M6a clip

Version Compatibility

Reference examples tested with: miCLIP2 pipeline (Kortel 2021), m6Aboost 1.0+, GLORI-tools (Liu 2023), Bullseye 1.0+, m6Anet 2.1+, EpiNano 1.2+, MeRIPSeq tools (exomePeak2 1.16+), nanocompore 1.0+, samtools 1.19+, bedtools 2.31+, R 4.3+.

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

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws unexpected errors, introspect the installed package and adapt the example to match the actual API rather than retrying.

m6A CLIP (N6-Methyladenosine Profiling)

"Map m6A modifications at single-nucleotide resolution" -> Profile m6A on RNA using one of three orthogonal approaches: antibody-based UV-CL (miCLIP/miCLIP2), antibody-free chemical conversion (GLORI), or enzyme-fusion editing (DART-seq with APOBEC1-YTH). Nanopore direct RNA (m6Anet, nanocompore, EpiNano) provides a fourth modality. The DRACH consensus motif (D=A/G/U, R=A/G, A=m6A, C=C, H=A/C/U) constrains plausible sites but is not exclusive - only a fraction of DRACH instances are methylated; some m6A sites occur outside DRACH. Cross-method discordance is real: DART-seq C->U mutations are 44% within DRACH motifs (Liu 2023), suggesting many DART sites are not consensus m6A. GLORI is the new (2023) gold standard for stoichiometric single-base m6A.

• CLI (miCLIP2 antibody-based): iCount or custom pipeline through truncation + C->T mutation analysis; then m6Aboost ML scoring
• CLI (GLORI antibody-free): GLORI-tools Python pipeline; output is per-A m6A fraction (stoichiometric)
• CLI (DART-seq editing): Bullseye or SAILOR pipeline; identify C->U editing sites; filter by DRACH; cross-check against APOBEC1-only control
• CLI (m6Anet nanopore): m6anet inference on nanopolish eventalign output; per-site probability of m6A
• CLI (MeRIP-seq peak calling): exomePeak2 in R for peak-level m6A from IP+input MeRIP libraries

The m6A field is rapidly evolving (2022-2026); single-base methods (GLORI, m6Anet) have largely replaced antibody-based miCLIP for new studies, but miCLIP2 remains the most common because of its eCLIP-like processing pipeline. Cross-method discordance means high-confidence m6A reporting should require concordance across at least two orthogonal methods.

Methods Taxonomy

Method	Detection chemistry	Resolution	Antibody	Stoichiometry	Strength	Fails when
MeRIP-seq (Dominissini 2012, Meyer 2012)	Anti-m6A IP + RNA-seq	Peak (50-300 nt)	Yes	No	Original m6A method; widely used	Low resolution; cannot distinguish m6A from m6Am
miCLIP (Linder 2015)	Anti-m6A + UV-CL + RT mutation	Single-nucleotide (some)	Yes	No	Single-nt subset of m6A peaks	Low yield of single-nt; high false-positive rate
miCLIP2 (Kortel 2021)	Anti-m6A + UV-CL + improved library	Single-nucleotide	Yes	No	Higher complexity; ML-classified (m6Aboost)	Antibody specificity remains issue
GLORI (Liu 2023)	Glyoxal + nitrite chemical conversion of A to N1-methyl-2-amino-6-oxopurine	Single-nucleotide	No (chemical)	Yes (stoichiometric)	Stoichiometric m6A fraction per site	New; less validated; harsh conversion may damage rare RNAs
DART-seq (Meyer 2019)	APOBEC1-YTH fusion edits C adjacent to m6A	Single-nucleotide (offset)	No	No	Antibody-free; in vivo	Only 44% of edits in DRACH motifs; high false positive
m6A-CLIP (Ke 2015)	Anti-m6A + UV-CL	Peak	Yes	No	Original UV-CL approach	Predecessor to miCLIP
m6Anet (Hendra 2022)	Nanopore direct RNA + neural net	Single-nucleotide (DRACH constraint)	No	Probability	Direct RNA; preserves isoform context	Restricted to DRACH; needs high coverage per site
EpiNano (Liu 2019)	Nanopore + SVM on signal features	Single-nucleotide	No	No	Pioneer nanopore m6A	Lower accuracy than m6Anet on benchmark
nanocompore (Leger 2021)	Nanopore + statistical test wt vs Mettl3-KO	Single-nucleotide	No	No	Comparative; high specificity	Requires KO control sample
DENA (Zhong 2024)	Nanopore + transformer model	Single-nucleotide	No	No	Single-sample tool	Newer; less validation
FTO/ALKBH5-aware methods	Eraser perturbation	Site	No	Indirect	Validates m6A regulation	Indirect
MAZTER-seq (Garcia-Campos 2019)	RNase T1 cleavage at unmodified ACA	Site (within ACA)	No	No	Antibody-free	Restricted to ACA context (subset of DRACH)
REF-seq (Werner 2020)	Endonuclease-cleavage	Site	No	No	Antibody-free	Restricted context
m6ACali (Yang 2024)	Calibrates MeRIP / miCLIP	Site	NA	Yes (calibration)	Cross-method calibration	Postprocessing only

