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/bio-epitranscriptomics-m6anet-analysis

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openclaw-medical-skills
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$ npx -y skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-epitranscriptomics-m6anet-analysis --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/bio-epitranscriptomics-m6anet-analysis

Context preview

The summary Claude sees to decide when to auto-load this skill.

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SKILL.md

bio-epitranscriptomics-m6anet-analysis.SKILL.md

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COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

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This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

#

Provenance: Authenticated by MD BABU MIA

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--- name: bio-epitranscriptomics-m6anet-analysis description: Detect m6A modifications from Oxford Nanopore direct RNA sequencing using m6Anet. Use when analyzing epitranscriptomic modifications from long-read RNA data without immunoprecipitation. tool_type: python primary_tool: m6Anet measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

---

m6Anet Analysis

Documentation: https://m6anet.readthedocs.io/

Data Preparation

# Basecall with Guppy (requires FAST5 files)
guppy_basecaller \
    -i fast5_dir \
    -s basecalled \
    --flowcell FLO-MIN106 \
    --kit SQK-RNA002

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

Run m6Anet

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
)

# Run m6A inference
run_inference.run(
    input_dir='m6anet_data',
    out_dir='m6anet_results',
    n_processes=4
)

CLI Workflow

# Preprocess
m6anet dataprep \
    --input_dir fast5_pass \
    --output_dir m6anet_data \
    --reference transcriptome.fa \
    --n_processes 8

# Inference
m6anet inference \
    --input_dir m6anet_data \
    --output_dir m6anet_results \
    --n_processes 4

Interpret Results

import pandas as pd

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

# Filter high-confidence m6A sites
# probability > 0.9: High confidence threshold
m6a_sites = results[results['probability_modified'] > 0.9]

Related Skills

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

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

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Repo: FreedomIntelligence/OpenClaw-Medical-Skills