- vendor comparison (1000 Genomes Omni2.5, AutoCall 1.6.2.2): every
computed field identical in 2138/2141 samples; the 3 others trace back
to the single-locus raw-intensity rule (K-009); sex rule 1 matches all
- AutoCall 1.6.2.x leaves GC10/GC50/#calls/#no-calls at 0 in GTC v3:
new `--preset autocall-1.6.2` (1.1.2 normalization, sex rule 1, GTC v3,
legacy statistics); the sample summary keeps the real statistics
- NaN sign canonicalised to the x86 pattern (K-014); report P4.md
Normalization 1.1.2 reproduces all 109,191 bins of the 1000 Genomes
Omni2.5 GTCs (AutoCall 1.6.2.2) with 0 ULP difference, reserved fields
included; 1.2.0 and 1.1.2 are bit-identical to the no-FMA gtc2vcf oracle
on 36 GSA samples. AWS run: 33 min, ~$0.15, no resources left.
Raw X/Y and controls assembled from IDATs equal the vendor GTC arrays for
2138/2141 samples x 2.45M loci; 3 single-locus differences are recorded
as K-009 (probable per-channel missing rule in AutoCall 1.6.2.2). Channel
order X=Red confirmed (U1). `simurg ingest` processed all 2141 samples in
19 min (I/O-bound). AWS run cost ~$0.34 and left no resources.
- pyproject (uv/hatch, ruff, mypy strict, pytest markers), MIT LICENSE,
THIRD_PARTY_NOTICES with verbatim BSD-2 (BeadArrayFiles) and MIT (gtc2vcf)
- CI workflow for Gitea/GitHub Actions: lint, type check, unit tests
- fetch_data.sh p0 now fetches the GSA v4 PGx demo IDATs (the GDA "Demo 36"
bundle is a GenomeStudio project), unpacks archives, fixes pipefail bug
- make_oracle_gtcs.sh builds bcftools + gtc2vcf and writes oracle GTCs
- plan §17: corrected P0 dataset table
Phase 0 deliverables: full technical specification and roadmap
(SIMURGARRAY_MASTER_PLAN.md), bootstrap script for reference
open-source repos and tiered dataset fetch script with checksums.
kemal