Methodology evolves; verify the latest benchmark publications (e.g., Tegowski 2024 Mol Cell review). The field is moving toward GLORI as the new gold standard but miCLIP2 remains the most-cited method because of its eCLIP-pipeline compatibility.

Critical Choice: Antibody-Based vs Antibody-Free

Antibody-based (MeRIP-seq, miCLIP, miCLIP2, m6A-CLIP): Anti-m6A antibody (Abcam/Synaptic Systems) immunoprecipitates m6A-bearing RNA. The antibody is the only limitation - false positives from non-specific binding to long structured RNAs (especially poly-A) and false negatives at sites with low m6A stoichiometry. Mettl3 knockout calibration is recommended.

Antibody-free chemical (GLORI): Glyoxal + nitrite converts unmodified A to a nucleotide that reads as G; m6A is protected and reads as A. Sites are detected as A->G discrepancies post-conversion. Stoichiometric (the fraction of reads showing A vs G at a position = m6A fraction). Most rigorous but chemistry is harsh - degrades very long RNAs.

Antibody-free enzymatic (DART-seq, APOBEC1-YTH): APOBEC1 cytidine deaminase fused to YTH-domain (m6A reader) edits C residues adjacent to m6A. Editing pattern (C->U) marks m6A nearby but not exactly. 44% of DART edits in DRACH; many edits are off-target.

Antibody-free nanopore (m6Anet, nanocompore, EpiNano): Direct RNA sequencing detects m6A via current signal perturbation. Preserves isoform context. m6Anet AUC 0.83 on HEK293T; outperforms EpiNano and Tombo on benchmark (Liu 2024).

Goal	Method
Stoichiometric m6A fraction per site	GLORI
eCLIP-compatible processing pipeline	miCLIP2 + m6Aboost
Isoform-resolved m6A	m6Anet (nanopore)
Cell-line comparison (KO available)	nanocompore vs Mettl3-KO
High-throughput screening	DART-seq (in vivo, no UV)
Initial discovery (low cost)	MeRIP-seq (with calibration)
Variants in m6A context	GLORI + variant-effect analysis
Combined m6A + 5'-cap m6Am	miCLIP2 (detects both with separate motifs)

DRACH Motif Constraint

The DRACH consensus (D=A/G/U, R=A/G, A=m6A, C=C, H=A/C/U) is the dominant motif at m6A sites - 70-90% of high-confidence sites fall in DRACH context. But:

• Some m6A sites occur outside DRACH (~10-20% in calibrated datasets)
• Many DRACH instances are NOT methylated (only a subset)
• Filtering for DRACH-only loses 10-20% of sites; not-filtering inflates false positives

miCLIP2 + m6Aboost (Kortel 2021) trained on Mettl3 knockout calibration data to score sites without DRACH filtering. The m6Aboost ML model is the recommended approach when DRACH-blind detection is needed.

