| --- |
| license: mit |
| library_name: sklearn |
| tags: |
| - genomics |
| - structural-variants |
| - short-tandem-repeats |
| - variant-calling |
| - confidence-calibration |
| - random-forest |
| pipeline_tag: tabular-classification |
| --- |
| |
| # SVSTR-Score (v1.0) — long-read-guided confidence scoring for short-read SV/STR calls |
|
|
| Two per-class **RandomForest + isotonic calibrator** models that assign a |
| **calibrated confidence score** `CS ∈ [0,1]` and a four-tier operating point to |
| each short-read **structural-variant (SV)** or **short-tandem-repeat (STR)** call. |
| The long-read genotype is used **only to build the training label**, so inference |
| needs short-read caller output only. |
|
|
| `CS = isotonic_calibrator( RandomForest.predict_proba(X)[:, 1] )` = P(the |
| short-read call is concordant with the long-read truth). |
|
|
| | | SV | STR | |
| |---|---|---| |
| | weights | `sv_model.joblib` + `sv_calibrator.joblib` | `str_model.joblib` + `str_calibrator.joblib` | |
| | features | 35 (`feature_builder.py`) | 21 | |
| | short-read caller | Manta | ExpansionHunter | |
| | long-read truth (label only) | sawfish | TRGT | |
| | training cohort | 208 HPRC paired genomes | 208 HPRC | |
|
|
| ## Tiers |
| `HIGH CS≥0.70` · `MODERATE 0.50–0.70` · `WARNING 0.30–0.50` · `LOW <0.30`. |
| Tiers are buckets of the calibrated CS (no heuristic overrides). **HIGH** is the |
| candidate-triage tier. |
|
|
| ## Intended use |
| Triage / down-weight *emitted* short-read SV & STR calls by their probability of |
| matching long-read truth, **without long-read sequencing every sample**. Not a |
| variant caller; does not recover missed variants; does not replace long-read |
| sequencing for complete discovery. |
|
|
| ## Performance |
| **Internal — 5-fold GroupKFold by sample (out-of-fold), 208 HPRC genomes** |
|
|
| | | AUROC | AUPRC | per-sample AUROC median | |
| |---|---|---|---| |
| | SV | 0.950 | 0.951 | 0.981 | |
| | STR | 0.834 | 0.886 | 0.835 | |
|
|
| **External — 295 ASD genomes, applied unchanged (no retraining)** |
|
|
| | | AUROC | observed concordance LOW / WARN / MOD / HIGH | |
| |---|---|---| |
| | SV | 0.891 | 12% / 35% / 50% / **86%** | |
| | STR | 0.831 | 16% / 40% / 60% / **85%** | |
|
|
| Calibrated tiers stay monotone and meaningful on the external cohort; isotonic |
| calibration improves Brier (SV 0.077→0.074, STR 0.167→0.159). |
|
|
| ## Usage |
| ```bash |
| pip install -U huggingface_hub scikit-learn==1.7.1 |
| hf download khyeom/SVSTR-Score sv_model.joblib sv_calibrator.joblib sv_config.json score_svstr.py feature_builder.py --local-dir svstr/ |
| # features from a single-sample VCF, then score: |
| python svstr/feature_builder.py --vcf sample.manta.vcf.gz --caller manta \ |
| --fasta GRCh38.fa --giab-dir giab_prepared --repeatmasker rmsk_class.bed.gz -o feats.tsv |
| python svstr/score_svstr.py --variant sv --model-dir svstr --features feats.tsv --out scored.tsv |
| ``` |
| Code, training pipeline and reproduction scripts: https://github.com/khyeom0608/SVSTR-Score |
|
|
| ## Limitations |
| - Relies on **caller support / breakpoint-confidence fields** (PR/SR, CIPOS/CIEND, |
| VAF, GQ, depth). On **merged or heavily filtered call sets that drop these**, |
| scores deflate and tiers are unreliable (rank-discrimination degrades only |
| moderately, but calibration breaks). |
| - Strongest for **DEL/INS**; DUP/BND are mostly down-weighted/triaged. |
| - STR scoring is **bound to the caller's genotyped locus set**. |
|
|
| ## Citation |
| Kim W\*, Yeom K\*, et al. *SVSTR-Score* (manuscript in preparation). Licence: MIT. |
|
|
