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