GLORI does not filter by DRACH; the per-A m6A fraction is reported regardless of context. The non-DRACH GLORI sites (10-20%) include genuine m6A in non-canonical context.

Cross-Method Discordance

Comparison	Concordance	Source
miCLIP vs miCLIP2	~70%	Kortel 2021
miCLIP2 vs GLORI	~60% (miCLIP2 calls in GLORI)	Liu 2023
GLORI vs MeRIP-seq peaks	~50% sites in MeRIP peaks	Liu 2023
DART-seq vs GLORI	44% DART edits in DRACH; 7-28% near GLORI sites	Liu 2023
m6Anet vs miCLIP2	~75% concordance at high-coverage sites	Hendra 2022
Antibody-based methods	High discordance between antibody lots	Tegowski 2024

Reconciliation strategy: Use GLORI as the new gold standard (2023+); cross-reference with m6Anet for nanopore isoform context; treat miCLIP2 + m6Aboost as a complementary in vivo perspective; treat DART-seq as a hypothesis-generating method. Three orthogonal methods agreeing on a site = high confidence.

miCLIP2 Workflow

miCLIP2 (Kortel 2021) is the eCLIP-pipeline-compatible m6A method. It uses anti-m6A antibody + UV-CL + improved library prep that increases complexity 10x over miCLIP.

Goal: Produce a high-confidence single-nucleotide m6A site BED from anti-m6A miCLIP2 reads with antibody-false-positive suppression via m6Aboost machine learning.

Approach: Run the eCLIP-style preprocessing + STAR + UMI-dedup pipeline, call single-nt CL sites with PureCLIP using SMInput control, then apply m6Aboost (trained on Mettl3-KO calibration data) to discriminate genuine m6A sites from antibody false positives without requiring strict DRACH motif filtering.

# Step 1: Preprocessing (eCLIP-style - see clip-seq/clip-preprocessing)
umi_tools extract --bc-pattern=NNNNNNNNNN \
    --stdin=R1.fq.gz --read2-in=R2.fq.gz \
    --stdout=R1.umi.fq.gz --read2-out=R2.umi.fq.gz

cutadapt -a AGATCGGAAGAGCACACGTCT -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT \
    -q 6 -m 18 \
    -o R1.trim.fq.gz -p R2.trim.fq.gz \
    R1.umi.fq.gz R2.umi.fq.gz

# Step 2: Alignment (eCLIP-style)
STAR --runMode alignReads --genomeDir STAR_index \
    --readFilesIn R1.trim.fq.gz R2.trim.fq.gz --readFilesCommand zcat \
    --alignEndsType EndToEnd --outFilterMultimapNmax 1 --outFilterMismatchNoverReadLmax 0.04 \
    --outSAMtype BAM SortedByCoordinate

umi_tools dedup --method=unique --paired -I aligned.bam -S dedup.bam

# Step 3: Single-nt CL site detection - PureCLIP or custom
pureclip -i dedup.bam -bai dedup.bam.bai -g genome.fa \
    -ibam sminput.bam -ibai sminput.bam.bai \
    -o miCLIP2_sites.bed -or miCLIP2_regions.bed -nt 8

# Step 4: m6Aboost ML scoring (Kortel 2021)
# Requires: site BED + features (sequence context, C->T rate, truncation position)
# Trained on Mettl3 KO calibration data
# Output: m6A probability score per site
python m6aboost.py \
    --sites miCLIP2_sites.bed \
    --bam dedup.bam \
    --genome genome.fa \
    --output m6Aboost_predictions.bed

# Step 5: Filter at m6Aboost score >= 0.5 (default; tune per study)
awk '$5 >= 0.5' m6Aboost_predictions.bed > m6a_high_confidence.bed

GLORI Workflow (Antibody-Free Stoichiometric)

GLORI (Liu 2023) is the new (2023) gold-standard for stoichiometric m6A. Chemistry: glyoxal + nitrite converts unmodified A; m6A is protected.

