bio-epitranscriptomics-m6anet-analysis

name	bio-epitranscriptomics-m6anet-analysis
description	Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.
tool_type	python
primary_tool	m6Anet

Version Compatibility

Reference examples tested with: m6anet 2.1+ (PyPI; project capitalisation m6Anet), nanopolish 0.14+, minimap2 2.26+, samtools 1.19+, Dorado 0.5+, xpore 2.1+, nanocompore 1.0.4+, ELIGOS 2 (GitHub novoalab/Eligos2), CHEUI (GitHub comprna/CHEUI), pandas 2.2+, pyranges 0.0.129+, pysam 0.22+.

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

• Python: pip show m6anet then m6anet --help, m6anet dataprep --help, m6anet inference --help
• CLI: nanopolish --version, minimap2 --version, dorado --version

If m6anet is invoked as a hyphenated command (m6anet-dataprep), the v1.x CLI is installed; v2.x uses subcommand syntax (m6anet dataprep). Pin m6anet version explicitly in any reproducible analysis. Dorado modification models are versioned independently of the basecaller (m6A_DRACH@v0.1, m6A_DRACH@v1, ...); model versions are NOT directly comparable across releases. RNA002 vs RNA004 nanopore chemistry use different signal characteristics — models trained on RNA002 do NOT transfer to RNA004.

m6Anet Direct-RNA m6A Detection

"Detect m6A from my Nanopore direct RNA data without IP" -> Run the full pipeline from POD5 / FAST5 signal to per-site m6A modification probabilities: Dorado basecalling -> minimap2 alignment to TRANSCRIPTOME (not genome) with -ax map-ont -uf -k14 -> nanopolish eventalign with --scale-events --signal-index (the m6Anet-required pair) -> m6anet dataprep -> m6anet inference. Report per-site probability_modified (model posterior that any reads at the site are modified) AND mod_ratio (per-site stoichiometry, the fraction of reads called modified) with coverage. CRITICAL: m6Anet is DRACH-only — non-DRACH sites are invisible to the model; cDNA-Nanopore data CANNOT be used (PCR erases the signal); RNA002 and RNA004 chemistry require different model versions.

• CLI: dorado basecaller rna004_130bps_sup@v5.0.0 --emit-fastq pod5/ -- modern RNA004 basecalling
• CLI: minimap2 -ax map-ont -uf -k14 --secondary=no transcriptome.fa reads.fastq -- transcriptome alignment
• CLI: nanopolish eventalign --reads reads.fastq --bam aligned.bam --genome transcriptome.fa --scale-events --signal-index -- signal-to-event (m6Anet-required flag pair)
• CLI: m6anet dataprep --eventalign eventalign.txt --out_dir m6anet_data/ -- feature extraction
• CLI: m6anet inference --input_dir m6anet_data/ --out_dir m6anet_results/ -- per-site probability + mod_ratio

The Single Most Important Modern Insight -- m6Anet scores per-site DRACH probabilities, but reliability depends on coverage and the model is DRACH-only

Hendra 2022 Nat Methods 19:1590 recommends a minimum 20-50 reads per site for stable per-site probability_modified estimates; sites with <20 reads at probability_modified > 0.9 are likely false positives driven by per-read noise. The per-site mod_ratio column (the fraction of reads called modified at the site) is the stoichiometry-aware signal — informative even when per-site probability_modified is borderline. CRITICAL: m6Anet only scores DRACH 5-mers (D=A/G/U, R=A/G, A=methylated, C=C, H=A/C/U); non-DRACH sites are invisible to the model regardless of methylation status. For non-DRACH discovery, switch to Dorado / Remora modification basecallers (genome-wide; pin the modification-model version explicitly) or to xPore / Nanocompore (which compare two conditions without an a-priori 5-mer restriction). cDNA-Nanopore data cannot be used for m6Anet because PCR amplification erases the modification signal — only direct-RNA-sequencing (ONT DRS, kit SQK-RNA002 or SQK-RNA004) preserves the modification signal in the ionic current. Cross-method comparison: m6Anet ~0.51 recall on the Pratanwanich/Hendra synthetic-mix benchmark at >=10% modification / >=10x coverage; Dorado RNA004 recall is markedly higher (reported ~0.9 at the same threshold in a 2024-2025 RNA004 benchmark) but with higher per-site FDR (~40% at low-prevalence sites) — the two figures are NOT from a head-to-head test set, so treat as bounds rather than a comparable pair. The systematic 10-tool benchmark Zhong 2023 Nat Commun 14:1906 documents broader precision-vs-recall tradeoffs. The right modern pipeline is Dorado native for first-pass discovery -> m6Anet (or CHEUI) for filtering high-confidence subset -> GLORI for orthogonal stoichiometry validation at named loci.

