bio-ribo-seq-orf-detection
Detect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation.
npx skills add majiayu000/claude-skill-registry --skill orf-detection-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.
# ORF Detection ## RiboCode Workflow ```bash # Step 1: Prepare annotation prepare_transcripts \ -g annotation.gtf \ -f genome.fa \ -o ribocode_annot # Step 2: Run RiboCode RiboCode \ -a ribocode_annot \ -c config.txt \ -l 27,28,29,30 \ -o output_prefix # config.txt format: # SampleName AlignmentFile Stranded # sample1 sample1.bam yes ``` ## One-Step RiboCode ```bash # All-in-one command RiboCode_onestep \ -g annotation.gtf \ -r riboseq.bam \ -f genome.fa \ -l 27,28,29,30 \ -o output_dir ``` ## RiboCode Output | File | Description | |------|-------------| | *_ORF_result.txt | Detected ORFs with coordinates | | *_ORF_result.html | Interactive visualization | | *_binomial_test.txt | Statistical test results | ## Parse RiboCode Results ```python import pandas as pd def load_ribocode_orfs(filepath): '''Load RiboCode ORF predictions''' df = pd.read_csv(filepath, sep='\t') # ORF categories categories = { 'annotated': df[df['ORF_type'] == 'annotated'], 'uORF': df[df['ORF_type'] == 'uORF'], 'dORF': df[df['ORF_type'] == 'dORF'], 'novel': df[df['ORF_type'].isin(['novel', 'noncoding'])] } return df, categories ``` ## Alternative: RibORF ```bash # RibORF uses random forest classifier RibORF.py
- RiboCode Workflow
- One-Step RiboCode
- RiboCode Output
- Parse RiboCode Results
- Alternative: RibORF
- Manual ORF Detection
- uORF Analysis
- ORF Categories
- ORFquant for ORF Quantification
- Installation
- Basic ORF Quantification
- P-site Corrected Quantification
- Detect and Quantify Novel ORFs
- ORFquant Output Interpretation
Step 1: Prepare annotation prepare_transcripts \ Step 2: Run RiboCode RiboCode \ config.txt format: SampleName AlignmentFile Stranded sample1 sample1.bam yes All-in-one command RiboCode_onestep \ RibORF uses random forest classifier
What does the bio-ribo-seq-orf-detection skill do?
Detect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill orf-detection-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.