pipeline reflexes; the trustworthy callset is decided at these seams.
Do NOT deduplicate non-UMI MeRIP — duplicates are signal, not artifact. A highly methylated, highly expressed transcript legitimately produces many identical fragments; removing them (or MACS3 default ) erases the strongest m6A peaks. Keep all reads (); only dedup when a UMI is present.
--keep-dup 1
--keep-dup all
Enrichment is IP-vs-Input, and differential is a FOUR-BAM comparison. Every condition needs its own IP AND Input. exomePeak2 has no mode=/experiment_design= argument; differential is triggered simply by populating bam_treated_ip + bam_treated_input alongside the control bam_ip + bam_input.
DRACH is a peak-SET sanity check, never a per-peak filter. Confirm the motif is enriched across the whole peak set (P-value < 1e-50); post-hoc dropping individual peaks that lack a DRACH match discards real non-canonical sites and biases the callset.
The Guitar stop-codon metagene is the biological go/no-go. m6A concentrates near the stop codon / 3'UTR-proximal CDS end; if that enrichment is absent, the IP failed or the antibody is wrong — STOP, do not interpret downstream. And before comparing peak COUNTS across conditions, rarefy BAMs to a common unique-read depth (peak number scales with depth).
Standard non-UMI MeRIP: do NOT pass --umi. See epitranscriptomics/merip-preprocessing for the do-NOT-dedup rationale.
Step 2: STAR Splice-Aware Genome Alignment
STAR --runMode alignReads \
--genomeDir refs/star_index \
--readFilesIn trimmed/IP_R1.fq.gz trimmed/IP_R2.fq.gz \
--readFilesCommand zcat \
--outSAMtype BAM SortedByCoordinate \
--outFilterMultimapNmax 20 \
--outSAMattributes NH HI AS nM NM MD \
--outFileNamePrefix aligned/IP_rep1_ \
--runThreadN 12
samtools index aligned/IP_rep1_Aligned.sortedByCoord.out.bam
ln -sf IP_rep1_Aligned.sortedByCoord.out.bam aligned/IP_rep1.bam # downstream QC/peak steps consume the short ${sample}_rep${n}.bam nameln -sf IP_rep1_Aligned.sortedByCoord.out.bam.bai aligned/IP_rep1.bam.bai
Repeat for each IP and Input replicate. Align to GENOME (not transcriptome) for downstream MeRIP peak calling. Do NOT deduplicate (no UMI in standard MeRIP).
For peak-count comparison across conditions, rarefy BAMs to a common unique-read depth informed by the saturation curve before calling peaks.
Step 4: exomePeak2 Peak Calling (Per-Condition)
Goal: Produce a transcript-aware set of m6A peaks with FDR and IP/input fold-change from paired IP/Input genome BAM files, suitable as input to differential analysis, motif scanning, or downstream visualisation.
Approach: Build a TxDb from the matched GTF; pass paired IP/Input BAM vectors to exomePeak2() with txdb and genome (BSgenome) for GC correction; export BED12 + RDS to save_dir/.
library(exomePeak2)
library(GenomicFeatures)
library(BSgenome.Hsapiens.UCSC.hg38)
txdb <- makeTxDbFromGFF('refs/annotation.gtf', format='gtf')
result <- exomePeak2(
bam_ip =c('aligned/IP_rep1.bam','aligned/IP_rep2.bam','aligned/IP_rep3.bam'),
bam_input =c('aligned/Input_rep1.bam','aligned/Input_rep2.bam','aligned/Input_rep3.bam'),
txdb = txdb,
bsgenome = BSgenome.Hsapiens.UCSC.hg38,# bsgenome= (a BSgenome object) for GC correction; genome= would be the UCSC string 'hg38'
paired_end =TRUE,
library_type ='unstranded',
save_dir ='exomePeak2_output'# no experiment_name arg; output goes straight under save_dir/)
peaks <- result
nrow(peaks)# SummarizedExomePeak has no length method (would return 1); nrow = peak count
Report DRACH enrichment on the peak set as a sanity check (P-value < 1e-50 expected). NEVER post-hoc filter individual peaks by DRACH.
Step 7: exomePeak2 Differential (Control vs Treatment)
Goal: Identify m6A peaks that change between control and treatment conditions, with per-peak log2FC + FDR, using exomePeak2's integrated peak-calling + differential interface.