Algorithmic Taxonomy

Tool	Mechanism	RNA002	RNA004	Strength	Fails when
m6Anet (Hendra 2022 Nat Methods 19:1590)	Multiple-instance-learning NN over DRACH 5-mer ionic-current features from nanopolish eventalign	YES	YES (recent)	Best-in-class for DRACH m6A on RNA002; generalises across cell lines	DRACH-only; requires nanopolish eventalign upstream
CHEUI (Acera Mateos 2024 Nat Commun 15:3899)	Deep CNN on ionic-current signals predicting m6A AND m5C in single molecules	YES	Limited	Simultaneous m6A + m5C calling; single-molecule co-occurrence	Newer; smaller user base; m5C less validated than m6A
Nanocompore (Leger 2021 Nat Commun 12:7198)	2-component GMM comparing current + dwell-time between two samples	YES	Limited	Generic (any modification with signal change); replicate-aware	Requires modification-free control sample (WT vs KO or IVT)
xPore (Pratanwanich 2021 Nat Biotechnol 39:1394)	Bayesian multi-sample GMM; estimates fraction-modified per site per sample	YES	Limited	No matched WT/KO needed; multi-sample design; replicate support	Per-site coverage threshold dropout
ELIGOS / Eligos2 (Jenjaroenpun 2021 NAR 49:e7)	Compares error profile between native dRNA and unmodified controls (IVT)	YES	Limited	General-purpose; validated on yeast rRNA (95% recall); both error AND signal modes	Requires IVT or cDNA-seq control; modification-type-blind
EpiNano (Liu H et al. 2019 Nat Commun 10:4079)	Basecalling-error features as classifier features for m6A	Albacore 2.1.7 only	NO	Historical relevance; the original error-feature approach	Tied to obsolete Albacore basecaller; do NOT use for new analyses
Tombo (ont-tombo; Stoiber 2017 bioRxiv)	Signal-level model comparison; de novo against canonical RNA model OR sample-compare	YES	Limited	First general modification-detection tool; multi-modification	Less actively maintained since 2020; modern Dorado / m6Anet preferred
Dorado native modification calls (ONT; RNA004 chemistry, 2024+)

Decision Tree by Scenario

Scenario	Recommended	Why wrong choices fail
RNA002 chemistry, single-sample m6A discovery, DRACH context	m6Anet for first-pass; CHEUI for cross-check at high-confidence	EpiNano tied to obsolete basecaller; Tombo less maintained
RNA004 chemistry, first-pass screening	Dorado native modification calling -> m6Anet (or CHEUI) for filtering high-confidence subset	Single Dorado pass has ~40% FDR at low-prevalence sites
Two-condition comparison (WT vs KO)	xPore (Bayesian; no matched IVT needed) OR Nanocompore (with control sample); avoid single-condition tool with post-hoc differential	Single-tool per-condition then ad-hoc comparison loses statistical efficiency
Multi-condition (time course, multiple KOs)	xPore (multi-sample Bayesian); fall back to per-condition m6Anet + manual differential	Per-condition + ad-hoc comparison is less efficient
Need m6A AND m5C simultaneously	CHEUI (only published tool covering both)	Running m6Anet (m6A only) + separate m5C tool loses single-molecule co-occurrence
Non-DRACH site discovery	Dorado native modification calling (genome-wide) OR xPore / Nanocompore (5-mer-agnostic)	m6Anet / CHEUI are DRACH-only
Verified cDNA-Nanopore data (no DRS)	None of these tools apply — PCR erases modification signal; modification detection is impossible	Running m6Anet on cDNA returns near-zero modification probabilities everywhere
Cross-laboratory reproducibility	Pin: basecaller version, Dorado modification model version, m6Anet model SHA, nanopolish version, reference transcriptome version (GENCODE / Ensembl release)	Unpinned versions break cross-batch comparability
Absolute stoichiometry needed	NOT direct-RNA alone -- supplement with GLORI on a subset of sites for calibration	Per-read modification rate is a stoichiometry estimate but has ~5-15% per-read error; GLORI is the gold standard
Wanting to validate MeRIP peaks orthogonally	m6Anet at the MeRIP peak positions in DRACH context; report concordance	Cross-method validation by independent technologies is the strongest evidence

