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DeepVariant quick start

This is an explanation of how to use DeepVariant.

Background

To get started, you'll need the DeepVariant programs (and some packages they depend on), some test data, and of course a place to run them.

We've provided a Docker image, and some test data in a bucket on Google Cloud Storage. The instructions below show how to download the data through the corresponding public URLs from these data.

This setup requires a machine with the AVX instruction set. To see if your machine meets this requirement, you can check the /proc/cpuinfo file, which lists this information under "flags". If you do not have the necessary instructions, see the next section for more information on how to build your own Docker image.

Use Docker to run DeepVariant in one command.

Starting from the 0.8 release, we introduced one convenient command that will run through all 3 steps that are required to go from a BAM file to the VCF/gVCF output files. You can still read about the r0.7 approach in Quick Start in r0.7.

If you want to compile the DeepVariant binaries for yourself, we also have a Dockerfile that you can use to build your own Docker image. You can read the docker build documentation on how to build.

Get Docker image, models, and test data

Get Docker image

BIN_VERSION="1.5.0"

sudo apt -y update
sudo apt-get -y install docker.io
sudo docker pull google/deepvariant:"${BIN_VERSION}"

Download test data

Before you start running, you need to have the following input files:

  1. A reference genome in FASTA format and its corresponding index file (.fai).

  2. An aligned reads file in BAM format and its corresponding index file (.bai). You get this by aligning the reads from a sequencing instrument, using an aligner like BWA for example.

We've prepared a small test data bundle for use in this quick start guide that can be downloaded to your instance from the public URLs.

Download the test bundle:

INPUT_DIR="${PWD}/quickstart-testdata"
DATA_HTTP_DIR="https://storage.googleapis.com/deepvariant/quickstart-testdata"

mkdir -p ${INPUT_DIR}
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/NA12878_S1.chr20.10_10p1mb.bam
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/NA12878_S1.chr20.10_10p1mb.bam.bai
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/test_nist.b37_chr20_100kbp_at_10mb.bed
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/test_nist.b37_chr20_100kbp_at_10mb.vcf.gz
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/test_nist.b37_chr20_100kbp_at_10mb.vcf.gz.tbi
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/ucsc.hg19.chr20.unittest.fasta
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/ucsc.hg19.chr20.unittest.fasta.fai
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/ucsc.hg19.chr20.unittest.fasta.gz
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/ucsc.hg19.chr20.unittest.fasta.gz.fai
wget -P ${INPUT_DIR} "${DATA_HTTP_DIR}"/ucsc.hg19.chr20.unittest.fasta.gz.gzi

This should create a subdirectory in the current directory containing the actual data files:

ls -1 ${INPUT_DIR}

outputting:

NA12878_S1.chr20.10_10p1mb.bam
NA12878_S1.chr20.10_10p1mb.bam.bai
test_nist.b37_chr20_100kbp_at_10mb.bed
test_nist.b37_chr20_100kbp_at_10mb.vcf.gz
test_nist.b37_chr20_100kbp_at_10mb.vcf.gz.tbi
ucsc.hg19.chr20.unittest.fasta
ucsc.hg19.chr20.unittest.fasta.fai
ucsc.hg19.chr20.unittest.fasta.gz
ucsc.hg19.chr20.unittest.fasta.gz.fai
ucsc.hg19.chr20.unittest.fasta.gz.gzi

Model location (optional)

Starting from r0.8, we put the model files inside the released Docker images. So there is no need to download model files anymore. If you want to find the model files of all releases, you can find them in our bucket on the Google Cloud Storage. You can view them in the browser: https://console.cloud.google.com/storage/browser/deepvariant/models/DeepVariant

Run DeepVariant with one command

DeepVariant consists of 3 main binaries: make_examples, call_variants, and postprocess_variants. To make it easier to run, we create one entrypoint that can be directly run as a docker command. If you want to see the details, you can read through run_deepvariant.py.

OUTPUT_DIR="${PWD}/quickstart-output"
mkdir -p "${OUTPUT_DIR}"

You can run everything with the following command:

sudo docker run \
  -v "${INPUT_DIR}":"/input" \
  -v "${OUTPUT_DIR}":"/output" \
  google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/run_deepvariant \
  --model_type=WGS \ **Replace this string with exactly one of the following [WGS,WES,PACBIO,ONT_R104,HYBRID_PACBIO_ILLUMINA]**
  --ref=/input/ucsc.hg19.chr20.unittest.fasta \
  --reads=/input/NA12878_S1.chr20.10_10p1mb.bam \
  --regions "chr20:10,000,000-10,010,000" \
  --output_vcf=/output/output.vcf.gz \
  --output_gvcf=/output/output.g.vcf.gz \
  --intermediate_results_dir /output/intermediate_results_dir \ **This flag is optional. Set to keep the intermediate results.
  --num_shards=1 **How many cores the `make_examples` step uses. Change it to the number of CPU cores you have.**

NOTE: If you want to look at all the commands being run, you can add --dry_run=true to the command above, which will print out all the commands but not execute them.

