bio-clip-seq-m6a-clip

name	bio-clip-seq-m6a-clip
description	Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.
tool_type	mixed
primary_tool	miCLIP2

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: only ~44% of DART-seq C->U mutations fall within DRACH motifs (Guo 2025 reanalysis of the DART-seq data), 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 deamination of unmodified A to inosine (reads as G)	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 (Qin 2022)	Nanopore + neural network	Single-nucleotide

Methodology evolves; verify the latest benchmark publications and reviews. 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 has high AUC on HEK293T and outperforms EpiNano and Tombo on the Hendra 2022 benchmark.

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% of DART edits within DRACH	Guo 2025
m6Anet vs miCLIP2	~75% concordance at high-coverage sites	Hendra 2022
Antibody-based methods	High discordance between antibody lots	Practitioner reports

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 yields substantially higher-complexity libraries from less input than 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); 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 (Ye 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)
• Guo W et al 2025 Mol Cell 85:1233 (single-molecule m6A; DART-seq DRACH reanalysis, 44% within DRACH)
• Hendra C et al 2022 Nat Methods 19:1590 (m6Anet)
• Liu H 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)
• Qin H et al 2022 Genome Biol 23:25 (DENA)
• Zhang Z et al 2019 Sci Adv 5:eaax0250 (m6A-REF-seq)
• Ye H et al 2024 Nucleic Acids Res 52:4830 (m6ACali, MeRIP-seq calibration)

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