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DeepVariant whole exome sequencing (WES) case study

Similar to the case study on whole genome sequencing data, in this study we describe applying DeepVariant to a real exome sample using a single machine.

Prepare environment

Tools

Docker will be used to run DeepVariant and hap.py,

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 Genome in a Bottle Benchmarks

We will benchmark our variant calls against v4.2.1 of the Genome in a Bottle small variant benchmarks for HG003.

mkdir -p benchmark

FTPDIR=ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG003_NA24149_father/NISTv4.2.1/GRCh38

curl ${FTPDIR}/HG003_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed > benchmark/HG003_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed
curl ${FTPDIR}/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz > benchmark/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz
curl ${FTPDIR}/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi > benchmark/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi

Download HG003 BAM

mkdir -p input
HTTPDIR=https://storage.googleapis.com/deepvariant/exome-case-study-testdata

curl ${HTTPDIR}/HG003.novaseq.wes_idt.100x.dedup.bam > input/HG003.novaseq.wes_idt.100x.dedup.bam
curl ${HTTPDIR}/HG003.novaseq.wes_idt.100x.dedup.bam.bai > input/HG003.novaseq.wes_idt.100x.dedup.bam.bai

Download capture target BED file

In this case study we'll use idt_capture_novogene.grch38.bed as the capture target BED file. For evaluation, hap.py will intersect this BED with the GIAB confident regions.

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

curl ${HTTPDIR}/idt_capture_novogene.grch38.bed > input/idt_capture_novogene.grch38.bed

Running on a CPU-only machine

mkdir -p output
mkdir -p output/intermediate_results_dir

BIN_VERSION="1.3.0"

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 WES \
  --ref /reference/GRCh38_no_alt_analysis_set.fasta \
  --reads /input/HG003.novaseq.wes_idt.100x.dedup.bam \
  --regions /input/idt_capture_novogene.grch38.bed \
  --output_vcf /output/HG003.output.vcf.gz \
  --output_gvcf /output/HG003.output.g.vcf.gz \
  --num_shards $(nproc) \
  --intermediate_results_dir /output/intermediate_results_dir

By specifying --model_type WES, you'll be using a model that is best suited for Illumina Whole Exome Sequencing data.

--intermediate_results_dir flag is optional. By specifying it, the intermediate outputs of make_examples and call_variants stages can be found in the directory. After the command, you can find these files in the directory:

call_variants_output.tfrecord.gz
gvcf.tfrecord-?????-of-?????.gz
make_examples.tfrecord-?????-of-?????.gz

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

Benchmark on all chromosomes

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/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz \
  /output/HG003.output.vcf.gz \
  -f /benchmark/HG003_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed \
  -T /input/idt_capture_novogene.grch38.bed \
  -r /reference/GRCh38_no_alt_analysis_set.fasta \
  -o /happy/happy.output \
  --engine=vcfeval \
  --pass-only

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         1051      1024        27         1471         8        417      6      1       0.974310           0.99241        0.283481         0.983277                     NaN                     NaN                   1.747283                   1.878728
INDEL   PASS         1051      1024        27         1471         8        417      6      1       0.974310           0.99241        0.283481         0.983277                     NaN                     NaN                   1.747283                   1.878728
  SNP    ALL        25279     24938       341        27521        57       2524     31      4       0.986511           0.99772        0.091712         0.992083                2.854703                2.771096                   1.623027                   1.629697
  SNP   PASS        25279     24938       341        27521        57       2524     31      4       0.986511           0.99772        0.091712         0.992083                2.854703                2.771096                   1.623027                   1.629697