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 | 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 | 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) | MazF (RNase) cleavage at unmodified ACA | Site (within ACA) | No | No | Antibody-free | Restricted to ACA context (subset of DRACH) | | REF-seq (Zhang 2019) | MazF endonuclease-cleavage | Site | No | No | Antibody-free | Restricted context | | m6ACali (Ye 2024) | Calibrates MeRIP | Site | NA | Yes (calibration) | Cross-method calibration | Postprocessing only | 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. ```bash # 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. ```bash # 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. ```bash # 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. ```bash # 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