bio-epitranscriptomics-m6anet-analysis

Version Compatibility

Reference examples tested with: minimap2 2.26+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

m6Anet Analysis

"Detect m6A from my Nanopore direct RNA data" → Identify m6A modifications directly from Oxford Nanopore signal-level data without immunoprecipitation using a neural network classifier.

• CLI: m6anet dataprep → m6anet inference on Nanopolish eventalign output

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

Goal: Run the complete m6Anet pipeline from FAST5 signal data to per-site m6A modification probabilities.

Approach: First extract features from FAST5 files with dataprep (signal-to-feature extraction), then run neural network inference to classify each DRACH motif site as modified or unmodified.

# 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