/genomics-sv-detection

name: genomics-sv-detection
description: Load when summarising structural variants from an SV VCF (DEL / DUP / INV / TRA) — INFO/SVTYPE-based
  classification, size classification, per-type counts. Skip when working with small SNVs / indels (use genomics-variant-calling);
  calling SVs from BAM (run Manta / Delly / Sniffles first).
trigger: structural variant, SV, Manta, Delly, Lumpy, Sniffles
tags:
- genomics
- structural-variants
- sv
- manta
- delly
- sniffles
- bnd

genomics-sv-detection

When to use

Load this skill for the file-based analysis named in the description. The function library and CLI share the same calculations; no external aligner, assembler, caller or annotation service is started.

Use from a step

from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("genomics-sv-detection")
data = read_input("input.vcf", reader=library.read_records)
result = library.analyze(data)
write_output(result, "tables/result.csv")
write_output(library.distribution_figure(result), "figures/distribution.png")

Run `examples/example_step.py` through the step runner for a small, hand-worked synthetic fixture. It asserts known summary values. The reader materializes the input in memory; use bounded FASTQ reads or pre-filter large genomic files before loading them.

API

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`read_records(path: str | Path) -> pd.DataFrame`

Read records through read_input(path, reader=library.read_records).

:param path: Existing input file in the format documented under Inputs and outputs. :returns: Parsed records as a DataFrame. :raises ValueError: Input values or file structure cannot be parsed.

`analyze(data: pd.DataFrame) -> pd.DataFrame`

Compute sv-detection summaries and return a new table, leaving data unchanged.

:param data: Records containing chrom, sv_type, sv_len, filter, size_class, genotype.

:returns: Result table with diagnostics and summary in attrs['run_info']. :raises ValueError: Required columns are absent or records are empty or invalid.

`run_info(data: pd.DataFrame, *, keep: bool=True) -> dict`

Return the analysis diagnostics and summary.

:param data: Result returned by analyze. :param keep: Keep diagnostics by default; the CLI passes False. :returns: Independent diagnostics dictionary. :raises ValueError: analyze has not populated diagnostics.

`distribution_figure(data: pd.DataFrame)`

Plot sv_len values without writing files.

:param data: Result table containing sv_len. :returns: Matplotlib Figure. :raises ValueError: The value column is absent or the table is empty.

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Methods and parameters

`analyze` returns a new DataFrame and leaves the input unchanged. `run_info(result)` returns the summary and method diagnostics. The CLI passes `keep=False` so diagnostics do not enter output tables. All calculations are deterministic; synthetic CLI demos retain seed 42.

Gotchas

• `read_records` reads INFO/SVTYPE; breakend ALT notation without that field stays UNKNOWN. There is no mate-pair resolution or SV calling.
• `run_info()["summary"]` uses absolute SVLEN and the legacy small (<1000), medium (<100000), and large size classes. Only the first sample genotype is read.

Inputs and outputs

Input files:

• File types: `.vcf`

CLI output files:

• `tables/structural_variants.csv`
• `report.md`
• `result.json`

The library writes no files. Steps use `write_output`; the CLI owns the listed artifacts. Public figure functions return matplotlib Figures and do not add new CLI outputs.

CLI

python skills/genomics/genomics-sv-detection/sv_detection.py --input input_file --output results/
python skills/genomics/genomics-sv-detection/sv_detection.py --demo --output /tmp/genomics_sv_detection_demo

See also

• `references/parameters.md`
• `references/methodology.md`
• `references/output_contract.md`

Dependencies

`numpy`, `pandas`, `matplotlib`