Skip to content

Latest commit

 

History

History
169 lines (128 loc) · 6.75 KB

deepvariant-ont-r104-simplex-case-study.md

File metadata and controls

169 lines (128 loc) · 6.75 KB
Raw

DeepVariant with Oxford Nanopore R10.4.1 Simplex reads

In this case study, we describe applying DeepVariant to Oxford Nanopore R10.4.1 simplex reads. Then we assess the quality of the DeepVariant variant calls with hap.py.

To make it faster to go over this case study, we run only on chromosome 20.

The dataset used in this case-study has following attributes:

Sample: HG003
Region: Chr20
Chemistry: ONT R10.4.1
Coverage: 80x

Model note:

  • The model is trained with Guppy 6+ "SUP" Simplex and Dorado v0.1.1 Duplex reads.

  • The model is trained on both Ultra-long and sheared reads with varying read N50 and coverage.

Prepare environment

In this case-study, we will use Docker to run DeepVariant for variant calling and hap.py for benchmarking.

If you want to run on GPU machines, or use Singularity instead of Docker, please follow Quick Start documentation.

Create input and output directory structures and download inputs

BASE="${HOME}/ont-case-study"

# Set up input and output directory data
INPUT_DIR="${BASE}/input/data"
OUTPUT_DIR="${BASE}/output"

## Create local directory structure
mkdir -p "${INPUT_DIR}"
mkdir -p "${OUTPUT_DIR}"

# Download reference to input directory
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 > ${INPUT_DIR}/GRCh38_no_alt_analysis_set.fasta
curl ${FTPDIR}/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.fai > ${INPUT_DIR}/GRCh38_no_alt_analysis_set.fasta.fai

# Download HG003 Ultra-long chr20 bam file to input directory
HTTPDIR=https://storage.googleapis.com/deepvariant/ont-case-study-testdata
curl ${HTTPDIR}/HG003_R104_sup_merged.80x.chr20.bam > ${INPUT_DIR}/HG003_R104_sup_merged.80x.chr20.bam
curl ${HTTPDIR}/HG003_R104_sup_merged.80x.chr20.bam.bai > ${INPUT_DIR}/HG003_R104_sup_merged.80x.chr20.bam.bai

# Set up input variables
REF="GRCh38_no_alt_analysis_set.fasta"
BAM="HG003_R104_sup_merged.80x.chr20.bam"
THREADS=$(nproc)
REGION="chr20"

# Set up output variable
OUTPUT_VCF="HG003_UL_R1041_Guppy6_sup_2_GRCh38.chr20.output.vcf.gz"
OUTPUT_GVCF="HG003_UL_R1041_Guppy6_sup_2_GRCh38.output.g.vcf.gz"
INTERMEDIATE_DIRECTORY="intermediate_results_dir"

mkdir -p "${OUTPUT_DIR}/${INTERMEDIATE_DIRECTORY}"

Run DeepVariant

We will run DeepVariant from docker using the run_deepvariant script.

BIN_VERSION="1.8.0"

sudo docker run \
  -v "${INPUT_DIR}":"${INPUT_DIR}" \
  -v "${OUTPUT_DIR}":"${OUTPUT_DIR}" \
  google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/run_deepvariant \
  --model_type ONT_R104 \
  --ref "${INPUT_DIR}/${REF}" \
  --reads "${INPUT_DIR}/${BAM}" \
  --output_vcf "${OUTPUT_DIR}/${OUTPUT_VCF}" \
  --output_gvcf "${OUTPUT_DIR}/${OUTPUT_GVCF}" \
  --num_shards "${THREADS}" \
  --regions "${REGION}" \
  --intermediate_results_dir "${OUTPUT_DIR}/${INTERMEDIATE_DIRECTORY}"

By specifying --model_type ONT_R104, you'll be using a model that is best suited for Oxford Nanopore R10.4.1 chemistry Simplex and Duplex reads.

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.tfrecord.gz
gvcf.tfrecord-?????-of-?????.gz
make_examples.tfrecord-?????-of-?????.gz

Benchmark HG003 chr20 output from DeepVariant

We will use Genome-in-a-Bottle (GIAB) dataset to evaluate the performance of DeepVariant.

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 HG003.

FTPDIR=ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG003_NA24149_father/NISTv4.2.1/GRCh38

curl ${FTPDIR}/HG003_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed > ${INPUT_DIR}/HG003_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed
curl ${FTPDIR}/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz > ${INPUT_DIR}/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz
curl ${FTPDIR}/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi > ${INPUT_DIR}/HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi

TRUTH_VCF="HG003_GRCh38_1_22_v4.2.1_benchmark.vcf.gz"
TRUTH_BED="HG003_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed"
sudo docker pull jmcdani20/hap.py:v0.3.12

sudo docker run \
  -v "${INPUT_DIR}":"${INPUT_DIR}" \
  -v "${OUTPUT_DIR}":"${OUTPUT_DIR}" \
  -v "${PWD}/happy:/happy" \
  jmcdani20/hap.py:v0.3.12 /opt/hap.py/bin/hap.py \
  "${INPUT_DIR}/${TRUTH_VCF}" \
  "${OUTPUT_DIR}/${OUTPUT_VCF}" \
  -f "${INPUT_DIR}/${TRUTH_BED}" \
  -r "${INPUT_DIR}/${REF}" \
  -o "${OUTPUT_DIR}/hg003.ul.r104.ont.chr20.happy.output" \
  --engine=vcfeval \
  --pass-only \
  -l "${REGION}"

Output:

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        10628      9408      1220        19574       912       8954    391    305       0.885209          0.914124        0.457444         0.899434                     NaN                     NaN                   1.748961                   2.269171
INDEL   PASS        10628      9408      1220        19574       912       8954    391    305       0.885209          0.914124        0.457444         0.899434                     NaN                     NaN                   1.748961                   2.269171
  SNP    ALL        70166     70100        66       103527        50      33353     20     24       0.999059          0.999287        0.322167         0.999173                2.296566                1.773367                   1.883951                   1.794260
  SNP   PASS        70166     70100        66       103527        50      33353     20     24       0.999059          0.999287        0.322167         0.999173                2.296566                1.773367                   1.883951                   1.794260

Acknowledgement

For providing analysis results and expertise, we are thankful to:

  • Karen Miga, Brandy McNulty, Jean Monlong, Benedict Paten from UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA.
  • Miten Jain from Department of Bioengineering, Department of Physics, Northeastern University, Boston, MA.