--- name: genomics-phasing description: Load when summarising a phased VCF (output of WhatsHap / SHAPEIT5 / Eagle2) — phased fraction of het variants, phase-block N50, PS-field parsing, pipe-delimited genotype detection. Skip when the input is unphased (run a phaser first); calling small variants (use genomics-variant-calling). trigger: haplotype phasing, WhatsHap, SHAPEIT, Eagle, phasing tags: - genomics - phasing - haplotype - whatshap - shapeit - eagle - ps --- # genomics-phasing ## 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 ```python from skills._sdk.notebook import load_skill, read_input, write_output library = load_skill("genomics-phasing") 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 ### `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 phasing summaries and return a new table, leaving data unchanged. A header-only VCF yields an empty table with zero-valued summary metrics. :param data: Records containing chrom, pos, gt, is_phased, is_het, phase_set. :returns: Result table with diagnostics and summary in attrs['run_info']. :raises ValueError: Required columns are absent or records are 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 pos values without writing files. :param data: Result table containing pos. :returns: Matplotlib Figure. :raises ValueError: The value column is absent or the table is empty. ## 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 only the first sample. The legacy heterozygous classification recognizes 0/1, 1/0, 0|1 and 1|0; other allele combinations are not included. - `run_info()["summary"]["n_phase_blocks"]` excludes singleton blocks. Missing PS uses the position as a singleton phase set. - `analyze` summarizes existing phasing; it does not phase variants or estimate switch-error rates. ## Inputs and outputs Input files: - File types: `.vcf` - Accepts artifact `genomics.filtered_variants` (`vcf`) CLI output files: - `tables/phase_blocks.csv` - `tables/phased_variants.csv` - `report.md` - `result.json` - Produces artifact `genomics.phased_variants` as `tables/phased_variants.csv` (`csv`) 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 ```bash python skills/genomics/genomics-phasing/genomics_phasing.py --input input_file --output results/ python skills/genomics/genomics-phasing/genomics_phasing.py --demo --output /tmp/genomics_phasing_demo ``` ## See also - `references/parameters.md` - `references/methodology.md` - `references/output_contract.md` ## Dependencies `numpy`, `pandas`, `matplotlib`