Methodology evolves; before any high-stakes m6Anet / direct-RNA analysis, web-search "m6anet v2 release notes", "Dorado modification model release notes", "RNA004 m6A benchmark 2024" for current best practice.

Full m6Anet Pipeline (RNA002 / RNA004 chemistry)

Goal: Take POD5 / FAST5 signal data through basecalling, transcriptome alignment, nanopolish event alignment, and m6anet inference to produce per-site m6A modification probabilities at DRACH 5-mers.

Approach: Basecall with Dorado (modern) or Guppy (legacy); align to TRANSCRIPTOME (not genome) with minimap2 -ax map-ont -uf -k14 --secondary=no; sort and index BAM; run nanopolish eventalign with the m6Anet-required --scale-events --signal-index flag pair (plus --summary / --threads for housekeeping); m6anet dataprep extracts features; m6anet inference produces per-site probability_modified and mod_ratio.

# Step 1: Basecalling (RNA004 chemistry example).
dorado download --model rna004_130bps_sup@v5.0.0

dorado basecaller \
    rna004_130bps_sup@v5.0.0 \
    pod5/ \
    --emit-fastq \
    > reads.fastq

# Step 2: Transcriptome alignment (NOT genome).
minimap2 \
    -ax map-ont \
    -uf \
    -k14 \
    --secondary=no \
    -t 12 \
    refs/transcriptome.fa \
    reads.fastq | \
samtools sort -@ 8 -o aligned.bam -

samtools index aligned.bam

# Step 3: Nanopolish eventalign with m6Anet-required --scale-events --signal-index pair.
# nanopolish uses --genome for both genome AND transcriptome FASTA (confusing nomenclature; the flag accepts either).
nanopolish index \
    -d pod5/ \
    reads.fastq

nanopolish eventalign \
    --reads reads.fastq \
    --bam aligned.bam \
    --genome refs/transcriptome.fa \
    --scale-events \
    --signal-index \
    --threads 12 \
    --summary nanopolish_summary.tsv \
    > eventalign.txt

# Step 4: m6Anet dataprep + inference. Output includes mod_ratio (per-site stoichiometry) as well as probability_modified.
m6anet dataprep \
    --eventalign eventalign.txt \
    --out_dir m6anet_data/ \
    --n_processes 8

m6anet inference \
    --input_dir m6anet_data/ \
    --out_dir m6anet_results/ \
    --n_processes 4 \
    --num_iterations 1000

ls m6anet_results/

The m6Anet-required nanopolish flags are --scale-events --signal-index; m6Anet was trained on this feature set. The additional --samples --print-read-names flags are sometimes needed by downstream tools (yanocomp, f5c-pipeline interop) but are NOT required by m6Anet itself. Transcriptome alignment with -uf forces the forward-strand interpretation (DRS is directional); -k14 is the recommended k-mer for ONT DRS. Use -ax map-ont against a TRANSCRIPTOME reference (the reference is already spliced); switch to -ax splice -uf -k14 only when aligning DRS reads to a GENOME reference (the lh3 cookbook pattern for SIRV / spike-in genomes).

Filtering and Interpreting m6Anet Results

Goal: Apply minimum-coverage and probability thresholds to data.site_proba.csv to retain high-confidence m6A site calls; report mod_ratio (per-site stoichiometry) alongside probability_modified (per-site model posterior).

