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DeepVariant whole genome case study

In this case study we describe applying DeepVariant to a real WGS sample.

We provide some guidelines on the computational resources needed for each step. And finally we assess the quality of the DeepVariant variant calls with hap.py.

NOTE: This case study demonstrates an example of how to run DeepVariant end-to-end on one machine. This might not be the fastest or cheapest configuration for your needs. For more scalable execution of DeepVariant see the cost- and speed-optimized, Docker-based pipelines created for Google Cloud Platform.

Consult this document for more information about using GPUs or reading BAM files from Google Cloud Storage (GCS) directly.

Update since r0.7 : using docker instead of copying binaries.

Starting from the 0.7 release, we use docker to run the binaries instead of copying binaries to local machines first. You can still read about the previous approach in the Case Study in r0.6.

We recognize that there might be some overhead of using docker run. But using docker makes this case study easier to generalize to different versions of Linux systems. For example, we have verified that you can use docker to run DeepVariant on other Linux systems such as CentOS 7.

Request a machine

You can run this exercise on any sufficiently capable machine. As a concrete but simple example of how we performed this study, we used 64-core (vCPU) machine with 128GiB of memory and no GPU, on the Google Cloud Platform.

We used a command like this to allocate it:

gcloud beta compute instances create "${USER}-deepvariant-casestudy"  \
--scopes "compute-rw,storage-full,cloud-platform" \
--image-family "ubuntu-1604-lts" \
--image-project "ubuntu-os-cloud" \
--machine-type "custom-64-131072" \
--boot-disk-size "300" \
--boot-disk-type "pd-ssd" \
--zone "us-west1-b"

Once the machine is ready, ssh into it:

gcloud compute ssh "${USER}-deepvariant-casestudy" --zone "us-west1-b"

NOTE: Having an instance up and running could cost you. Remember to delete the instances you're not using. You can find the instances at: https://console.cloud.google.com/compute/instances?project=YOUR_PROJECT

Preliminaries

Set a number of shell variables, to make what follows easier to read.

BASE="${HOME}/case-study"
BUCKET="gs://deepvariant"
BIN_VERSION="0.7.0"
MODEL_VERSION="0.7.0"

MODEL_BUCKET="${BUCKET}/models/DeepVariant/${MODEL_VERSION}/DeepVariant-inception_v3-${MODEL_VERSION}+data-wgs_standard"
DATA_BUCKET="${BUCKET}/case-study-testdata"

INPUT_DIR="${BASE}/input"
MODELS_DIR="${INPUT_DIR}/models"
MODEL="${MODELS_DIR}/model.ckpt"
DATA_DIR="${INPUT_DIR}/data"
REF="${DATA_DIR}/hs37d5.fa.gz"
BAM="${DATA_DIR}/HG002_NIST_150bp_50x.bam"
TRUTH_VCF="${DATA_DIR}/HG002_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-22_v.3.3.2_highconf_triophased.vcf.gz"
TRUTH_BED="${DATA_DIR}/HG002_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-22_v.3.3.2_highconf_noinconsistent.bed"

N_SHARDS="64"

OUTPUT_DIR="${BASE}/output"
EXAMPLES="${OUTPUT_DIR}/HG002.examples.tfrecord@${N_SHARDS}.gz"
GVCF_TFRECORDS="${OUTPUT_DIR}/HG002.gvcf.tfrecord@${N_SHARDS}.gz"
CALL_VARIANTS_OUTPUT="${OUTPUT_DIR}/HG002.cvo.tfrecord.gz"
OUTPUT_VCF="${OUTPUT_DIR}/HG002.output.vcf.gz"
OUTPUT_GVCF="${OUTPUT_DIR}/HG002.output.g.vcf.gz"
LOG_DIR="${OUTPUT_DIR}/logs"

Create local directory structure

mkdir -p "${OUTPUT_DIR}"
mkdir -p "${DATA_DIR}"
mkdir -p "${MODELS_DIR}"
mkdir -p "${LOG_DIR}"

Download extra packages

There are some extra programs we will need.

We are going to use GNU Parallel to run make_examples. We are going to install samtools and docker.io to help do some analysis at the end.

sudo apt-get -y update
sudo apt-get -y install parallel
sudo apt-get -y install samtools
sudo apt-get -y install docker.io

Download models, and test data

Models

Copy the model files to your local disk.

time gsutil -m cp -r "${MODEL_BUCKET}/*" "${MODELS_DIR}"

This step should be really fast. It took us about 5 seconds.

Test data

Copy the input files you need to your local disk from our gs:// bucket.

The original source of these files are:

  1. BAM file: HG002_NIST_150bp_50x.bam

    The original FASTQ file comes from the PrecisionFDA Truth Challenge. I ran it through PrecisionFDA's BWA-MEM app with default setting, and then got the HG002_NIST_150bp_50x.bam file as output. The FASTQ files are originally from the Genome in a Bottle Consortium. For more information, see this Scientific Data paper.

  2. FASTA file: hs37d5.fa.gz

    The original file came from: ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_reference_assembly_sequence. Because DeepVariant requires bgzip files, we had to unzip and bgzip it, and create corresponding index files.

  3. Truth VCF and BED

    These come from NIST, as part of the Genome in a Bottle project. They are downloaded from ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG002_NA24385_son/NISTv3.3.2/GRCh37/

You can simply run the command below to get all the data you need for this case study.

time gsutil -m cp -r "${DATA_BUCKET}/*" "${DATA_DIR}"

It took us about 15min to copy the files.

