install
source · Clone the upstream repo
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/bio-epitranscriptomics-m6anet-analysis" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-bio-epitranscriptomics-m6anet-analys && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/bio-epitranscriptomics-m6anet-analysis" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-bio-epitranscriptomics-m6anet-analys && rm -rf "$T"
manifest:
skills/bio-epitranscriptomics-m6anet-analysis/SKILL.mdsource content
<!--
# 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.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA
-->
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