svphaser 2.0.4

Structural-variant phasing from HP-tagged long-read BAMs

SvPhaser

Haplotype‑aware structural‑variant genotyper for long‑read data

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SvPhaser phases pre‑called structural variants (SVs) using HP‑tagged long‑read alignments (PacBio HiFi, ONT Q20+, …). Think of it as WhatsHap for insertions/deletions/duplications: we do not discover SVs; we assign each variant a haplotype genotype (0|1, 1|0, 1|1, or ./.) together with a Genotype Quality (GQ) score – all in a single, embarrassingly‑parallel pass over the genome.

Key highlights

• Fast, per‑chromosome multiprocessing – linear scale‑out on 32‑core workstations.
• Deterministic Δ‑based decision tree – no MCMC or hidden state machines.
• Friendly CLI (svphaser phase …) and importable Python API.
• Seamless VCF injection – adds HP_GT, HP_GQ, HP_GQBIN INFO tags while copying the original header verbatim.
• Configurable confidence bins and publication‑ready plots (see result_images/).

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Installation

# Requires Python ≥3.9
pip install svphaser            # PyPI (coming soon)
# or
pip install git+https://github.com/your‑org/SvPhaser.git@v0.2.0

cyvcf2, pysam, typer[all], and pandas are pulled in automatically.

Quick‑start

svphaser phase \
    sample_unphased.vcf.gz \
    sample.sorted_phased.bam \
    --out-dir results/ \
    --min-support 10 \
    --major-delta 0.70 \
    --equal-delta 0.25 \
    --gq-bins "30:High,10:Moderate" \
    --threads 32

Outputs (written inside results/)

sample_unphased_phased.vcf   # original VCF + HP_* INFO fields
sample_unphased_phased.csv   # tidy table for plotting / downstream R

See docs/methodology.md and the flow‑chart below for algorithmic details.

Folder layout

SvPhaser/
├─ src/svphaser/        # importable package
│  ├─ cli.py            # Typer entry‑point
│  ├─ logging.py        # unified log setup
│  └─ phasing/
│     ├─ algorithms.py  # core maths
│     ├─ io.py          # driver & I/O
│     ├─ _workers.py    # per‑chrom processes
│     └─ types.py       # thin dataclasses
├─ tests/               # pytest suite + mini data
├─ docs/                # extra documentation
├─ result_images/       # generated plots & diagrams
└─ CHANGELOG.md

Python usage

from pathlib import Path
from svphaser.phasing.io import phase_vcf

phase_vcf(
    Path("sample.vcf.gz"),
    Path("sample.bam"),
    out_dir=Path("results"),
    min_support=10,
    major_delta=0.70,
    equal_delta=0.25,
    gq_bins="30:High,10:Moderate",
    threads=8,
)

The resulting DataFrame can be loaded from the CSV for custom analytics.

Development & contributing

1. Clone and create a virtual env:

   git clone https://github.com/your‑org/SvPhaser.git && cd SvPhaser
   python -m venv .venv && source .venv/bin/activate
   pip install -e .[dev]
   

2. Run the test‑suite & type checks:

   pytest -q
   mypy src/svphaser
   black --check src tests
   

3. Send a PR targeting the dev branch; one topic per PR.

Please read CONTRIBUTING.md (to come) for style‑guides and the DCO sign‑off.

Citing SvPhaser

If SvPhaser contributed to your research, please cite:

@software{svphaser2024,
  author       = {Pranjul Mishra, Sachin Ghadak, CeNT Lab},
  title        = {SvPhaser: haplotype‑aware SV genotyping},
  version      = {0.2.0},
  date         = {2024-06-18},
  url          = {https://github.com/your‑org/SvPhaser}
}

License

SvPhaser is released under the MIT License – see LICENSE.

📬 Contact

Developed by Team5 (BioAI Hackathon) – Sachin Gadakh & Pranjul Mishra.

Lead contacts: • [e-mail] • [e-mail]

Feedback, feature requests and bug reports are all appreciated — feel free to open a GitHub issue or reach out by e‑mail.

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Happy phasing!