Approach: Read the CSV; filter by n_reads >= 20 (conservative) or >= 50 (stringent) AND probability_modified >= 0.9 (high-precision threshold per Hendra 2022); inspect mod_ratio as the per-site stoichiometry estimate. The full column set is transcript_id, transcript_position, n_reads, probability_modified, kmer, mod_ratio.

import pandas as pd

PROBABILITY_THRESHOLD = 0.9
MIN_COVERAGE = 20

sites = pd.read_csv('m6anet_results/data.site_proba.csv')

print(sites.columns.tolist())
print(f'Total DRACH sites tested: {len(sites)}')

filtered = sites[(sites['n_reads'] >= MIN_COVERAGE) &
                 (sites['probability_modified'] >= PROBABILITY_THRESHOLD)]

print(f'High-confidence m6A sites (n_reads >= {MIN_COVERAGE}, prob >= {PROBABILITY_THRESHOLD}): {len(filtered)}')

print(f'mod_ratio summary among high-confidence: min={filtered["mod_ratio"].min():.2f} median={filtered["mod_ratio"].median():.2f} max={filtered["mod_ratio"].max():.2f}')

per_transcript = (filtered
    .groupby('transcript_id')
    .agg(n_high_conf_sites=('transcript_position', 'count'),
         mean_mod_ratio=('mod_ratio', 'mean'),
         total_coverage=('n_reads', 'sum'))
    .reset_index()
    .sort_values('n_high_conf_sites', ascending=False))

per_transcript.head(20).to_csv('m6anet_results/top_modified_transcripts.tsv', sep='\t', index=False)
filtered.to_csv('m6anet_results/high_confidence_sites.tsv', sep=, index=)

probability_modified is the per-site model posterior (multiple-instance-learning aggregation over reads); mod_ratio is the per-site stoichiometry (fraction of reads called modified above the model's internal per-read threshold). For stoichiometry claims, mod_ratio is the right column. For genuine per-read output (probability per individual read), enable --read_proba_threshold <T> during inference (NOT --per_read_proba_threshold); per-model defaults are very small (0.033379376 for HCT116_RNA002), so 0.5 is an interpretation threshold on the output column, not the CLI default.

xPore Two-Condition Differential

Goal: Compare m6A modification rate at each DRACH site between two conditions using a Bayesian multi-sample GMM; no matched IVT control needed.

Approach: Run nanopolish eventalign + xpore dataprep separately for each condition; build a YAML config listing conditions and per-condition runs; xpore diffmod tests for differential modification per site.

xpore dataprep \
    --eventalign eventalign_ctrl.txt \
    --out_dir xpore_ctrl/

xpore dataprep \
    --eventalign eventalign_treat.txt \
    --out_dir xpore_treat/

cat > xpore_config.yaml << 'EOF'
data:
  ctrl:
    rep1: xpore_ctrl/dataprep
  treat:
    rep1: xpore_treat/dataprep
out: xpore_diff_output/
EOF

xpore diffmod \
    --config xpore_config.yaml \
    --n_processes 8

head xpore_diff_output/diffmod.table

xPore reports diff_mod_rate (per-site rate difference) AND p-value AND posterior probability. Filter for high-confidence differential by combining diff_mod_rate >= 0.1 (10 percentage points) AND pval < 0.05.

Nanocompore Comparative Modification Detection

Goal: Compare current intensity and dwell time per position between WT and KO (or treated vs control) to detect modification differences via Gaussian mixture comparison.

Approach: nanopolish eventalign per condition; Nanocompore CLI compares the two; outputs per-position differential GMM logit + KS + MWU statistics.

nanocompore eventalign_collapse \
    --input eventalign_ctrl.txt \
    --output_dir nanocompore_ctrl/

nanocompore eventalign_collapse \
    --input eventalign_treat.txt \
    --output_dir nanocompore_treat/

nanocompore sampcomp \
    --file_list1 nanocompore_ctrl/out_eventalign_collapse.tsv \
    --file_list2 nanocompore_treat/out_eventalign_collapse.tsv \
    --label1 ctrl \
    --label2 treat \
    --fasta refs/transcriptome.fa \
    --outpath nanocompore_diff_output/ \
    --nthreads 8

head nanocompore_diff_output/outSampComp_results.tsv

Nanocompore reports GMM_logit_pvalue, KS_dwell_pvalue, KS_intensity_pvalue, MW_dwell_pvalue, MW_intensity_pvalue. The GMM_logit test is the primary modification signal; use as a starting filter, then inspect dwell / intensity for direction. CRITICAL: Nanocompore is modification-type-AGNOSTIC — it detects signal differences which may be any of m6A, m5C, Ψ, or other modifications. Type assignment requires orthogonal information.

