Runtime is on HG003 (all chromosomes).
| Stage | Time (minutes) |
|---|---|
| make_examples | ~106m |
| call_variants | ~168m |
| postprocess_variants (with gVCF) | ~49m |
| total | ~323m = ~5.38 hours |
hap.py results on HG003 (all chromosomes, using NIST v4.2.1 truth), which was held out while training.
| Type | TRUTH.TP | TRUTH.FN | QUERY.FP | METRIC.Recall | METRIC.Precision | METRIC.F1_Score |
|---|---|---|---|---|---|---|
| INDEL | 501681 | 2820 | 1272 | 0.99441 | 0.997573 | 0.995989 |
| SNP | 3307193 | 20303 | 4141 | 0.993898 | 0.99875 | 0.996318 |
Runtime is on HG003 (all chromosomes).
| Stage | Time (minutes) |
|---|---|
| make_examples | ~7m |
| call_variants | ~1m |
| postprocess_variants (with gVCF) | ~1m |
| total | ~9m |
hap.py results on HG003 (all chromosomes, using NIST v4.2.1 truth), which was held out while training.
| Type | TRUTH.TP | TRUTH.FN | QUERY.FP | METRIC.Recall | METRIC.Precision | METRIC.F1_Score |
|---|---|---|---|---|---|---|
| INDEL | 1020 | 31 | 9 | 0.970504 | 0.991429 | 0.980855 |
| SNP | 24976 | 303 | 46 | 0.988014 | 0.998162 | 0.993062 |
Runtime is on HG003 (all chromosomes).
| Stage | Time (minutes) |
|---|---|
| make_examples | ~169m |
| call_variants | ~191m |
| postprocess_variants (with gVCF) | ~60m |
| total | ~420m = ~7 hours |
hap.py results on HG003 (all chromosomes, using NIST v4.2.1 truth), which was held out while training.
Starting from v1.4.0, users don't need to phase the BAMs first, and only need to run DeepVariant once.
| Type | TRUTH.TP | TRUTH.FN | QUERY.FP | METRIC.Recall | METRIC.Precision | METRIC.F1_Score |
|---|---|---|---|---|---|---|
| INDEL | 501573 | 2928 | 2910 | 0.994196 | 0.994461 | 0.994328 |
| SNP | 3324469 | 3026 | 1905 | 0.999091 | 0.999428 | 0.999259 |
Runtime is on HG003 (all chromosomes).
| Stage | Time (minutes) |
|---|---|
| make_examples | ~159m |
| call_variants | ~165m |
| postprocess_variants (with gVCF) | ~44m |
| total | ~368m = ~6.13 hours |
Evaluating on HG003 (all chromosomes, using NIST v4.2.1 truth), which was held out while training the hybrid model.
| Type | TRUTH.TP | TRUTH.FN | QUERY.FP | METRIC.Recall | METRIC.Precision | METRIC.F1_Score |
|---|---|---|---|---|---|---|
| INDEL | 503355 | 1146 | 1939 | 0.997728 | 0.996346 | 0.997037 |
| SNP | 3323945 | 3551 | 1581 | 0.998933 | 0.999525 | 0.999229 |
For simplicity and consistency, we report runtime with a CPU instance with 64 CPUs This is NOT the fastest or cheapest configuration.
Use gcloud compute ssh to log in to the newly created instance.
Download and run any of the following case study scripts:
# Get the script.
curl -O https://raw.githubusercontent.com/google/deepvariant/r1.4/scripts/inference_deepvariant.sh
# WGS
bash inference_deepvariant.sh --model_preset WGS
# WES
bash inference_deepvariant.sh --model_preset WES
# PacBio
bash inference_deepvariant.sh --model_preset PACBIO
# Hybrid
bash inference_deepvariant.sh --model_preset HYBRID_PACBIO_ILLUMINA
Runtime metrics are taken from the resulting log after each stage of DeepVariant. The runtime numbers reported above are the average of 5 runs each. The accuracy metrics come from the hap.py summary.csv output file. The runs are deterministic so all 5 runs produced the same output.