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DeepVariant exome case study

Similar to the case study on whole genome sequencing data, in this study we describe applying DeepVariant to a real exome sample using a single machine.

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 Docker-based exome pipeline created for Google Cloud Platform.

Request a machine

Any sufficiently capable machine will do. For this case study, we used a 64-core non-preemptible instance with 128GiB and no GPU.

Preliminaries

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

BASE="${HOME}/exome-case-study"
BUCKET="gs://deepvariant"
BIN_VERSION="0.5.1"
MODEL_VERSION="0.5.0"
MODEL_CL="181413382"

# Note that we don't specify the CL number for the binary, only the bin version.
BIN_BUCKET="${BUCKET}/binaries/DeepVariant/${BIN_VERSION}/DeepVariant-${BIN_VERSION}+cl-*"
MODEL_BUCKET="${BUCKET}/models/DeepVariant/${MODEL_VERSION}/DeepVariant-inception_v3-${MODEL_VERSION}+cl-${MODEL_CL}.data-wes_standard"
DATA_BUCKET="${BUCKET}/exome-case-study-testdata"

INPUT_DIR="${BASE}/input"
BIN_DIR="${INPUT_DIR}/bin"
MODELS_DIR="${INPUT_DIR}/models"
MODEL="${MODELS_DIR}/model.ckpt"
DATA_DIR="${INPUT_DIR}/data"
REF="${DATA_DIR}/hs37d5.fa.gz"
BAM="${DATA_DIR}/151002_7001448_0359_AC7F6GANXX_Sample_HG002-EEogPU_v02-KIT-Av5_AGATGTAC_L008.posiSrt.markDup.bam"
TRUTH_VCF="${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"

CAPTURE_BED="${DATA_DIR}/agilent_sureselect_human_all_exon_v5_b37_targets.bed"

Create local directory structure

mkdir -p "${OUTPUT_DIR}"
mkdir -p "${BIN_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 install parallel
sudo apt-get -y install samtools
sudo apt-get -y install docker.io

Download binaries, models, and test data

Binaries

Copy our binaries from the cloud bucket.

time gsutil -m cp -r "${BIN_BUCKET}/*" "${BIN_DIR}"
chmod a+x "${BIN_DIR}"/*

This step should be very fast - it took us about 6 seconds when we tested.

Now, we need to install all prerequisites on the machine. Run this command:

cd "${BIN_DIR}"; time bash run-prereq.sh; cd -

In our test run it took about 1 min.

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:

BAM file:

151002_7001448_0359_AC7F6GANXX_Sample_HG002-EEogPU_v02-KIT-Av5_AGATGTAC_L008.posiSrt.markDup.bam

Downloaded from https://github.com/genome-in-a-bottle/giab_data_indexes/blob/master/AshkenazimTrio/alignment.index.AJtrio_OsloUniversityHospital_IlluminaExome_bwamem_GRCh37_11252015

FASTA

Same as described in the case study for whole genome data

Truth VCF and BED

HG002_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-22_v.3.3.2_highconf_* are from NIST, as part of the Genomes 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/

Capture target BED file

According to the paper "Extensive sequencing of seven human genomes to characterize benchmark reference materials", the HG002 exome was generated with Agilent SureSelect. In this case study we'll use the SureSelect v5 BED (agilent_sureselect_human_all_exon_v5_b37_targets.bed) and intersect it with the GIAB confident regions for evaluation.

Copy the data

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 a few minuntes to copy the files.

Run make_examples

In this step, we used the --regions flag to constrain the regions we processed to the capture region BED file:

( time seq 0 $((N_SHARDS-1)) | \
  parallel --halt 2 --joblog "${LOG_DIR}/log" --res "${LOG_DIR}" \
    python "${BIN_DIR}"/make_examples.zip \
      --mode calling \
      --ref "${REF}" \
      --reads "${BAM}" \
      --examples "${EXAMPLES}" \
      --regions "${CAPTURE_BED}" \
      --gvcf "${GVCF_TFRECORDS}" \
      --task {}
) >"${LOG_DIR}/make_examples.log" 2>&1

Timing information is included in a later section.

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 python "${BIN_DIR}"/call_variants.zip \
    --outfile "${CALL_VARIANTS_OUTPUT}" \
    --examples "${EXAMPLES}" \
    --checkpoint "${MODEL}" \
    --batch_size 32
) >"${LOG_DIR}/call_variants.log" 2>&1

More discussion can be found in the call_variants section in the case study.

Run postprocess_variants

( time python "${BIN_DIR}"/postprocess_variants.zip \
    --ref "${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

The last two flags are optional, only if gVCF outputs are desired. More discussion can be found in the postprocess_variants section in the case study.

Resources used by each step

Step wall time
make_examples 69m 22s
call_variants 6m 32s
postprocess_variants (no gVCF) 0m 13s
postprocess_variants (with gVCF) 1m 29s
total time (single machine) ~ 1h 17m

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.

To set up:

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

We evaluate against the capture region:

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}" \
  --preprocess-truth \
  -f "${TRUTH_BED}" \
  -T "${CAPTURE_BED}" \
  -r "${UNCOMPRESSED_REF}" \
  -o "${OUTPUT_DIR}/happy.output"

Here are the results:

Type # FN # FP Recall Precision F1_Score
INDEL 150 48 0.943117 0.981080 0.961724
SNP 46 24 0.998636 0.999288 0.998962

Separate models for calling whole genome and exome data

In the DeepVariant 0.5.* release, we recommend a separate model for calling exome sequencing data. Here is how the exome model is trained: we used a WGS model as the starting checkpoint (instead of an ImageNet one), and trained only on examples created from exome data.