Per-Method Failure Modes

cDNA-Nanopore data fed to m6Anet

Trigger: Sequencing run used cDNA-Nanopore (PCR-amplified) rather than DRS (direct RNA); m6Anet pipeline attempted on the output.

Mechanism: Modification detection from nanopore signals (m6Anet, xPore, Nanocompore, ELIGOS, Dorado native) requires the RAW ionic-current signal from direct-RNA sequencing. cDNA-Nanopore amplifies the cDNA via PCR, which erases all modification signal because PCR uses canonical bases. Only DRS protocols (kit SQK-RNA002 or SQK-RNA004; no PCR; RNA-native sequencing with the DNA RT-adapter) preserve the modification signal.

Symptom: nanopolish eventalign runs successfully but produces uniform-looking event distributions; m6anet inference returns near-zero probability_modified everywhere; no DRACH enrichment in flagged sites.

Fix: Confirm DRS protocol BEFORE running any modification-detection pipeline. Check sequencing-core run report for kit (SQK-RNA002 / SQK-RNA004) vs cDNA kit (SQK-PCS, SQK-LSK). If cDNA, modification detection is impossible; defer to MeRIP or chemistry-based methods.

m6Anet run with low-coverage sites at high probability

Trigger: Reporting m6Anet probability_modified > 0.9 at sites with n_reads < 10.

Mechanism: m6Anet's per-site probability is computed via multiple-instance aggregation over reads. With low coverage, per-read predictions are weakly aggregated and per-site probability is noisy. Hendra 2022 recommends 20-50 reads per site minimum for stable calls.

Symptom: Many "high-confidence" m6A sites at sparsely-covered transcripts; per-site probability scatter at low coverage; sites do not validate orthogonally.

Fix: Apply minimum-coverage filter (n_reads >= 20 conservative; >= 50 stringent) BEFORE the probability filter. For high-stakes sites at low coverage, report both per-site probability AND per-read modification rate; orthogonally validate with GLORI / SAC-seq.

Genome alignment instead of transcriptome alignment

Trigger: minimap2 invoked with -ax splice -uf (genome splice-aware) before m6Anet pipeline.

Mechanism: m6Anet expects reads aligned to a TRANSCRIPTOME FASTA (per-transcript coordinates); nanopolish eventalign signal alignment is per-transcript. Genome-aligned reads with splice junctions break the per-transcript signal interpretation.

Symptom: m6anet dataprep fails with chromosome / transcript ID errors; OR runs but produces few site calls; OR per-site probabilities are nonsensical.

Fix: Use minimap2 -ax map-ont -uf -k14 --secondary=no against a TRANSCRIPTOME FASTA for the m6Anet pipeline. For downstream genome-coordinate visualisation, convert site predictions back to genome coordinates via the transcript-to-genome mapping (use the GTF + a custom script or samtools lifting).

Nanopolish eventalign without m6Anet-required flags

Trigger: nanopolish eventalign invoked without --scale-events --signal-index.

Mechanism: m6Anet was trained on the specific eventalign output format produced by --scale-events --signal-index. Without these, the dataprep step fails or produces feature vectors that don't match the model's expected input. The additional --samples --print-read-names flags are commonly added because other downstream tools (yanocomp, f5c interop) need them, but m6Anet itself does NOT require them.

Symptom: m6anet dataprep fails with parse errors; OR runs but produces empty feature files; OR inference returns no calls.

Fix: Always pass --scale-events --signal-index for m6Anet; add --samples --print-read-names only if downstream tools need them. Verify against m6anet quickstart for the installed version.

f5c eventalign substituted for nanopolish without validation

Trigger: GPU-accelerated f5c eventalign used in place of nanopolish for speed; m6Anet model run on f5c output.

Mechanism: f5c is a re-implementation of nanopolish; eventalign output is numerically very close but NOT bit-identical, particularly in event_level_mean and model_kmer columns under certain edge cases. m6Anet was trained on nanopolish output; f5c is empirically usable but not officially supported and can shift per-site probabilities by 5-10% in extreme cases.

Fix: Use nanopolish unless GPU acceleration is required AND the user has validated f5c output equivalence on a control dataset. Document the eventalign source in any published analysis.

