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 FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-ribo-seq-orf-detection --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.
## Version Compatibility Reference examples tested with: BioPython 1.83+, DESeq2 1.42+, 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 - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - 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. # ORF Detection **"Detect translated ORFs from my Ribo-seq data"** → Identify actively translated open reading frames including uORFs and novel ORFs using 3-nucleotide periodicity as evidence of active translation. - CLI: `RiboCode` for periodicity-based ORF detection - R: `ORFik` for ORF quantification and annotation ## RiboCode Workflow **Goal:** Detect actively translated ORFs from Ribo-seq data using 3-nucleotide periodicity as evidence of translation. **Approach:** Prepare transcript annotations, then run RiboCode with specified read lengths to identify ORFs with significant periodicity. ```bash # Step 1: Prepare annotation prepa
- Version Compatibility
- 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
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 FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-ribo-seq-orf-detection --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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.