# GLORI-tools pipeline (Liu lab, github). GLORI-tools is a multi-step Python pipeline
# (`run_GLORI.py` is the typical orchestrator); the conceptual flow below is illustrative --
# verify the exact CLI against the GLORI-tools repo before scripting.
# 1. Pre-conversion sequencing (control)
# 2. Post-conversion sequencing (treated)
# 3. GLORI-tools computes per-A m6A fraction

python run_GLORI.py \
    --input pre_conversion.bam \
    --treated post_conversion.bam \
    --reference genome.fa \
    --output glori_sites.tsv

# Output columns: chr, pos, strand, m6A_fraction, coverage, p_value
# m6A_fraction: 0.0 = unmodified; 1.0 = fully methylated
# Filter at coverage >= 20 and m6A_fraction >= 0.1
awk 'NR>1 && $5 >= 20 && $4 >= 0.1' glori_sites.tsv > glori_high_confidence.tsv

DART-seq Workflow (Editing-Based)

DART-seq (Meyer 2019) expresses APOBEC1-YTH fusion in cells; the YTH domain binds m6A, APOBEC1 edits nearby Cs.

# Bullseye pipeline (Meyer lab github)
# Requires APOBEC1-only (no YTH) control to subtract off-target editing
Bullseye \
    --ip dart_sample.bam \
    --control apobec1_only.bam \
    --reference genome.fa \
    --output dart_sites.bed

# Filter for DRACH motif overlap (44% of DART sites are in DRACH)
# Sites outside DRACH may be off-target editing
bedtools intersect -wa -u -s -a dart_sites.bed -b drach_motifs.bed > dart_drach_sites.bed

m6Anet Workflow (Nanopore)

m6Anet (Hendra 2022) is the leading nanopore direct-RNA m6A detector. Uses signal-level features in a multiple-instance learning framework.

# Step 1: nanopolish eventalign on raw nanopore signal
# (assumes basecalled FASTQ, aligned BAM, raw FAST5/POD5)
nanopolish eventalign \
    --reads basecalled.fastq \
    --bam aligned.bam \
    --genome transcriptome.fa \
    --scale-events --signal-index --samples \
    > eventalign.tsv

# Step 2: m6Anet feature extraction
m6anet dataprep \
    --eventalign eventalign.tsv \
    --out_dir m6anet_features \
    --n_processes 8

# Step 3: m6Anet inference
m6anet inference \
    --input_dir m6anet_features \
    --out_dir m6anet_out \
    --pretrained_model HEK293T_RNA002

# Output: per-site probability of m6A
# Filter at probability_modified >= 0.9 (high confidence)
awk -F'\t' 'NR>1 && $5 >= 0.9' m6anet_out/data.indiv_proba.csv > m6Anet_high.tsv

Per-Method Failure Modes

miCLIP / miCLIP2 -- Antibody specificity

Trigger: Antibody lot variation; off-target binding to structured non-methylated RNAs.

Mechanism: Anti-m6A antibody (Abcam, Synaptic Systems) has variable specificity. Long structured RNAs (especially poly-A regions, snRNAs) capture non-specifically. False-positive rate without Mettl3-KO calibration is 30-50%.

Symptom: miCLIP sites overlap with snRNAs and long ncRNAs at unexpected rates; m6Aboost predicts < 30% of sites are true m6A.

Fix: Always include Mettl3-KO calibration (m6Aboost was trained on this). Apply m6Aboost ML; do not just filter by DRACH. Or switch to antibody-free GLORI.

GLORI -- RNA degradation

Trigger: GLORI on long RNAs (> 5 kb); high glyoxal+nitrite concentration.

Mechanism: Harsh chemistry damages long RNAs; coverage at long transcripts drops 50-80% post-conversion.

Symptom: Long transcripts (e.g., Titin) have poor coverage post-GLORI; m6A sites in coding regions of long mRNAs under-called.

Fix: Use shorter conversion times for long-RNA studies (4 h vs 24 h); accept reduced power on long transcripts; cross-reference with miCLIP2 for long-RNA m6A.

DART-seq -- Off-target editing

Trigger: APOBEC1-YTH expressed in cells; no APOBEC1-only control.

