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[email protected], m6ADRACH@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 probabilitymodified (model posterior that any reads at the site are modified) AND modratio (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 rna004130bps[email protected] --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 --outdir m6anetdata/ -- feature extraction
• CLI: m6anet inference --inputdir m6anetdata/ --outdir m6anetresults/ -- 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 probabilitymodified estimates; sites with <20 reads at probabilitymodified > 0.9 are likely false positives driven by per-read noise. The per-site modratio column (the fraction of reads called modified at the site) is the stoichiometry-aware signal — informative even when per-site probabilitymodified 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

Decision Tree by Scenario

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 probabilitymodified and modratio.

# Step 1: Basecalling (RNA004 chemistry example).
dorado download --model [email protected]

dorado basecaller \
    [email protected] \
    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.siteproba.csv to retain high-confidence m6A site calls; report modratio (per-site stoichiometry) alongside probability_modified (per-site model posterior).

Approach: Read the CSV; filter by nreads >= 20 (conservative) or >= 50 (stringent) AND probabilitymodified >= 0.9 (high-precision threshold per Hendra 2022); inspect modratio as the per-site stoichiometry estimate. The full column set is transcriptid, transcriptposition, nreads, probabilitymodified, kmer, modratio.

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='\t', index=False)

probabilitymodified is the per-site model posterior (multiple-instance-learning aggregation over reads); modratio is the per-site stoichiometry (fraction of reads called modified above the model's internal per-read threshold). For stoichiometry claims, modratio is the right column. For genuine per-read output (probability per individual read), enable --readprobathreshold <T> during inference (NOT --perreadprobathreshold); 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 diffmodrate (per-site rate difference) AND p-value AND posterior probability. Filter for high-confidence differential by combining diffmodrate >= 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 GMMlogitpvalue, KSdwellpvalue, KSintensitypvalue, MWdwellpvalue, MWintensitypvalue. 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 probabilitymodified > 0.9 at sites with nreads < 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 eventlevelmean 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[email protected], m6A[email protected], 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

Quantitative Thresholds

Common Errors

Anticipated Reviewer Pushback

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