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DeepTrio whole genome sequencing case study

In this case study, we describe applying DeepTrio to a real WGS trio. Then we assess the quality of the DeepTrio variant calls with hap.py. In addition we evaluate a mendelian violation rate for a merged VCF.

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

Prepare environment

Tools

Docker will be used to run DeepTrio 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 HG002, HG003, and HG004 trio.

mkdir -p benchmark

FTPDIR=ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio

curl ${FTPDIR}/HG002_NA24385_son/NISTv4.2.1/GRCh38/HG002_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed > benchmark/HG002_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed
curl ${FTPDIR}/HG002_NA24385_son/NISTv4.2.1/GRCh38/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz > benchmark/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz
curl ${FTPDIR}/HG002_NA24385_son/NISTv4.2.1/GRCh38/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi > benchmark/HG002_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi

curl ${FTPDIR}/HG003_NA24149_father/NISTv4.2.1/GRCh38/HG003_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed > benchmark/HG003_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed
curl ${FTPDIR}/HG003_NA24149_father/NISTv4.2.1/GRCh38/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz > benchmark/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz
curl ${FTPDIR}/HG003_NA24149_father/NISTv4.2.1/GRCh38/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi > benchmark/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi

curl ${FTPDIR}/HG004_NA24143_mother/NISTv4.2.1/GRCh38/HG004_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed > benchmark/HG004_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed
curl ${FTPDIR}/HG004_NA24143_mother/NISTv4.2.1/GRCh38/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz > benchmark/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz
curl ${FTPDIR}/HG004_NA24143_mother/NISTv4.2.1/GRCh38/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi > benchmark/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi

Download HG002, HG003, and HG004 BAM files

We'll use HG002, HG003, HG004 Illumina WGS reads publicly available from the PrecisionFDA Truth v2 Challenge.

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

curl ${HTTPDIR}/HG002.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam > input/HG002.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam
curl ${HTTPDIR}/HG002.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam.bai > input/HG002.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam.bai

curl ${HTTPDIR}/HG003.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam > input/HG003.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam
curl ${HTTPDIR}/HG003.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam.bai > input/HG003.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam.bai

curl ${HTTPDIR}/HG004.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam > input/HG004.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam
curl ${HTTPDIR}/HG004.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam.bai > input/HG004.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam.bai

Running DeepTrio with one command

DeepTrio pipeline consists of 4 steps: make_examples, call_variants, postprocess_variants and GLnexus merge. It is possible to run DeepTrio with one command using the run_deepvariant script. GLnexus is run as a separate command.

Running on a CPU-only machine

mkdir -p output
mkdir -p output/intermediate_results_dir

BIN_VERSION="1.10.0"

sudo docker pull google/deepvariant:deeptrio-"${BIN_VERSION}"

time sudo docker run \
  -v "${PWD}/input":"/input"   \
  -v "${PWD}/output":"/output"  \
  -v "${PWD}/reference":"/reference" \
  google/deepvariant:deeptrio-"${BIN_VERSION}" \
  /opt/deepvariant/bin/deeptrio/run_deeptrio \
  --model_type WGS \
  --ref /reference/GRCh38_no_alt_analysis_set.fasta \
  --reads_child /input/HG002.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam \
  --reads_parent1 /input/HG003.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam \
  --reads_parent2 /input/HG004.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam \
  --output_vcf_child /output/HG002.output.vcf.gz \
  --output_vcf_parent1 /output/HG003.output.vcf.gz \
  --output_vcf_parent2 /output/HG004.output.vcf.gz \
  --sample_name_child 'HG002' \
  --sample_name_parent1 'HG003' \
  --sample_name_parent2 'HG004' \
  --num_shards $(nproc)  \
  --regions chr20 \
  --intermediate_results_dir /output/intermediate_results_dir \
  --output_gvcf_child /output/HG002.g.vcf.gz \
  --output_gvcf_parent1 /output/HG003.g.vcf.gz \
  --output_gvcf_parent2 /output/HG004.g.vcf.gz

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

NOTE: If you want to run each of the steps separately, add --dry_run=true to the command above to figure out what flags you need in each step. Based on the different model types, different flags are needed in the make_examples step.

--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_child.tfrecord.gz
call_variants_output_parent1.tfrecord.gz
call_variants_output_parent2.tfrecord.gz

gvcf_child.tfrecord-?????-of-?????.gz
gvcf_parent1.tfrecord-?????-of-?????.gz
gvcf_parent2.tfrecord-?????-of-?????.gz

make_examples_child.tfrecord-?????-of-?????.gz
make_examples_parent1.tfrecord-?????-of-?????.gz
make_examples_parent2.tfrecord-?????-of-?????.gz

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

Merge VCFs using GLnexus

At this step we take all 3 VCFs generated in the previous step and merge them using GLnexus.

# bcftools and bgzip are now included in our docker images.
# You can also install them separately.
sudo docker run \
  -v "${PWD}/output":"/output" \
  quay.io/mlin/glnexus:v1.2.7 \
  /usr/local/bin/glnexus_cli \
  --config DeepVariant_unfiltered \
  /output/HG002.g.vcf.gz \
  /output/HG003.g.vcf.gz \
  /output/HG004.g.vcf.gz \
  | sudo docker run -i google/deepvariant:deeptrio-"${BIN_VERSION}" \
    bcftools view - \
  | sudo docker run -i google/deepvariant:deeptrio-"${BIN_VERSION}" \
    bgzip -c > output/HG002_trio_merged.vcf.gz

After completion of GLnexus command we should have a new merged VCF file in the output directory.

