bio-genome-annotation-repeat-annotation
Identify and classify repetitive elements and transposable elements using RepeatModeler for de novo repeat library construction and RepeatMasker for genome-wide repeat annotation. Quantify TE expression from RNA-seq with TEtranscripts. Use when masking repeats before gene prediction or analyzing transposable element activity.
npx skills add BioTender-max/awesome-bio-agent-skills --skill repeat-annotation --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: DESeq2 1.42+, STAR 2.7.11+, matplotlib 3.8+, 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. # Repeat and Transposable Element Annotation **"Mask repeats in my genome assembly"** → Build a de novo repeat library and annotate/softmask repetitive elements as a prerequisite for gene prediction. - CLI: `RepeatModeler -database mydb` (library), `RepeatMasker -lib custom-lib.fa -xsmall assembly.fa` (masking) Identify, classify, and mask repetitive elements using RepeatModeler (de novo library construction) and RepeatMasker (genome-wide annotation). Softmasked output is a prerequisite for eukaryotic gene prediction. ## RepeatModeler (De Novo Library) RepeatModeler builds a species-specific repeat library by detecting repetitive elements de novo from the assembly. ### Build Database and Run `
- Version Compatibility
- RepeatModeler (De Novo Library)
- Build Database and Run
- Key Options
- Output
- RepeatMasker (Genome-Wide Annotation)
- With De Novo Library
- With Dfam/RepBase Library
- Combined Library (De Novo + Known)
- Output Files
- Softmasking for Gene Prediction
- TEtranscripts (TE Expression)
- Key TEtranscripts Options
- Python: Repeat Statistics
Build RepeatModeler database BuildDatabase -name my_genome -engine ncbi assembly.fasta Run RepeatModeler (this takes hours to days depending on genome size) RepeatModeler -database my_genome -pa 16 -LTRStruct Use species-specific de novo library (recommended) RepeatMasker \ assembly.fasta Use Dfam curated library for a known species Combine de novo and curated libraries for best results cat my_genome-families.fa known_repeats.fa > combined_lib.fa
What does the bio-genome-annotation-repeat-annotation skill do?
Identify and classify repetitive elements and transposable elements using RepeatModeler for de novo repeat library construction and RepeatMasker for genome-wide repeat annotation. Quantify TE expression from RNA-seq with TEtranscripts. Use when masking repeats before gene prediction or analyzing transposable element activity.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill repeat-annotation --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.