Run make_examples

First, to set up,

sudo docker pull gcr.io/deepvariant-docker/deepvariant:"${BIN_VERSION}"

Because the informational log messages (written to stderr) are voluminous, and because this takes a long time to finish, we will redirect all the output (stdout and stderr) to a file.

( time seq 0 $((N_SHARDS-1)) | \
  parallel --halt 2 --joblog "${LOG_DIR}/log" --res "${LOG_DIR}" \
    sudo docker run \
      -v /home/${USER}:/home/${USER} \
      gcr.io/deepvariant-docker/deepvariant:"${BIN_VERSION}" \
      /opt/deepvariant/bin/make_examples \
      --mode calling \
      --ref "${REF}" \
      --reads "${BAM}" \
      --examples "${EXAMPLES}" \
      --gvcf "${GVCF_TFRECORDS}" \
      --task {} \
) >"${LOG_DIR}/make_examples.log" 2>&1

This will take several hours to run.

Run call_variants

There are different ways to run call_variants. In this case study, we ran just one call_variants job. Here's the command that we used:

( time sudo docker run \
    -v /home/${USER}:/home/${USER} \
    gcr.io/deepvariant-docker/deepvariant:"${BIN_VERSION}" \
    /opt/deepvariant/bin/call_variants \
    --outfile "${CALL_VARIANTS_OUTPUT}" \
    --examples "${EXAMPLES}" \
    --checkpoint "${MODEL}"
) >"${LOG_DIR}/call_variants.log" 2>&1

This will start one process of call_variants and read all 64 shards of HG002.examples.tfrecord@64.gz as input, but output to one single output file HG002.cvo.tfrecord.gz.

We add --batch_size 32 here because we noticed a significant slowdown after the Meltdown/Spectre CPU patch if we continue to use the default batch_size (512).

We noticed that running multiple call_variants on the same machine didn't seem to save the overall time, because each of the call_variants slowed down when multiple are running on the same machine.

So if you care about finishing the end-to-end run faster, you could request 64 machines (for example, n1-standard-8 machines) and run each shard as input separately, and output to corresponding output shards. For example, the first machine will run this command:

time sudo docker run \
  -v /home/${USER}:/home/${USER} \
  gcr.io/deepvariant-docker/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/call_variants \
  --outfile=${OUTPUT_DIR}/HG002.cvo.tfrecord-00000-of-00064.gz \
  --examples=${OUTPUT_DIR}/HG002.examples.tfrecord-00000-of-00064.gz \
  --checkpoint="${MODEL}"

And the rest will process 00001 to 00063. You can also use tools like dsub. In this case study we only report the runtime on a 1-machine example.

Run postprocess_variants

( time sudo docker run \
    -v /home/${USER}:/home/${USER} \
    gcr.io/deepvariant-docker/deepvariant:"${BIN_VERSION}" \
    /opt/deepvariant/bin/postprocess_variants \
    --ref "${REF}" \
    --infile "${CALL_VARIANTS_OUTPUT}" \
    --outfile "${OUTPUT_VCF}"
) >"${LOG_DIR}/postprocess_variants.log" 2>&1

Because this step is single-process, single-thread, if you're orchestrating a more complicated running pipeline, you might want to request a machine with fewer cores for this step.

If you want to create a gVCF output file, two additional flags must be passed to the postprocess_variants step, so the full call would look instead like:

( time sudo docker run \
    -v /home/${USER}:/home/${USER} \
    gcr.io/deepvariant-docker/deepvariant:"${BIN_VERSION}" \
    /opt/deepvariant/bin/postprocess_variants \
    --ref "${REF}" \
    --infile "${CALL_VARIANTS_OUTPUT}" \
    --outfile "${OUTPUT_VCF}" \
    --nonvariant_site_tfrecord_path "${GVCF_TFRECORDS}" \
    --gvcf_outfile "${OUTPUT_GVCF}"
) >"${LOG_DIR}/postprocess_variants.withGVCF.log" 2>&1

Resources used by each step

Step wall time
make_examples 1h 45m 46s
call_variants 3h 25m 38s
postprocess_variants (no gVCF) 21m 33s
postprocess_variants (with gVCF) 55m 47s
total time (single machine) 5h 33m - 6h 07m

Variant call quality

Here we use the hap.py (https://github.com/Illumina/hap.py) program from Illumina to evaluate the resulting vcf file. This serves as a check to ensure the three DeepVariant commands ran correctly and produced high-quality results.

UNCOMPRESSED_REF="${OUTPUT_DIR}/hs37d5.fa"

# hap.py cannot read the compressed fa, so uncompress
# into a writable directory and index it.
zcat <"${REF}" >"${UNCOMPRESSED_REF}"
samtools faidx "${UNCOMPRESSED_REF}"

sudo docker pull pkrusche/hap.py
sudo docker run -it \
-v "${DATA_DIR}:${DATA_DIR}" \
-v "${OUTPUT_DIR}:${OUTPUT_DIR}" \
pkrusche/hap.py /opt/hap.py/bin/hap.py \
  "${TRUTH_VCF}" \
  "${OUTPUT_VCF}" \
  -f "${TRUTH_BED}" \
  -r "${UNCOMPRESSED_REF}" \
  -o "${OUTPUT_DIR}/happy.output" \
  --engine=vcfeval
Type # FN # FP Recall Precision F1_Score
INDEL 1431 916 0.996921 0.998106 0.997513
SNP 1329 746 0.999564 0.999755 0.999660