This will generate 5 files and 1 directory in ${OUTPUT_DIR}:

ls -1 ${OUTPUT_DIR}

outputting:

intermediate_results_dir
output.g.vcf.gz
output.g.vcf.gz.tbi
output.vcf.gz
output.vcf.gz.tbi
output.visual_report.html

The directory "intermediate_results_dir" exists because --intermediate_results_dir /output/intermediate_results_dir is specified. This directory contains the intermediate output of make_examples and call_variants steps.

For more information about output.visual_report.html, see the VCF stats report documentation.

Notes on GPU image

If you are using GPUs, you can pull the GPU version, and make sure you run with --gpus 1. call_variants is the only step that uses the GPU, and can only use one at a time. make_examples and postprocess_variants do not run on GPU.

For an example to install GPU driver and docker, see install_nvidia_docker.sh.

sudo docker run --gpus 1 \
  -v "${INPUT_DIR}":"/input" \
  -v "${OUTPUT_DIR}:/output" \
  google/deepvariant:"${BIN_VERSION}-gpu" \
  /opt/deepvariant/bin/run_deepvariant \
  ...

Notes on Singularity

CPU version

# Pull the image.
singularity pull docker://google/deepvariant:"${BIN_VERSION}"

# Run DeepVariant.
singularity run -B /usr/lib/locale/:/usr/lib/locale/ \
  docker://google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/run_deepvariant \
  --model_type=WGS \ **Replace this string with exactly one of the following [WGS,WES,PACBIO,ONT_R104,HYBRID_PACBIO_ILLUMINA]**
  --ref="${INPUT_DIR}"/ucsc.hg19.chr20.unittest.fasta \
  --reads="${INPUT_DIR}"/NA12878_S1.chr20.10_10p1mb.bam \
  --regions "chr20:10,000,000-10,010,000" \
  --output_vcf="${OUTPUT_DIR}"/output.vcf.gz \
  --output_gvcf="${OUTPUT_DIR}"/output.g.vcf.gz \
  --intermediate_results_dir "${OUTPUT_DIR}/intermediate_results_dir" \ **Optional.
  --num_shards=1 \ **How many cores the `make_examples` step uses. Change it to the number of CPU cores you have.**

GPU version

# Pull the image.
singularity pull docker://google/deepvariant:"${BIN_VERSION}-gpu"

# Run DeepVariant.
# Using "--nv" and "${BIN_VERSION}-gpu" is important.
singularity run --nv -B /usr/lib/locale/:/usr/lib/locale/ \
  docker://google/deepvariant:"${BIN_VERSION}-gpu" \
  /opt/deepvariant/bin/run_deepvariant \
  ...

Evaluating the results

Here we use the hap.py (https://github.com/Illumina/hap.py) program from Illumina to evaluate the resulting 10 kilobase vcf file. This serves as a quick check to ensure the three DeepVariant commands ran correctly.

sudo docker pull jmcdani20/hap.py:v0.3.12
sudo docker run -it \
  -v "${INPUT_DIR}":"/input" \
  -v "${OUTPUT_DIR}:/output" \
  jmcdani20/hap.py:v0.3.12 /opt/hap.py/bin/hap.py \
  /input/test_nist.b37_chr20_100kbp_at_10mb.vcf.gz \
  /output/output.vcf.gz \
  -f "/input/test_nist.b37_chr20_100kbp_at_10mb.bed" \
  -r "/input/ucsc.hg19.chr20.unittest.fasta" \
  -o "/output/happy.output" \
  --engine=vcfeval \
  --pass-only \
  -l chr20:10000000-10010000

You should see output similar to the following.

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            4         4         0           13         0          9      0      0       1.000000               1.0        0.692308         1.000000                     NaN                     NaN                   0.333333                   1.000000
INDEL   PASS            4         4         0           13         0          9      0      0       1.000000               1.0        0.692308         1.000000                     NaN                     NaN                   0.333333                   1.000000
  SNP    ALL           44        43         1           59         0         16      0      0       0.977273               1.0        0.271186         0.988506                     1.2                    1.36                   0.333333                   0.340909
  SNP   PASS           44        43         1           59         0         16      0      0       0.977273               1.0        0.271186         0.988506                     1.2                    1.36                   0.333333                   0.340909