Bio Epitranscriptomics M6anet Analysis
- 3 installs
- 1.1k repo stars
- Updated July 25, 2026
- gptomics/bioskills
Detect m6A modifications from Oxford Nanopore direct RNA sequencing using m6Anet, without immunoprecipitation.
About
Detects m6A RNA modifications from Oxford Nanopore direct RNA sequencing using m6Anet. A developer uses it when identifying epitranscriptomic modifications from long-read RNA data without antibody-based enrichment.
- m6Anet detection from Nanopore direct RNA reads
- No immunoprecipitation required
Bio Epitranscriptomics M6anet Analysis by the numbers
- 3 all-time installs (skills.sh)
- Ranked #1,661 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 3 |
|---|---|
| repo stars | ★ 1.1k |
| Last updated | July 25, 2026 |
| Repository | gptomics/bioskills ↗ |
What it does
Detect m6A modifications from Oxford Nanopore direct RNA sequencing using m6Anet, without immunoprecipitation.
Files
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.samRun 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 4Interpret 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
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