Mechanism: APOBEC1 has intrinsic C->U editing activity independent of YTH-m6A binding. Without APOBEC1-only control, 30-50% of edits are off-target.

Symptom: Many DART edits in non-DRACH context (44% in DRACH per Liu 2023); GO term enrichment of edited genes is non-specific.

Fix: Always run APOBEC1-only control in parallel; subtract its edits. Filter for DRACH motif overlap when reporting. Cross-validate with miCLIP2 or GLORI.

m6Anet -- Coverage requirement

Trigger: Nanopore direct RNA on a low-input sample; per-site coverage < 20 reads.

Mechanism: m6Anet's multiple-instance learning needs >= 20 reads per DRACH position for stable probability estimate.

Symptom: Many "not enough coverage" sites in m6Anet output; gene-level coverage uneven.

Fix: Increase nanopore flowcell yield; pool replicates; restrict analysis to high-expression transcripts (TPM >= 5).

MeRIP-seq -- Peak-level resolution

Trigger: MeRIP-seq on antibody-based platforms; peak width 100-300 nt.

Mechanism: MeRIP fragments are 100-300 nt; the peak captures a region containing m6A but cannot pinpoint the exact A.

Symptom: Peak BED width > 100 nt; downstream single-nt analysis impossible.

Fix: Combine MeRIP-seq with single-nt method (GLORI, miCLIP2). Or use m6ACali (Yang 2024) for cross-method calibration.

DRACH-only filter -- Misses non-canonical m6A

Trigger: Filtered miCLIP2 / DART sites to DRACH-only.

Mechanism: 10-20% of validated m6A sites are outside DRACH context.

Symptom: Lost some validated sites; published m6A list shorter than expected.

Fix: Use m6Aboost (DRACH-blind ML) or GLORI (DRACH-blind chemical). Report both DRACH-filtered and unfiltered sets.

Cross-method discordance frustration

Trigger: Three methods produce three different m6A site lists; user wants ONE truth.

Mechanism: Methods have different chemistries, sensitivities, and biases. They are not interchangeable. Discordance is real biology + technical.

Symptom: Two papers on the same RNA report different m6A sites.

Fix: Triangulate. Report (a) high-confidence sites from any single rigorous method (GLORI preferred); (b) consensus sites across 2+ methods. Acknowledge method limitations.

Decision Tree by Use Case

Scenario	Method	Why
New 2024+ study, gold-standard single-base	GLORI	Stoichiometric, antibody-free
eCLIP-pipeline-compatible processing	miCLIP2 + m6Aboost	Uses eCLIP infrastructure
Isoform-resolved m6A	m6Anet (nanopore)	Long reads preserve isoforms
Mettl3 KO calibration available	miCLIP2 + m6Aboost; OR nanocompore	KO is the m6A negative control
In vivo, no UV	DART-seq	No UV CL needed
Initial discovery (low cost)	MeRIP-seq + exomePeak2 + m6ACali	Cheapest
Long RNAs (> 5 kb)	miCLIP2 or m6Anet (not GLORI)	GLORI degrades long RNAs
Variant in m6A context	GLORI single-base + variant-effect	Stoichiometric reveals dosage
m6Am at 5' cap	miCLIP2 (distinguishes via context)	The 5'-cap-adjacent A
Bacterial m6A	Custom methods	Mammalian DRACH irrelevant
Time-course m6A dynamics	GLORI per time point	Stoichiometric quantitation
Cross-species m6A	Use method validated in that species	Generalization not assumed