Dorado modification model version not pinned

Trigger: SKILL.md / pipeline uses Dorado for m6A modification calling without specifying the model version.

Mechanism: Dorado modification models are versioned independently of the basecaller (m6A_DRACH@v0.1, m6A_DRACH@v1.0, etc.). Calls from different model versions are NOT directly comparable; model retraining shifts per-site probabilities.

Fix: Pin the exact modification model SHA / version in every CLI invocation; record in pipeline metadata. For multi-batch projects, re-call older batches with the new model when upgrading.

Reference transcriptome version drift

Trigger: m6Anet results in batch 1 computed against GENCODE v44 transcripts; batch 2 against GENCODE v45; direct transcript-coordinate comparison.

Mechanism: Transcript IDs include version suffixes (ENST00000123456.1 vs .2); some transcripts change between releases. Site coordinates relative to transcript start may shift.

Fix: Pin GENCODE / Ensembl transcript release for ALL m6Anet runs in a project. Lift over results when upgrading.

Per-read modification rate confused with per-site probability

Trigger: Reporting m6Anet probability_modified as "the fraction of molecules modified at this site".

Mechanism: probability_modified is the model's posterior probability that the site has any modified reads (multiple-instance learning aggregation). It is NOT the per-read modification rate. Per-read modification rate is the fraction of reads at the site whose per-read posterior exceeds 0.5 (or another threshold).

Symptom: Stoichiometry claims based on per-site probability; numbers don't match orthogonal GLORI / SAC-seq stoichiometry estimates.

Fix: For stoichiometry, compute per-read modification rate from per-read output; cross-validate against GLORI at named sites.

RNA002 model run on RNA004 chemistry data

Trigger: Pipeline uses an m6Anet model trained on RNA002 to analyse RNA004 chemistry runs.

Mechanism: RNA002 and RNA004 nanopore chemistry produce different signal characteristics — different motor protein, different translocation kinetics. Models trained on RNA002 do NOT transfer to RNA004 (and vice versa).

Fix: Pin RNA chemistry version per project; use the m6Anet model trained on the matching chemistry. m6Anet v2+ has explicit RNA004 model support; verify against m6anet --help.

Reconciliation: When Methods Disagree

Pattern	Likely cause	Action
m6Anet high-confidence; GLORI does not call	Site in low-stoichiometry tail; GLORI conservatism	Trust GLORI for absolute; m6Anet probability + per-read rate informative for prevalence
m6Anet calls; MeRIP peak in same gene but different position	MeRIP fragment-level vs m6Anet single-base; methylation position offset within peak	Map m6Anet site against MeRIP peak coordinates with tolerance (~100 nt); concordance at gene level
Dorado calls; m6Anet does not at same DRACH	Dorado broader / less conservative; m6Anet model trained more strictly	Use m6Anet for high-precision; Dorado for high-recall first-pass
xPore differential strong; Nanocompore weak	xPore Bayesian multi-sample; Nanocompore pairwise GMM less powered	Trust xPore for multi-sample designs
Per-site probability high but per-read rate low	Few reads contributed strongly; rest were near-threshold	Suspect over-fit per-site at low coverage; verify with higher-coverage replicate
All ELIGOS / EpiNano sites have low signal	Older tools tied to obsolete basecallers; signal interpretation broken	Switch to modern tools (m6Anet, Dorado native)
CHEUI m6A and m5C overlap at same site	Co-occurring modifications OR cross-talk in model	CHEUI is the only published tool that handles co-occurrence; interpret as biological signal if validated
Tombo de novo and sample-compare disagree	Different baseline assumptions	Prefer sample-compare with KO / IVT control

