Benchmarks
Honest numbers — same datasets, same folds, same seeds. We compare SEED's GBLUP, GxE-aware GS and GWAS engines against the standard R/Python references researchers already trust. Code, parameters and seeds are embedded in every reproducibility bundle.
Accuracy gap
±0.01 vs sommer
Runtime
3–17× faster
Memory
2–4× lower
Wheat-599 (CIMMYT)
Genomic prediction · grain yield · 5-fold CV
599 lines × 1,279 DArT markers
| Method | Accuracy / hits | Runtime | Peak memory | Notes |
|---|---|---|---|---|
| SEED · GBLUP | 0.54 ± 0.04 | 1.8 s | 92 MB | ridge-BLUP, browser-cached |
| rrBLUP (R) | 0.53 ± 0.04 | 6.1 s | 210 MB | mixed.solve() |
| sommer (R) | 0.54 ± 0.04 | 14.2 s | 380 MB | mmer() REML |
| BGLR (R, BayesB) | 0.55 ± 0.05 | 98 s | 440 MB | 12k MCMC iter |
Maize G2F-2017
Multi-environment GS · GxE-aware
1,250 hybrids × 8 envs × 350k SNPs
| Method | Accuracy / hits | Runtime | Peak memory | Notes |
|---|---|---|---|---|
| SEED · GxE GBLUP | 0.61 ± 0.03 | 12 s | 640 MB | main+env decomposition |
| sommer (GE block) | 0.60 ± 0.03 | 210 s | 1.8 GB | unstructured G×E |
| ASReml-R | 0.61 ± 0.03 | 180 s | 1.4 GB | license required |
Rice-RDP1 (USDA)
GWAS · plant height · MLM + 5 PCs
413 accessions × 36,901 SNPs
| Method | Accuracy / hits | Runtime | Peak memory | Notes |
|---|---|---|---|---|
| SEED · MLM-PC | 11 hits @ 5% FDR | 4.4 s | 160 MB | client-side Manhattan |
| GAPIT (R, MLM) | 10 hits | 38 s | 520 MB | FarmCPU off |
| PLINK 2.0 (--glm) | 12 hits | 1.9 s | 85 MB | no kinship, faster but inflated |
Methodology
- All runs used identical train/test partitions (seed = 42, 5-fold CV).
- Reference R packages run on R 4.4 with BLAS = OpenBLAS, single thread.
- Runtime is end-to-end wall clock; memory is RSS peak (psutil/ps).
- SEED runs are reproducible from any browser via the in-app run; parameters embedded in the JSON bundle.
- Source datasets are public: Wheat-599 (CIMMYT, BGLR), Maize G2F (genomes2fields.org), Rice RDP1 (USDA-ARS).