Run DeepVariant

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

This tutorial explains how to run DeepVariant on Google Cloud using sample data. You run DeepVariant on a single Compute Engine instance.

Objectives

After completing this tutorial, you'll know how to run DeepVariant on Google Cloud.

Costs

This tutorial uses billable components of Google Cloud, including:

• Compute Engine

Generate a cost estimate based on your projected usage by using the Pricing Calculator. New Cloud Platform users might be eligible for a free trial.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud Console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Cloud project. Learn how to confirm that billing is enabled for your project.

4. Enable the Compute Engine API.

   Enable the API

5. Install and initialize the Cloud SDK.

Tip: Need a command prompt? You can use the Cloud Shell. The Cloud Shell is a command-line environment that already includes the Cloud SDK, so you don't need to install it.

Create a Compute Engine instance

Create a Compute Engine instance using the Google Cloud Console or the gcloud tool to run DeepVariant.

Console

1. In the Cloud Console, go to the VM Instances page.

   Go to the VM Instances page

2. Click Create instance.
3. Choose a Name for the instance in the format PROJECT_ID-deepvariant-run where PROJECT_ID is the ID for your Google Cloud project.
4. Choose a Region and Zone for the instance. Unless you have a specific reason to run the instance in a certain location, select us-central1 (Iowa) for the Region and us-central1-a for the Zone.
5. In the Machine type menu, select n1-standard-64 (64 vCPU, 240 GB memory).
6. In the CPU platform menu, select Intel Skylake or later.
7. In the Boot disk section, click Change to begin configuring your boot disk.
8. On the OS images tab, choose Google Drawfork Ubuntu 16.04 LTS. In the Boot disk type menu, select Standard persistent disk. In the Size (GB) field, enter 300. Click Select.
9. Click Create to create the instance.

gcloud

gcloud compute instances create \
    PROJECT_ID-deepvariant-run \
    --project PROJECT_ID \
    --zone ZONE \
    --scopes "cloud-platform" \
    --image-project ubuntu-os-cloud \
    --image-family ubuntu-1604-lts \
    --machine-type n1-standard-64 \
    --min-cpu-platform "Intel Skylake" \
    --boot-disk-size=300GB

Replace the following:

• PROJECT_ID: your Google Cloud project ID
• ZONE: the zone in which your instance is deployed. A zone is an approximate regional location in which your instance and its resources live. For example, us-west1-a is a zone in the us-west region. If you've set a default zone previously using gcloud config set compute/zone, the value of this flag overrides that default.

Allow a short time for the instance to start up. After it's ready, it will appear on the VM Instances page with a green status icon.

Connect to the instance

You can connect to the instance using either the Cloud Console or the gcloud tool:

Console

1. In the Cloud Console, go to the VM Instances page.

   Go to the VM Instances page

2. In the list of virtual machine instances, click SSH in the row of the instance that you created.

gcloud

gcloud compute ssh PROJECT_ID-deepvariant-run --zone ZONE

Run DeepVariant

Configure your environment and run DeepVariant on the Compute Engine instance you created:

1. Install Docker Community Edition (CE):

   sudo apt-get -qq -y install \
     apt-transport-https \
     ca-certificates \
     curl \
     gnupg-agent \
     software-properties-common
   curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
   sudo add-apt-repository \
     "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
     $(lsb_release -cs) \
     stable"
   sudo apt-get -qq -y update
   sudo apt-get -qq -y install docker-ce
   

2. Configure the DeepVariant environment variables by copying and pasting the following commands into your local environment:

   BIN_VERSION="1.1.0"
   BASE="${HOME}/deepvariant-run"
   INPUT_DIR="${BASE}/input"
   REF="GRCh38_no_alt_analysis_set.fasta"
   BAM="HG003.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam"
   OUTPUT_DIR="${BASE}/output"
   DATA_DIR="${INPUT_DIR}/data"
   OUTPUT_VCF="HG003.output.vcf.gz"
   OUTPUT_GVCF="HG003.output.g.vcf.gz"
   

3. Create the local directory structure for the input data directory and the output directory:

   mkdir -p "${OUTPUT_DIR}"
   mkdir -p "${INPUT_DIR}"
   mkdir -p "${DATA_DIR}"
   

4. This tutorial uses a publicly available HG003 genome at 30x coverage mapped to GRCh38 reference. To ensure a quicker runtime, add the --regions chr20 flag when you run DeepVariant so that DeepVariant only runs on chromosome 20 (chr20).