Approach: Populate bam_ip + bam_input with the control arm and bam_treated_ip + bam_treated_input with the treatment arm; populating the treated arms triggers differential mode (there is NO mode= argument). Apply effect-size + FDR filters downstream.
exomePeak2 has NO mode= or experiment_design= argument. Populating bam_treated_ip + bam_treated_input triggers differential output. For batch / antibody-lot covariate adjustment, fall through to featureCounts-on-peaks -> DESeq2 (see epitranscriptomics/m6a-differential).
Step 8: Peak Annotation to Transcript Features
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(rtracklayer)
peaks <- import('exomePeak2_output/Mod.bed')
anno <- annotatePeak(peaks, TxDb=TxDb.Hsapiens.UCSC.hg38.knownGene, level='transcript')
plotAnnoBar(anno)
plotDistToTSS(anno)
Flag peaks within ~50 nt of TSS as m6A-or-m6Am ambiguous (antibody cross-reactivity with PCIF1-deposited cap m6Am).
Expected pattern: peak density rises toward and peaks near the stop codon (3'UTR-proximal end of CDS). If absent, suspect IP failure or wrong antibody; do NOT proceed to downstream interpretation.
The full pipeline (incl. exomePeak2 peak calling, DRACH check, ChIPseeker annotation, Guitar metagene) is best orchestrated in Snakemake or Nextflow with the per-skill recipes from the four epitranscriptomics/ skills.
QC Checkpoints
Checkpoint
Expected
Action if Failed
Properly-paired rate (samtools flagstat)
>=85%
Check trimming and adapter contamination
Replicate Spearman within condition (10 kb bins)
>=0.85 IP-IP
Inspect divergent replicate; consider exclusion
plotFingerprint IP-vs-input JS distance
>=0.5
Suspect failed IP if lower
Saturation plateau depth
~30-60M unique reads
Sequence deeper if not plateaued
DRACH motif enrichment (HOMER, peak set)
P-value < 1e-50
Suspect IP failure or wrong antibody
Stop-codon enrichment in Guitar metagene
Clear 3'UTR-proximal peak
Suspect IP failure, wrong antibody, or non-m6A modification
5'UTR peaks fraction
Note ambiguity zone (~50 nt of TSS)
Flag as m6A-or-m6Am ambiguous; PCIF1 cross-reactivity
Strongest m6A peaks (high-expression transcripts) vanish
Deduplicated non-UMI MeRIP, or MACS3 default --keep-dup 1
Keep all reads (--keep-dup all); never dedup non-UMI MeRIP
exomePeak2 runs but gives no differential output
Expected a mode=/experiment_design= argument
Populate bam_treated_ip + bam_treated_input to trigger differential
Real non-canonical m6A sites lost
Filtered individual peaks by DRACH presence
DRACH is a peak-SET sanity check (E<1e-50), never a per-peak filter
Peak counts "differ" between conditions but it's depth
Compared raw peak numbers at unequal depth
Rarefy BAMs to a common unique-read depth before cross-condition counts
No stop-codon enrichment in the metagene
IP failure, wrong antibody, or non-m6A signal
STOP; do not interpret downstream (Guitar go/no-go)
5'UTR peaks over-interpreted as m6A
Antibody cross-reacts with cap-adjacent m6Am (PCIF1)
Flag peaks within ~50 nt of TSS as m6A-or-m6Am ambiguous
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 485:201-206. DOI 10.1038/nature11112. (MeRIP/m6A-seq; stop-codon enrichment.)
Meyer KD, Saletore Y, Zumbo P, et al (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons. Cell 149:1635-1646. DOI 10.1016/j.cell.2012.05.003.
Meng J, Lu Z, Liu H, et al (2014) A protocol for RNA methylation differential analysis with MeRIP-Seq data and the exomePeak R/Bioconductor package. Methods 69:274-281. DOI 10.1016/j.ymeth.2014.06.008. (exome-based peak calling.)
Cui X, Wei Z, Zhang L, et al (2016) Guitar: an R/Bioconductor package for gene annotation guided transcriptomic analysis of RNA-related genomic features. BioMed Research International 2016:8367534. DOI 10.1155/2016/8367534. (transcript-feature metagene.)
