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DeepVariant Complete Genomics G400 case study

In this case study, we describe applying DeepVariant to a Complete Genomics G400 sample. Then we assess the quality of the DeepVariant variant calls with hap.py.

To make it faster to run over this case study, we run only on chromosome 20.

For how to prepare environment, the steps are the same as this doc.

Download Reference

We will be using GRCh38 for this case study.

mkdir -p reference

FTPDIR=ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids

curl ${FTPDIR}/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz | gunzip > reference/GRCh38_no_alt_analysis_set.fasta
curl ${FTPDIR}/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.fai > reference/GRCh38_no_alt_analysis_set.fasta.fai

Download Complete Genomics G400 HG002 chr20 BAM

mkdir -p input

HTTPDIR=https://storage.googleapis.com/deepvariant/complete-case-study-testdata

curl ${HTTPDIR}/HG002.complete_g400.V350151728.grch38.chr20.bam > input/HG002.complete_g400.V350151728.grch38.chr20.bam

curl ${HTTPDIR}/HG002.complete_g400.V350151728.grch38.chr20.bam.bai > input/HG002.complete_g400.V350151728.grch38.chr20.bam.bai

Download Genome in a Bottle Benchmarks for HG002

mkdir -p benchmark

FTPDIR=ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG002_NA24385_son/NISTv4.2.1/GRCh38

curl ${FTPDIR}/HG002_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed > benchmark/HG002_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed
curl ${FTPDIR}/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz > benchmark/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz
curl ${FTPDIR}/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi > benchmark/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi

Download Complete Genomics G400 model

HTTPDIR=https://storage.googleapis.com/deepvariant/complete-case-study-testdata

curl ${HTTPDIR}/complete-g400/weights-60-0.993753.ckpt.data-00000-of-00001 > input/weights-60-0.993753.ckpt.data-00000-of-00001

curl ${HTTPDIR}/complete-g400/weights-60-0.993753.ckpt.index > input/weights-60-0.993753.ckpt.index

curl ${HTTPDIR}/complete-g400/example_info.json > input/example_info.json

curl ${HTTPDIR}/complete-g400/model.example_info.json > input/model.example_info.json

Running DeepVariant with one command

On a CPU-only machine:

mkdir -p output
mkdir -p output/intermediate_results_dir

BIN_VERSION="1.10.0"

sudo docker run \
  -v "${PWD}/input":"/input" \
  -v "${PWD}/output":"/output" \
  -v "${PWD}/reference":"/reference" \
  google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/run_deepvariant \
  --model_type WGS \
  --ref /reference/GRCh38_no_alt_analysis_set.fasta \
  --reads /input/HG002.complete_g400.V350151728.grch38.chr20.bam \
  --output_vcf /output/HG002.output.vcf.gz \
  --output_gvcf /output/HG002.output.g.vcf.gz \
  --num_shards $(nproc) \
  --regions chr20 \
  --intermediate_results_dir /output/intermediate_results_dir \
  --customized_model /input/weights-60-0.993753.ckpt

For running on GPU machines, or using Singularity instead of Docker, see Quick Start.

Benchmark on chr20

mkdir -p happy

sudo docker pull jmcdani20/hap.py:v0.3.12

sudo docker run \
  -v "${PWD}/benchmark":"/benchmark" \
  -v "${PWD}/input":"/input" \
  -v "${PWD}/output":"/output" \
  -v "${PWD}/reference":"/reference" \
  -v "${PWD}/happy:/happy" \
  jmcdani20/hap.py:v0.3.12 /opt/hap.py/bin/hap.py \
  /benchmark/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz \
  /output/HG002.output.vcf.gz \
  -f /benchmark/HG002_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed \
  -r /reference/GRCh38_no_alt_analysis_set.fasta \
  -o /happy/happy.output \
  --engine=vcfeval \
  --pass-only \
  -l chr20

Output:

Benchmarking Summary:
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
INDEL    ALL        11256     11129       127        20918        29       9336     24      4       0.988717          0.997496        0.446314         0.993087                     NaN                     NaN                   1.561710                   2.049444
INDEL   PASS        11256     11129       127        20918        29       9336     24      4       0.988717          0.997496        0.446314         0.993087                     NaN                     NaN                   1.561710                   2.049444
  SNP    ALL        71333     70952       381        85724        51      14673     28      7       0.994659          0.999282        0.171166         0.996965                2.314904                 2.09936                   1.715978                   1.753945
  SNP   PASS        71333     70952       381        85724        51      14673     28      7       0.994659          0.999282        0.171166         0.996965                2.314904                 2.09936                   1.715978                   1.753945