Reconciliation: When Methods Disagree

Pattern	Likely cause	Action
miCLIP2 calls site; GLORI does not	Antibody false positive; or m6A fraction low	Trust GLORI for stoichiometry; flag miCLIP2 site for re-validation
GLORI calls site; miCLIP2 does not	Antibody false negative (saturation); or non-DRACH	Trust GLORI; check DRACH context of miCLIP2 site
DART edits not in DRACH	Off-target APOBEC1 editing	Subtract APOBEC1-only control; filter for DRACH
m6Anet calls site; miCLIP2 does not	Nanopore signal-specific detection; complementary	Cross-validate with GLORI; nanopore is orthogonal
MeRIP peak but no single-base call within	Peak captures multiple low-stoichiometry sites OR antibody non-specific	Use single-base method for confirmation
Discordance between antibody lots	Specificity variation	Use ENCODE-validated antibody; document lot
Cross-species method comparison	Method validated only in HEK293 / mouse	Re-validate before applying
Time-course shows decrease, methods disagree on magnitude	Stoichiometric (GLORI) vs fraction-based (miCLIP)	GLORI is quantitative; miCLIP is binary call

Operational rule for high-confidence m6A reporting: (a) Use GLORI for stoichiometric single-base sites where chemistry permits; (b) Use miCLIP2 + m6Aboost where eCLIP-pipeline compatibility is required; (c) Use m6Anet for isoform-resolved or long RNAs; (d) Require concordance across at least two orthogonal methods for any m6A site claimed in publication.

Common Errors

Error / symptom	Cause	Solution
miCLIP2 sites > 100k - more than realistic m6A count	No m6Aboost ML scoring	Apply m6Aboost; expect 10-50k high-confidence
GLORI coverage uneven across transcripts	Glyoxal harshness on long RNAs	Shorter conversion; or use other methods for long RNAs
DART edits everywhere	No APOBEC1-only subtraction	Add APOBEC1-only control
m6Anet returns "no sites"	Coverage < 20 per DRACH	Pool replicates; restrict to high-expression transcripts
10-20% sites outside DRACH	Real biology + some false positives	Report DRACH and non-DRACH separately
MeRIP peaks > 200 nt wide	Method resolution	Use single-base method for single-nt sites
Different methods give different sites	Method-specific biases	Triangulate; cross-validate
Antibody lot variation in miCLIP	Specificity drift	Document lot; use Mettl3-KO calibration
m6Am detection failing in miCLIP2	Failed at 5'-cap	Check 5'-cap adjacent context filter
Cross-method calibration confusing	m6ACali heuristic	Apply pre-publication; verify with m6Aboost

References

• Dominissini D et al 2012 Nature 485:201 (MeRIP-seq)
• Meyer KD et al 2012 Cell 149:1635 (MeRIP-seq concurrent)
• Linder B et al 2015 Nat Methods 12:767 (miCLIP)
• Ke S et al 2015 Genes Dev 29:2037 (m6A-CLIP)
• Kortel N et al 2021 Nucleic Acids Res 49:e92 (miCLIP2 + m6Aboost)
• Liu C et al 2023 Nat Biotechnol 41:355 (GLORI)
• Meyer KD 2019 Nat Methods 16:1275 (DART-seq)
• Hendra C et al 2022 Nat Methods 19:1590 (m6Anet)
• Liu Y et al 2019 Nat Commun 10:4079 (EpiNano)
• Leger A et al 2021 Nat Commun 12:7198 (nanocompore)
• Garcia-Campos MA et al 2019 Cell 178:731 (MAZTER-seq)
• Tegowski M et al 2024 Mol Cell 84:1135 (m6A methods review)
• Yang Y et al 2024 Briefings Bioinform 25:bbae001 (m6ACali, benchmark)

Related Skills

• clip-seq/clip-preprocessing - miCLIP2 uses eCLIP-style preprocessing
• clip-seq/clip-alignment - miCLIP2 uses eCLIP-style alignment
• clip-seq/crosslink-site-detection - miCLIP2 single-nt CL detection
• clip-seq/clip-peak-calling - MeRIP-seq exomePeak2 peak calling
• clip-seq/stamp-antibody-free - STAMP / DART-seq antibody-free approach
• long-read-sequencing/nanopore-methylation - Native nanopore m6A
• long-read-sequencing/basecalling - dRNA-seq basecalling
• epitranscriptomics/m6a-peak-calling - MeRIP-specific peak calling
• epitranscriptomics/m6a-differential - Differential m6A
• epitranscriptomics/m6anet-analysis - Nanopore m6Anet workflow