kemal
5164124438
beta r 0.99998 vs sesame 1.28.1 (max |dbeta| 0.011), detection calls 99.1% identical, noob within 0.5% median; replicate r 0.997; all 21 control metrics pass. Spec deviations recorded (5-field control lines, pOOBAH formula, minfi sheet keys). User guide section.
56 lines
3.4 KiB
Markdown
56 lines
3.4 KiB
Markdown
# Changelog
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## Unreleased (v2)
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**Methylation control QC** (`simurg methyl qc`, `simurg methyl convert-manifest`; MET-001 … MET-018): methylation
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CSV manifests, the 21 control metrics, pOOBAH detection p-values, noob, linear/nonlinear dye bias, β and M-values,
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summary tsv/xlsx/html with a β PCA, per-sample tables; methylation folders load in `simurg qc report`;
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`qc controls --array-type methylation`. Checked against sesame (`docs/validation/V2_methylation.md`, K-026).
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## 1.1.0 — 2026-09-28 (P9, P10)
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**Packaging (P10).** `scripts/build_dist.sh` (sdist, pure wheel, manylinux_2_28 kernel wheels for x86_64 and
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aarch64), `Dockerfile`, conda recipes (`packaging/conda/`). Verified on fresh x86_64 and aarch64 VMs: pip, Docker
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and conda installs give identical outputs (`docs/validation/P10.md`). A closed stdout pipe now exits 141 silently
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instead of reporting an internal error.
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**Performance (P9).**
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Optional compiled kernels `rust/simurg-core` (PyO3): fused GenCall calling and BAF/LRR, libm maps with CPython
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semantics, normalization nearest-neighbour searches, BPM parser. Byte-identical outputs to the pure-Python paths
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(`SIMURG_PURE_PYTHON=1`); 78–80 GSA samples/min per Graviton3 core (pure Python 30), manifest loading 4× faster.
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Address coverage memoised per IDAT layout; worker state pickled once. `bench/profile_genotype.py`,
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`bench/scaling.py`. Details and scaling limits: `docs/validation/P9.md`.
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## 1.0.0 — 2026-09-28
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First release: the v1.0 goals of the master plan (§1.1), built and validated phase by phase (P0–P8).
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**Formats (P1–P2).** Readers for IDAT (plain or gzipped; encrypted v1 refused), BPM, CSV manifest, EGT, GTC v3–v5
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and sample sheets; `simurg inspect`; raw X/Y assembly into a canonical Parquet model (`simurg ingest`, resumable).
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**Signal and calling (P3–P4).** Per-bin affine normalization 1.2.0 and 1.1.2; GenCall-equivalent calling from an
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EGT with GenCall score; θ/R, BAF, LRR, LogR deviation, GC10/GC50, intensity percentiles, sex (rules 1 and 2);
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GTC v5/v3 writer (`simurg genotype`). Presets `current` and `autocall-1.6.2`.
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**QC (P5).** Control-probe metrics, threshold profiles and YAML, two-axis sample status and flags,
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BAF-regression contamination, KING-robust relatedness and Mendel errors, locus summary, threshold suggestion, a
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self-contained HTML QC report with xlsx/csv table (`simurg qc …`).
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**Outputs (P6).** Plus-strand harmonisation with an audit trail; per-sample VCF.gz + tabix or cohort BCF + CSI
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(`GT:GS:BAF:LRR`, optional INFO and intensity fields, merged multi-probe sites, indels from SourceSeq, SNV+indel
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merging, auxiliary MNV loci); PLINK 1 `.bed` and PLINK 2 `.pgen`; BAF/LRR bedGraph; final report and genotype matrix.
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**Testing (P7).** `simurg compare` (EXACT/NEAR/DISCREPANCY/UNKNOWN per field and locus class, HTML dashboard) and a
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nightly differential run against gtc2vcf, GTCtoVCF and vendor GTCs.
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**Validation (P8).** 360 GSA arrays of 120 1000 Genomes individuals vs NYGC 30x WGS (concordance 0.9934) and
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GIAB HG001 (0.9985, non-ref F1 0.997); replicate concordance 0.9966. Vendor-equivalence: 2138/2141 Omni2.5 GTCs
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byte-identical, zero unexplained VCF/PLINK differences vs gtc2vcf, GTCtoVCF and plink2.
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Documentation: `docs/user_guide.md`, `examples/`, `docs/validation/P1.md … P8.md`, `docs/known_unknowns.md`,
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`docs/messages.md`.
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Known limitations: needs an existing cluster file (training is v2); no CN/LOH/PGx yet; largest tested cohort
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2141 samples; pure NumPy speed.
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