m6Anet Analysis

bio-epitranscriptomics-m6anet-analysis

Detect m6A modifications from Oxford Nanopore direct RNA sequencing using m6Anet. Use when analyzing epitranscriptomic modifications from long-read RNA data without immunoprecipitation.

majiayu000/claude-skill-registry-data·0installs23starsProductivity & Planning

SKILL.md

Full skill instructions

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

Install skill

Copy and run in your terminal

npx -y skills add https://github.com/majiayu000/claude-skill-registry-data --skill m6anet-analysis

View on GitHub

data/m6anet-analysis/SKILL.md

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