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.
npx skills add majiayu000/claude-skill-registry --skill m6anet-analysis-gptomics-bioskills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# m6Anet Analysis Documentation: https://m6anet.readthedocs.io/ ## Data Preparation ```bash # 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 ```python 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 ```bash # 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 ```python 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] ``` ## R
- Data Preparation
- Run m6Anet
- CLI Workflow
- Interpret Results
- Related Skills
Basecall with Guppy (requires FAST5 files) guppy_basecaller \ Align to transcriptome minimap2 -ax map-ont -uf transcriptome.fa reads.fastq > aligned.sam Preprocess m6anet dataprep \ Inference m6anet inference \
What does the bio-epitranscriptomics-m6anet-analysis skill do?
Detect m6A modifications from Oxford Nanopore direct RNA sequencing using m6Anet. Use when analyzing epitranscriptomic modifications from long-read RNA data without immunoprecipitation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill m6anet-analysis-gptomics-bioskills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.