DeepTrio whole genome sequencing case study

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=1.5.0

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.

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]
95 non-pass records were skipped
Concordance HG002: F:138421/140063 (98.83%)  M:138488/140228 (98.76%)  F+M:135815/138691 (97.93%)
Sample HG002 has less than 99.0 concordance with both parents. Check for incorrect pedigree or sample mislabelling.
0/146377 (0.00%) records did not conform to expected call ploidy
142622/146377 (97.43%) records were variant in at least 1 family member and checked for Mendelian constraints
3465/142622 (2.43%) records had indeterminate consistency status due to incomplete calls
3215/142622 (2.25%) 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 21421 19 9768 11 7 0.995380 0.998370 0.456001 0.996873 NaN NaN 1.561710 2.069815 INDEL PASS 11256 11204 52 21421 19 9768 11 7 0.995380 0.998370 0.456001 0.996873 NaN NaN 1.561710 2.069815 SNP ALL 71333 71081 252 87680 29 16517 5 3 0.996467 0.999592 0.188378 0.998027 2.314904 2.055511 1.715978 1.691110 SNP PASS 71333 71081 252 87680 29 16517 5 3 0.996467 0.999592 0.188378 0.998027 2.314904 2.055511 1.715978 1.691110

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 21216 21 10151 15 5 0.996236 0.998102 0.478460 0.997168 NaN NaN 1.748961 2.267300 INDEL PASS 10628 10588 40 21216 21 10151 15 5 0.996236 0.998102 0.478460 0.997168 NaN NaN 1.748961 2.267300 SNP ALL 70166 69988 178 87159 64 17069 12 3 0.997463 0.999087 0.195837 0.998274 2.296566 2.039568 1.883951 1.885275 SNP PASS 70166 69988 178 87159 64 17069 12 3 0.997463 0.999087 0.195837 0.998274 2.296566 2.039568 1.883951 1.885275

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 10952 48 21664 27 10200 22 3 0.995636 0.997645 0.470827 0.996640 NaN NaN 1.792709 2.350414 INDEL PASS 11000 10952 48 21664 27 10200 22 3 0.995636 0.997645 0.470827 0.996640 NaN NaN 1.792709 2.350414 SNP ALL 71659 71456 203 88259 66 16686 7 7 0.997167 0.999078 0.189057 0.998122 2.310073 2.041714 1.878340 1.783300 SNP PASS 71659 71456 203 88259 66 16686 7 7 0.997167 0.999078 0.189057 0.998122 2.310073 2.041714 1.878340 1.783300