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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=1.3.0

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.

--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 vialation 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]
81 non-pass records were skipped
Concordance HG002: F:137455/138929 (98.94%)  M:137567/139104 (98.90%)  F+M:134901/137492 (98.12%)
Sample HG002 has less than 99.0 concordance with both parents. Check for incorrect pedigree or sample mislabelling.
0/144439 (0.00%) records did not conform to expected call ploidy
141835/144439 (98.20%) records were variant in at least 1 family member and checked for Mendelian constraints
3841/141835 (2.71%) records had indeterminate consistency status due to incomplete calls
2961/141835 (2.09%) 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     11209        47        21264        16       9609     10      5       0.995824          0.998627        0.451891         0.997224                     NaN                     NaN                   1.561710                   2.081021
INDEL   PASS        11256     11209        47        21264        16       9609     10      5       0.995824          0.998627        0.451891         0.997224                     NaN                     NaN                   1.561710                   2.081021
  SNP    ALL        71333     71063       270        87471        30      16324      4      3       0.996215          0.999578        0.186622         0.997894                2.314904                2.048981                   1.715978                   1.715178
  SNP   PASS        71333     71063       270        87471        30      16324      4      3       0.996215          0.999578        0.186622         0.997894                2.314904                2.048981                   1.715978                   1.715178

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     10588        40        21060        16       9999     12      4       0.996236          0.998553        0.474786         0.997394                     NaN                     NaN                   1.748961                   2.256561
INDEL   PASS        10628     10588        40        21060        16       9999     12      4       0.996236          0.998553        0.474786         0.997394                     NaN                     NaN                   1.748961                   2.256561
  SNP    ALL        70166     69928       238        85229        47      15216     18      2       0.996608          0.999329        0.178531         0.997967                2.296566                2.066067                   1.883951                   1.875008
  SNP   PASS        70166     69928       238        85229        47      15216     18      2       0.996608          0.999329        0.178531         0.997967                2.296566                2.066067                   1.883951                   1.875008

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     10953        47        21465        22      10003     15      5       0.995727          0.998081        0.466014         0.996903                     NaN                     NaN                   1.792709                   2.331949
INDEL   PASS        11000     10953        47        21465        22      10003     15      5       0.995727          0.998081        0.466014         0.996903                     NaN                     NaN                   1.792709                   2.331949
  SNP    ALL        71659     71428       231        86370        44      14848     10      5       0.996776          0.999385        0.171912         0.998079                2.310073                2.069269                   1.878340                   1.775156
  SNP   PASS        71659     71428       231        86370        44      14848     10      5       0.996776          0.999385        0.171912         0.998079                2.310073                2.069269                   1.878340                   1.775156