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bio-epitranscriptomics-m6anet-analysis

Maintainer FreedomIntelligence · Last updated April 1, 2026

Nanopore direct RNA m6A detection with m6Anet deep learning.

OpenClawNanoClawTrainingEvaluationbio-epitranscriptomics-m6anet-analysis🧠 bioos extended suitebioos extended bioinformatics suitenanopore

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-epitranscriptomics-m6anet-analysis

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Documentation: https://m6anet.readthedocs.io/.
  • guppy_basecaller \ -i fast5_dir \ -s basecalled \ --flowcell FLO-MIN106 \ --kit SQK-RNA002.

Source Doc

Excerpt From SKILL.md

Align to transcriptome

minimap2 -ax map-ont -uf transcriptome.fa reads.fastq > aligned.sam


## Run m6Anet

```python
from m6anet.utils import preprocess
from m6anet import run_inference

## Preprocess: extract features from FAST5

preprocess.run(
    fast5_dir='fast5_pass',
    out_dir='m6anet_data',
    reference='transcriptome.fa',
    n_processes=8
)

Use cases

  • Use bio-epitranscriptomics-m6anet-analysis in research workflows aligned with this subject area.
  • Follow the upstream documentation for the full working procedure.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Upstream Related Skills

  • long-read-sequencing - ONT data processing
  • m6a-peak-calling - MeRIP-seq alternative
  • modification-visualization - Plot m6A sites

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