HG002_trio_merged.vcf.gz

Benchmark on chr20

Calculate mendelian violation rate

sudo docker run \
  -v "${PWD}/input":"/input" \
  -v "${PWD}/reference":"/reference" \
  realtimegenomics/rtg-tools format \
  -o /reference/GRCh38_no_alt_analysis_set.sdf "/reference/GRCh38_no_alt_analysis_set.fasta"

FILE="reference/trio.ped"
cat <<EOM >$FILE
#PED format pedigree
#
#fam-id/ind-id/pat-id/mat-id: 0=unknown
#sex: 1=male; 2=female; 0=unknown
#phenotype: -9=missing, 0=missing; 1=unaffected; 2=affected
#
#fam-id ind-id pat-id mat-id sex phen
1 HG002 HG003 HG004 1 0
1 HG003 0 0 1 0
1 HG004 0 0 2 0
EOM

sudo docker run \
-v "${PWD}/input":"/input" \
-v "${PWD}/reference":"/reference" \
-v "${PWD}/output":"/output" \
realtimegenomics/rtg-tools mendelian \
-i "/output/HG002_trio_merged.vcf.gz" \
-o "/output/HG002_trio_annotated.output.vcf.gz" \
--pedigree=/reference/trio.ped \
-t /reference/GRCh38_no_alt_analysis_set.sdf \
| tee output/deepvariant.input_rtg_output.txt

As a result we should get the following output:

Checking: /output/HG002_trio_merged.vcf.gz
Family: [HG003 + HG004] -> [HG002]
90 non-pass records were skipped
Concordance HG002: F:138180/140153 (98.59%)  M:138249/140339 (98.51%)  F+M:135066/138513 (97.51%)
Sample HG002 has less than 99.0 concordance with both parents. Check for incorrect pedigree or sample mislabelling.
0/145514 (0.00%) records did not conform to expected call ploidy
143322/145514 (98.49%) records were variant in at least 1 family member and checked for Mendelian constraints
4116/143322 (2.87%) records had indeterminate consistency status due to incomplete calls
3995/143322 (2.79%) records contained a violation of Mendelian constraints

Perform analysis with hap.py against 4.2.1 truth set

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/HG002.output \
  --engine=vcfeval \
  --pass-only \
  -l chr20

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 \
  -r /reference/GRCh38_no_alt_analysis_set.fasta \
  -o /happy/HG003.output \
  --engine=vcfeval \
  --pass-only \
  -l chr20

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/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz \
  /output/HG004.output.vcf.gz \
  -f /benchmark/HG004_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed \
  -r /reference/GRCh38_no_alt_analysis_set.fasta \
  -o /happy/HG004.output \
  --engine=vcfeval \
  --pass-only \
  -l chr20
Benchmarking Summary for HG002:
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     11204        52        21103        17       9451     10      5       0.995380          0.998541        0.447851         0.996958                     NaN                     NaN                   1.561710                   2.018104
INDEL   PASS        11256     11204        52        21103        17       9451     10      5       0.995380          0.998541        0.447851         0.996958                     NaN                     NaN                   1.561710                   2.018104
  SNP    ALL        71333     71087       246        88094        39      16921      6      6       0.996551          0.999452        0.192079         0.998000                2.314904                2.038305                   1.715978                   1.698838
  SNP   PASS        71333     71087       246        88094        39      16921      6      6       0.996551          0.999452        0.192079         0.998000                2.314904                2.038305                   1.715978                   1.698838

Benchmarking Summary for HG003:
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        10628     10571        57        20941        28       9889     24      3       0.994637          0.997467        0.472232         0.996050                     NaN                     NaN                   1.748961                   2.171389
INDEL   PASS        10628     10571        57        20941        28       9889     24      3       0.994637          0.997467        0.472232         0.996050                     NaN                     NaN                   1.748961                   2.171389
  SNP    ALL        70166     69975       191        85456        55      15390     18      3       0.997278          0.999215        0.180093         0.998246                2.296566                2.058615                   1.883951                   1.849001
  SNP   PASS        70166     69975       191        85456        55      15390     18      3       0.997278          0.999215        0.180093         0.998246                2.296566                2.058615                   1.883951                   1.849001

Benchmarking Summary for HG004:
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        11000     10945        55        21348        29       9890     22      5       0.995000          0.997469        0.463275         0.996233                     NaN                     NaN                   1.792709                   2.238266
INDEL   PASS        11000     10945        55        21348        29       9890     22      5       0.995000          0.997469        0.463275         0.996233                     NaN                     NaN                   1.792709                   2.238266
  SNP    ALL        71659     71445       214        86665        49      15119      6      6       0.997014          0.999315        0.174453         0.998163                2.310073                2.058352                   1.878340                   1.738801
  SNP   PASS        71659     71445       214        86665        49      15119      6      6       0.997014          0.999315        0.174453         0.998163                2.310073                2.058352                   1.878340                   1.738801