Bio Clip Seq 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: 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

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.

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

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.