Quantitative Thresholds

Quantity	Threshold	Source / rationale
m6Anet per-site probability_modified (high confidence)	>= 0.9	Hendra 2022 Nat Methods 19:1590 default; high precision, lower recall
m6Anet per-site probability_modified (discovery)	>= 0.7	Hendra 2022 lower threshold for less-stringent screens
m6Anet per-site minimum coverage	>= 20 reads (conservative); >= 50 (stringent)	Hendra 2022 recommendation for stable per-site estimates
Per-read modification probability interpretation threshold	>= 0.5 on output column (NOT CLI default)	m6Anet per-read posterior column convention; the CLI flag is --read_proba_threshold and per-model defaults are very small (~0.003-0.03)
xPore differential modification rate	>= 0.1 (10 percentage points)	xPore convention for meaningful biological effect
Nanocompore GMM_logit p-value	< 0.05	Standard convention; primary statistic
ELIGOS error-rate ratio threshold	>= 2 (modified vs control)	Jenjaroenpun 2021 convention
Dorado native modification probability (per-read)	>= 0.5 (default)	ONT convention; threshold tuned per model version
minimap2 k-mer for ONT DRS	14	ONT recommendation for direct-RNA
minimap2 strand flag for DRS	-uf (forward strand only)	DRS is directional; reverse-strand alignment is incorrect
Per-tool benchmark recall (>=10% mod, >=10x cov, GLORI ground truth)	m6Anet ~0.51 (Pratanwanich/Hendra synthetic-mix); Dorado RNA004 ~0.9 (reported in 2024-2025 RNA004 benchmarks; NOT head-to-head)	Hendra 2022 Nat Methods 19:1590; broader 10-tool tradeoff context in Zhong 2023 Nat Commun 14:1906
Cross-method concordance (m6Anet + GLORI + SAC-seq)	~70-85% at high-confidence sites	Approximate from per-method validation tables in Liu C 2023 Nat Biotechnol 41:355 and Hu L 2022 Nat Biotechnol 40:1210; verify against current cross-method benchmark
Common-core m6A sites (HEK293T cross-method)	~6,000-15,000	Intersection of orthogonal methods

Common Errors

Error / symptom	Cause	Solution
m6anet-dataprep not found	v1 hyphenated CLI not in installed v2	Use m6anet dataprep (subcommand syntax)
nanopolish eventalign errors with "no event annotation"	FAST5 / POD5 not indexed; OR --scale-events --signal-index missing	Run nanopolish index -d pod5/ reads.fastq first; include the m6Anet-required flag pair
m6anet inference returns empty CSV	dataprep output empty; OR all sites filtered by default coverage threshold	Check dataprep log; verify feature TSV in dataprep output dir
Reads marked as cDNA in run summary	Library prep used cDNA kit (SQK-PCS / SQK-LSK), not DRS	Modification detection impossible; switch to MeRIP / chemistry-based methods
minimap2 -ax splice instead of map-ont	Genome-splice flag used for transcriptome alignment	Switch to -ax map-ont -uf -k14
nanopolish eventalign extremely slow	Single-threaded by default	Pass --threads N
f5c output produces shifted m6Anet probabilities	f5c is numerically near-identical but not bit-identical to nanopolish	Use nanopolish unless GPU acceleration required
Per-site probability high at 5 reads	Low coverage; per-site noise	Filter by n_reads >= 20 before interpretation
xPore diffmod takes hours per condition	Single-threaded default	Pass --n_processes 8
Dorado m6A model not found	Model not downloaded	dorado download --model m6A_DRACH@v1
Transcriptome IDs missing from m6anet output	Reference mismatch between minimap2 input and m6anet config	Use same transcriptome FASTA throughout pipeline
Modification probabilities differ between Dorado releases	Model versioned independently from basecaller	Pin model version explicitly
Per-read CSV not generated	--read_proba_threshold not set (NOT )

Anticipated Reviewer Pushback

Pushback	Response
"Why m6Anet over Dorado native?"	m6Anet for high-precision DRACH calling; Dorado for high-recall first-pass; both reported when feasible
"How were non-DRACH sites handled?"	m6Anet is DRACH-only by design; non-DRACH discovery via Dorado / xPore / Nanocompore; cited limitation
"What's the per-site coverage threshold?"	n_reads >= 20 conservative per Hendra 2022; >= 50 for stringent calls
"Was cross-validation against orthogonal methods done?"	High-stakes sites cross-checked against GLORI / m6A-SAC-seq / MeRIP peaks; cross-method concordance reported
"Why RNA002 vs RNA004?"	Chemistry version pinned per project; m6Anet model selected to match (RNA002 model on RNA002 data, RNA004 on RNA004)
"Was the Dorado modification model version pinned?"	Yes — version recorded in pipeline metadata; rerun batches with new model when upgrading
"Per-site probability vs mod_ratio?"	probability_modified is the per-site model posterior; mod_ratio is the per-site stoichiometry (fraction of reads called modified) — both reported
"Is mod_ratio the same as absolute stoichiometry?"	Approximately; per-read calls have ~5-15% error; for absolute stoichiometry at named loci, cross-validate with GLORI
"Why not just use Tombo / EpiNano?"	Tombo less maintained since 2020; EpiNano tied to obsolete Albacore basecaller; m6Anet / Dorado / CHEUI are modern
"How were f5c vs nanopolish handled?"	Used nanopolish (m6Anet-trained source); did not substitute f5c

