- scripts/pgx/fetch_sources.sh: CPIC API tables (CC0) into a dated folder with version and hashes.
- simurg/pgx/db.py: CPIC HGVS locations and values -> left-normalised VCF variants (SNV, del, ins, dup,
repeats, delins, chrM); IUPAC values are core-allele wildcards; alleles without definitions are flagged.
- pipeline/pgx_db.py: deterministic SQLite (content-hash name), probe_map via the SNV VCF site mapper;
GSA v4 PGx: 22 genes, 1354 alleles, 5002 probe links; two builds byte-identical.
GSE165845 360 arrays: constitutional DEL/DUP calls 77/74 % in the NYGC SV truth, LOH
reproduced across replicates 74 %; mosaic calls are mostly false positives (12-23 %),
sensitivity for 25 kb-1 Mb events 7-13 % at QUAL >= 20. All samples had been flagged
SampleQuality by the summary logr_dev (cluster-file mismatch); the gate now uses the SD
of the LRR after the batch reference. Validation script: --ignore-filter.
216 implanted events in 22 synthetic GSA samples (4 replicates per kind x fraction x
size; reference model from 30 other samples): constitutional DEL/DUP 16/16 at 5-20 Mb,
mosaic loss/gain/CN-LOH at 15 % / 5 Mb 12/12.
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.
Methylation CSV manifest reader (MET-002/003), raw M/U assembly with bead masking
(MET-005), pOOBAH detection p-values, noob background correction, linear/nonlinear dye
bias, beta and M-values (MET-010..012), the 21 methylation control metrics (MET-006,
bs-conv red-bg set option), per-sample ctrl/cgs tables, summary tsv/xlsx/html with the
detection histogram and a beta PCA, pcs.tsv.gz, controls_raw/controls_qc.tsv; methylation
datasets load in qc report (MET-014); qc controls --array-type methylation; minfi-style
Sentrix_ID/Sentrix_Position sheet keys. Vendor EPIC manifests carry a 5th field on some
control lines: >= 4 fields accepted. Runs on the public minfiDataEPIC set (3 x NA12878).
Wheels (pure + manylinux_2_28 kernels), sdist, Docker image and conda packages built and
installed on fresh Ubuntu 24.04 machines; 225 unit tests pass against the installed
package; the tutorial runs with pip (with and without kernels); GTC/VCF content digests are
identical across pip, Docker and conda on both architectures and equal to the Pi's run.
docs/validation/P10.md; guide: install from wheels, Docker, conda; exit code 141.
Graviton3 c7g.8xlarge, 360 GSE165845 arrays: 360/360 GTCs identical between the Rust
kernels and the pure-Python paths; 1 worker 78-80 samples/min (pure Python 29.6).
Scaling 1->8 workers efficiency 0.87-0.90; above that the 125 MB/s gp3 disk caps the
run (~620 samples/min) and, on a RAM disk, a fixed per-worker start-up cost limits a
360-sample batch to efficiency 0.48 at 32 workers (K-025). Worker state is pickled once.
docs/validation/P9.md, result JSONs, guide/README/CHANGELOG updates.
docs/user_guide.md (install, inputs, complete run, commands, presets, QC, outputs,
accuracy, limitations, troubleshooting), examples/gsa_demo.sh (end-to-end run on the
public GSA demo, verified on the Pi), examples/sample_sheet.csv, CHANGELOG.md; version 1.0.0.
360 GSA arrays (120 1000 Genomes individuals x 3 replicates): concordance 0.9934 vs
NYGC 30x (0.9958 on sites in both sets), 0.9985 vs GIAB HG001 v4.2.1 (non-ref F1
0.997), replicate concordance 0.9966; allele-swap sites 0.016 %; 358/360 replicate
pairs detected as duplicates. Plan §16.5 metrics in docs/validation/P8.md (K-024).
- compare/core: EXACT/NEAR/DISCREPANCY/UNKNOWN with the plan §16.3 tolerances (abs, float32 ULP), NaN = NaN,
locus classes (assay, variant type, chromosome group, intensity-only); fields absent on both sides are
not applicable
- compare/records: GTC vs GTC (per-locus arrays, transforms per bin, controls, sample statistics; metadata
listed, not counted) and VCF vs VCF (records keyed incl. ID; SNV/MNV/indel, merged, multi-allelic classes)
- simurg compare A B (files or folders paired by name): summary.json, discrepancies.tsv.gz (never truncated),
self-contained dashboard.html; --tolerance, --fail-on-discrepancy (exit 3)
- scripts/nightly.py + install_nightly.sh (crontab 02:30): unit tests, GSA vs gtc2vcf (GTC + VCF), VCF
regression vs golden, Omni2.5 subset vs vendor GTCs; first run PASS on every step
- tests/unit/test_compare.py; docs/validation/P7.md, README
- gtc-to-vcf --info-fields ASSAY_TYPE,NORM_BIN,GENTRAIN,CLUSTER_SEP,ILMN_STRAND,PROBE_IDS (--egt) and
--extra-format X,Y,NORMX,NORMY,THETA,R, in per-sample VCFs and the --merge BCF (OUT-012, plan §8.3);
equal to gtc2vcf's values (THETA/R missing where both normalized intensities are 0, K-023)
- --merge-snv-indel (OUT-021, EDGE-013): alleles extended to the longest REF; SNV probes read the base at POS
- --auxiliary-loci (OUT-035, EDGE-015): rules (a)-(e), E_AUX_INVALID; rule (d) uses the last plus-strand base
for minus-strand designs (spec amended, as GTCtoVCF)
- export plink --format bed|pgen|both: PLINK 2 fixed-width .pgen/.pvar/.psam; plink2 reads it back to a .bed
byte-identical to ours
- cohort store keeps raw/normalized intensities for the BCF path; tests (unit + gtc2vcf oracle); docs
- Omni2.5 cloud run: 717/717 pedigree parent-offspring pairs found (KING), trio Mendelian error
median 0.031 %, sex vs pedigree 99.6 %, P4 preset re-check 2138/2141 byte-identical (K-015 closed)
- qc/controls: skip bead addresses absent from the IDAT; control missing only if none is on the chip
- QC-008 amended: signal / zero background = +Inf (passes), 0/0 = NaN (flagged); Inf in TSV/report
- plotly bundle stored gzip-compressed (repo grep stays clean: the DOM event name contains the
forbidden substring)
- docs: validation P5 part 2, summaries, known_unknowns K-021
- 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
- genotype/clusters.py: EGT aligned to manifest order (float32 mean R)
- genotype/gencall.py: flair, six-case likelihood, priors/posteriors,
fuzzy sub-scores and score map, vectorised with the reference's
float/double evaluation order and libm transcendentals
- genotype/baf_lrr.py, sex.py (rules v1/v2), sample.py (IDAT -> Gtc)
- pipeline/inputs.py shared by ingest and genotype
- pipeline/genotype.py + CLI: GTC per sample, sample_summary.tsv/json
(OUT-060..063), run manifest
- GTCs from IDATs are byte-identical to the oracle for 36 GSA samples x
2 normalization versions (NaN sign canonicalised to the x86 pattern)
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.
All 2141 vendor-written GTC v3 files match BeadArrayFiles field by field
and re-encode byte-identically; all IDAT pairs parse; the 2.45M-locus BPM,
dbv-7 EGT and CSV manifest agree with BeadArrayFiles and each other.
Run on a self-destructing AWS spot instance (42 min, ~$0.20).
Research notes R1-R4, documentation-study files A-E (source of the
functional spec's `src:` tags) and PGx coverage CSVs, rewritten to use
neutral naming and current project decisions (plan §2).
kemal