   The sample data was created using Illumina sequencing, but DeepVariant also supports these other types of input data:

   • Whole genome (Illumina) (WGS)
   • Exome (Illumina) (WES)
   • Whole genome (PacBio)
   • Whole genome PacBio and Illumina hybrid (HYBRID_PACBIO_ILLUMINA)

   Run gsutil cp to copy the input test data from the deepvariant Cloud Storage bucket to the directories on the instance you created:

   # Input BAM and BAI files:
   gsutil cp gs://deepvariant/case-study-testdata/"${BAM}" "${DATA_DIR}"
   gsutil cp gs://deepvariant/case-study-testdata/"${BAM}".bai "${DATA_DIR}"
   
   # GRCh38 reference FASTA file:
   FTPDIR=ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids
   curl ${FTPDIR}/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz | gunzip > "${DATA_DIR}/${REF}"
   curl ${FTPDIR}/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.fai > "${DATA_DIR}/${REF}".fai
   

5. DeepVariant is a containerized application staged in a pre-built Docker image in Container Registry. To pull the image, run the following command:

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

6. To start DeepVariant, run the following command:

   sudo docker run \
       -v "${DATA_DIR}":"/input" \
       -v "${OUTPUT_DIR}:/output" \
       gcr.io/deepvariant-docker/deepvariant:"${BIN_VERSION}"  \
       /opt/deepvariant/bin/run_deepvariant \
       --model_type=WGS \
       --ref="/input/${REF}" \
       --reads="/input/${BAM}" \
       --output_vcf=/output/${OUTPUT_VCF} \
       --output_gvcf=/output/${OUTPUT_GVCF} \
       --regions chr20 \
       --num_shards=$(nproc) \
       --intermediate_results_dir /output/intermediate_results_dir
   

   The following table describes the flags passed to the command:

   Flag	Description
   model_type	DeepVariant supports several different types of input data. This tutorial uses Whole Genome Sequencing (WSG).
   ref	The location of the reference FASTA file.
   reads	The location of the input BAM file.
   output_vcf	The location of the output VCF files.
   output_gvcf	The location of the output gVCF files.
   regions	(Optional) A space-separated list of chromosome regions to process. Individual elements can be region literals, such as chr20:10-20 or paths to BED/BEDPE files.
   num_shards	The number of shards to run in parallel. For best results, set the value of this flag to the number of cores on the machine where DeepVariant runs.
   intermediate_results_dir	Optional flag specifying the directory for the intermediate outputs of make_examples and call_variants stages. After the command completes, the files will be saved to your local directory in the following formats:

   call_variants_output.tfrecord.gz
   gvcf.tfrecord-SHARD_NUMBER-of-NUM_OF_SHARDS.gz
   make_examples.tfrecord-SHARD_NUMBER-of-NUM_OF_SHARDS.gz
   

   If the command is successful, it outputs a message starting with the following:

   ***** Running the command:*****
   time seq 0 63 | parallel
   -k
   --line-buffer /opt/deepvariant/bin/make_examples
   --mode calling
   --ref "/input/GRCh38_no_alt_analysis_set.fasta"
   --reads "/input/HG003.novaseq.pcr-free.35x.dedup.grch38_no_alt.chr20.bam"
   --examples "/output/intermediate_results_dir/[e-mail]"
   --regions "chr20"
   --gvcf "/output/intermediate_results_dir/[e-mail]"
   --task {}
   

7. After DeepVariant finishes, it outputs the following files to the deepvariant-run/output directory:

   • HG003.output.g.vcf.gz
   • HG003.output.g.vcf.gz.tbi
   • HG003.output.vcf.gz
   • HG003.output.vcf.gz.tbi
   • HG003.output.visual_report.html

   Run the following command to list the files in the output directory, and check that the output files display:

   ls $OUTPUT_DIR
   

Cost and runtime estimates

The following table shows the approximate runtime and costs when running DeepVariant using a 30x whole genome sample in a BAM file. These estimates do not include the time required to set up the instance and download any sample data from Cloud Storage.

The table contains estimates for preemptible VMs and non-preemptible VMs. The runtime estimates are based on using non-preemptible VMs.

Preemptible VMs are up to 80% cheaper than regular VMs. However, if Compute Engine requires access to the preemptible VMs for other tasks, Compute Engine might terminate (preempt) the VMs. Preemptible VMs are not covered by any Service Level Agreement (SLA). If you require guarantees on turnaround time, do not use the --preemptible flag.

See Compute Engine best practices for how to effectively use preemptible VMs.

Machine type	Runtime in hours	Cost (non-preemptible)	Cost (preemptible)
n1-standard-8	27.6	$11.3	$3.04
n1-standard-16	15.4	$12.1	$2.92
n1-standard-32	9.47	$14.7	$3.32
n1-standard-64	6.8	$20.9	$4.55
n1-standard-96	5.58	$25.6	$5.53

Clean up

After you finish the tutorial, you can clean up the resources that you created so that they stop using quota and incurring charges. The following sections describe how to delete or turn off these resources.

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple tutorials and quickstarts, reusing projects can help you avoid exceeding project quota limits.

1. In the Cloud Console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.

What's next

• There are more complex configurations available in the DeepVariant GitHub repository. For example, you can run DeepVariant using multiple instances. These variations provide improvements in processing speed and reduced costs.
• Read a Google AI blog post about DeepVariant's open source release.
• If you have question about DeepVariant, you can file a GitHub issue. If you have questions about Google Cloud, you can post to the [e-mail] mailing list.