References

• Hendra C, Pratanwanich PN, Wan YK, Goh WSS, Thiery A, Göke J (2022) Detection of m6A from direct RNA sequencing using a multiple instance learning framework. Nat Methods 19(12):1590-1598. doi:10.1038/s41592-022-01666-1
• Pratanwanich PN, Yao F, Chen Y et al (2021) Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. Nat Biotechnol 39(11):1394-1402. doi:10.1038/s41587-021-00949-w
• Leger A, Amaral PP, Pandolfini L et al (2021) RNA modifications detection by comparative Nanopore direct RNA sequencing. Nat Commun 12(1):7198. doi:10.1038/s41467-021-27393-3
• Jenjaroenpun P, Wongsurawat T, Wadley TD et al (2021) Decoding the epitranscriptional landscape from native RNA sequences. Nucleic Acids Res 49(2):e7. doi:10.1093/nar/gkaa620
• Liu H, Begik O, Lucas MC et al (2019) Accurate detection of m6A RNA modifications in native RNA sequences. Nat Commun 10(1):4079. doi:10.1038/s41467-019-11713-9
• Acera Mateos P, Sethi AJ, Ravindran A et al (2024) Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications. Nat Commun 15:3899. doi:10.1038/s41467-024-47953-7
• Stoiber MH, Quick J, Egan R et al (2017) De novo identification of DNA modifications enabled by genome-guided nanopore signal processing. bioRxiv 094672. doi:10.1101/094672
• Zhong ZD, Xie YY, Chen HX et al (2023) Systematic comparison of tools used for m6A mapping from nanopore direct RNA sequencing. Nat Commun 14:1906. doi:10.1038/s41467-023-37596-5
• Liu C, Sun H, Yi Y et al (2023) Absolute quantification of single-base m6A methylation in the mammalian transcriptome using GLORI. Nat Biotechnol 41(3):355-366. doi:10.1038/s41587-022-01487-9
• Hu L, Liu S, Peng Y et al (2022) m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome. Nat Biotechnol 40(8):1210-1219. doi:10.1038/s41587-022-01243-z
• Li H (2018) Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34(18):3094-3100. doi:10.1093/bioinformatics/bty191
• Loman NJ, Quick J, Simpson JT (2015) A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat Methods 12(8):733-735. doi:10.1038/nmeth.3444
• 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(7397):201-206. doi:10.1038/nature11112
• Linder B, Grozhik AV, Olarerin-George AO, Meydan C, Mason CE, Jaffrey SR (2015) Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat Methods 12(8):767-772. doi:10.1038/nmeth.3453

Related Skills

• merip-preprocessing - Genome-aligned MeRIP for cross-validation against direct-RNA calls
• m6a-peak-calling - MeRIP fragment-level peaks for orthogonal validation comparison
• m6a-differential - Per-site direct-RNA modification rate comparable to MeRIP differential at named loci
• modification-visualization - Metagene and browser-track rendering of m6Anet site calls
• long-read-sequencing/basecalling - Upstream Dorado / Guppy basecalling fundamentals
• long-read-sequencing/long-read-alignment - General minimap2 alignment patterns
• long-read-sequencing/long-read-qc - Direct RNA QC (yield, length distribution, basecall accuracy)
• long-read-sequencing/nanopore-methylation - DNA methylation calling sibling (different chemistry, related framework)
• read-alignment/star-alignment - Splice-aware alignment for cross-method comparison context
• variant-calling/vcf-basics - General per-site variant call framework (analogue)
• rna-quantification/featurecounts-counting - For cross-method site-vs-MeRIP-peak comparison