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Update case studies for 1.9
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‎docs/deepvariant-fast-pipeline-case-study.md

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@@ -48,13 +48,13 @@ Please refer to the following documentation for more details.
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[Installing the NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
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For this case study we used the
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[script](https://github.com/google/deepvariant/blob/r1.8.0/scripts/install_nvidia_docker.sh)
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[script](https://github.com/google/deepvariant/blob/r1.9/scripts/install_nvidia_docker.sh)
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that automates the CUDA and container tools kit installation.
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Please note that the script takes about 30 minutes to run.
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```bash
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wget https://raw.githubusercontent.com/google/deepvariant/refs/heads/r1.8.0/scripts/install_nvidia_docker.sh
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wget https://raw.githubusercontent.com/google/deepvariant/refs/heads/r1.9/scripts/install_nvidia_docker.sh
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chmod +x install_nvidia_docker.sh
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./install_nvidia_docker.sh
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```
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### Get DeepVariant Docker image
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```bash
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BIN_VERSION="1.8.0"
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BIN_VERSION="1.9.0"
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sudo docker pull google/deepvariant:"${BIN_VERSION}-gpu"
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```
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With the same settings the pipeline takes approximately 10 minutes.
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```
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real 8m15.252s
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user 0m0.007s
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sys 0m0.035s
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real 12m45.795s
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user 0m0.018s
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sys 0m0.038s
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```
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## Benchmark output
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```
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Benchmarking Summary:
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Type Filter TRUTH.TOTAL TRUTH.TP TRUTH.FN QUERY.TOTAL QUERY.FP QUERY.UNK FP.gt FP.al METRIC.Recall METRIC.Precision METRIC.Frac_NA METRIC.F1_Score TRUTH.TOTAL.TiTv_ratio QUERY.TOTAL.TiTv_ratio TRUTH.TOTAL.het_hom_ratio QUERY.TOTAL.het_hom_ratio
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INDEL ALL 10628 10543 85 22403 74 11375 40 29 0.992002 0.993290 0.507744 0.992646 NaN NaN 1.748961 2.138647
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INDEL PASS 10628 10543 85 22403 74 11375 40 29 0.992002 0.993290 0.507744 0.992646 NaN NaN 1.748961 2.138647
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SNP ALL 70166 70101 65 105602 71 35342 12 12 0.999074 0.998989 0.334672 0.999032 2.296566 1.713281 1.883951 1.503192
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SNP PASS 70166 70101 65 105602 71 35342 12 12 0.999074 0.998989 0.334672 0.999032 2.296566 1.713281 1.883951 1.503192
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INDEL ALL 10628 10553 75 22560 72 11522 37 28 0.992943 0.993477 0.510727 0.993210 NaN NaN 1.748961 2.180292
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INDEL PASS 10628 10553 75 22560 72 11522 37 28 0.992943 0.993477 0.510727 0.993210 NaN NaN 1.748961 2.180292
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SNP ALL 70166 70106 60 102415 69 32148 9 9 0.999145 0.999018 0.313899 0.999081 2.296566 1.72911 1.883951 1.442237
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SNP PASS 70166 70106 60 102415 69 32148 9 9 0.999145 0.999018 0.313899 0.999081 2.296566 1.72911 1.883951 1.442237
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```

‎docs/deepvariant-training-case-study.md

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--disable_small_model
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```
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Starting in v1.8.0, by default we use a small model to classify some
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We use a small model to classify some
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candidates. In this example, we set `--disable_small_model` so
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that small model is disabled. This allows us to run all examples
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through the model we just trained.

‎docs/deepvariant-vg-case-study.md

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```bash
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FTPDIR=ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids
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curl ${FTPDIR}/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz | gunzip > ${DATA_DIR}/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna
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samtools faidx ${DATA_DIR}/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna
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```
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[DeepVariant Case Study](deepvariant-case-study.md).)
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```bash
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BIN_VERSION="1.8.0"
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BIN_VERSION="1.9.0"
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sudo docker pull google/deepvariant:"${BIN_VERSION}"
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Stage | Time (minutes)
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-------------------------------- | -----------------
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make_examples | 59m19.845s
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call_variants | 49m41.643s
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postprocess_variants (with gVCF) | 7m46.195s
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make_examples | 81m11.112s
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call_variants | 38m27.228s
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postprocess_variants (with gVCF) | 9m13.565s
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### Run hap.py
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```
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Benchmarking Summary:
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Type Filter TRUTH.TOTAL TRUTH.TP TRUTH.FN QUERY.TOTAL QUERY.FP QUERY.UNK FP.gt FP.al METRIC.Recall METRIC.Precision METRIC.Frac_NA METRIC.F1_Score TRUTH.TOTAL.TiTv_ratio QUERY.TOTAL.TiTv_ratio TRUTH.TOTAL.het_hom_ratio QUERY.TOTAL.het_hom_ratio
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INDEL ALL 504501 502210 2291 954974 1522 429900 956 362 0.995459 0.997101 0.450169 0.996279 NaN NaN 1.489759 1.942299
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INDEL PASS 504501 502210 2291 954974 1522 429900 956 362 0.995459 0.997101 0.450169 0.996279 NaN NaN 1.489759 1.942299
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SNP ALL 3327496 3316336 11160 3823082 4229 500683 1696 356 0.996646 0.998727 0.130963 0.997686 2.102576 1.990152 1.535137 1.449299
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SNP PASS 3327496 3316336 11160 3823082 4229 500683 1696 356 0.996646 0.998727 0.130963 0.997686 2.102576 1.990152 1.535137 1.449299
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INDEL ALL 504501 502342 2159 956579 1444 431515 881 290 0.995721 0.99725 0.451102 0.996485 NaN NaN 1.489759 1.924206
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INDEL PASS 504501 502342 2159 956579 1444 431515 881 290 0.995721 0.99725 0.451102 0.996485 NaN NaN 1.489759 1.924206
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SNP ALL 3327496 3319188 8308 4031912 5621 705300 1705 469 0.997503 0.99831 0.174929 0.997907 2.102576 1.889869 1.535137 1.312185
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SNP PASS 3327496 3319188 8308 4031912 5621 705300 1705 469 0.997503 0.99831 0.174929 0.997907 2.102576 1.889869 1.535137 1.312185
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```
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This can be compared with
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https://github.com/google/deepvariant/blob/r1.8/docs/metrics.md#accuracy.
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https://github.com/google/deepvariant/blob/r1.9/docs/metrics.md#accuracy.
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Which shows that `vg giraffe` improves F1:
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- Indel F1: 0.995945 --> 0.996279
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- SNP F1: 0.996213 --> 0.997686
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- Indel F1: 0.995845 --> 0.996485
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- SNP F1: 0.996133 --> 0.997907

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