Agent skill · Data & Analytics

bio-longread-structural-variants

Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-longread-structural-variants --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 6 KB
Bundled scripts: yes
Path: skills/bio-longread-structural-variants/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: bcftools 1.19+ Before using code patterns, verify installed versions match. If versions differ: - 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. # Structural Variant Detection **"Call structural variants from my long reads"** → Detect large deletions, insertions, inversions, duplications, and translocations with precise breakpoint resolution from ONT or PacBio alignments. - CLI: `sniffles --input aligned.bam --vcf svs.vcf`, `cuteSV aligned.bam ref.fa svs.vcf output/` ## Sniffles2 - Basic SV Calling ```bash # Call SVs from aligned BAM sniffles --input aligned.bam \ --vcf structural_variants.vcf \ --reference reference.fa \ --threads 4 ``` ## Sniffles2 - Common Options ```bash sniffles --input aligned.bam \ --vcf structural_variants.vcf \ --reference reference.fa \ --threads 8 \ --minsupport 3 \ # Min supporting reads --minsvlen 50 \ # Min SV length --mapq 20 \ # Min mapping quality --output-rnames \ # Include read names --mosaic # Detect mosaic SVs ``` ##

What's inside
Steps it walks through
  1. Version Compatibility
  2. Sniffles2 - Basic SV Calling
  3. Sniffles2 - Common Options
  4. Sniffles2 - Population Calling
  5. cuteSV - Alternative Caller
  6. cuteSV - ONT Optimized
  7. cuteSV - PacBio HiFi Optimized
  8. SVIM - Another Alternative
  9. pbsv - PacBio Specific
  10. Filter SV Calls
  11. Merge Multiple Callers
  12. Annotate SVs
  13. SV Types
  14. Key Parameters - Sniffles2
Ships with 2 files
  • examples/sv_calling.sh
  • usage-guide.md
Commands it runs
Call SVs from aligned BAM
sniffles --input aligned.bam \
Step 1: Call SVs per sample with SNF output
sniffles --input sample1.bam --snf sample1.snf --reference reference.fa
sniffles --input sample2.bam --snf sample2.snf --reference reference.fa
Step 2: Merge and genotype
sniffles --input sample1.snf sample2.snf \
cuteSV SV calling
cuteSV aligned.bam reference.fa output.vcf work_dir/ \
Settings optimized for ONT
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the bio-longread-structural-variants skill do?

Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-longread-structural-variants --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.

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