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DeepVariant PacBio Iso-Seq/MAS-Seq Case Study

This case study will demonstrate how to run DeepVariant using the PacBio Iso-Seq/MAS-Seq model, and evaluate the result using hap.py.

Overview

Data

We will use these data in our analysis. Files will be downloaded in subsequent steps.

  • HG004 MAS-Seq BAM (chr20)
  • Model Checkpoint Files
  • GRCh38 Reference + Index
  • GIAB benchmark data

Prepare Data

Setup directories

Lets first create directories to organize files.

mkdir -p input benchmark reference output happy

Download the GRCh38 Reference

We will be using GRCh38 for this case study.

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 > reference/GRCh38_no_alt_analysis_set.fasta
curl ${FTPDIR}/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.fai > reference/GRCh38_no_alt_analysis_set.fasta.fai

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 HG004.

FTPDIR=ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG004_NA24143_mother/NISTv4.2.1/GRCh38

curl -L ${FTPDIR}/HG004_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed > benchmark/HG004_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed
curl -L ${FTPDIR}/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz > benchmark/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz
curl -L ${FTPDIR}/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi > benchmark/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz.tbi

Download HG004 BAM (chr20)

For this case study, we download the chr20 of a HG004 MAS-Seq BAM.

HTTPDIR=https://storage.googleapis.com/deepvariant/masseq-case-study

curl -L ${HTTPDIR}/HG004.giab_na24143.hifi_reads.lima.0--0.lima.IsoSeqX_bc01_5p--IsoSeqX_3p.refined.grch38.mm2.splitN.fc.chr20.bam > input/HG004.giab_na24143.hifi_reads.lima.0--0.lima.IsoSeqX_bc01_5p--IsoSeqX_3p.refined.grch38.mm2.splitN.fc.chr20.bam
curl -L ${HTTPDIR}/HG004.giab_na24143.hifi_reads.lima.0--0.lima.IsoSeqX_bc01_5p--IsoSeqX_3p.refined.grch38.mm2.splitN.fc.chr20.bam.bai > input/HG004.giab_na24143.hifi_reads.lima.0--0.lima.IsoSeqX_bc01_5p--IsoSeqX_3p.refined.grch38.mm2.splitN.fc.chr20.bam.bai

Download a BED file for evaluation

When evaluating, we will use a BED file that restricts to exon regions, and only include regions where the BAM file has 10x or more coverage.

HTTPDIR=https://storage.googleapis.com/deepvariant/masseq-case-study

curl -L ${HTTPDIR}/HG004.giab_na24143.hifi_reads.lima.0--0.lima.IsoSeqX_bc01_5p--IsoSeqX_3p.refined.grch38.mm2.splitN.fc.depth.10x.exons.bed > input/HG004.giab_na24143.hifi_reads.lima.0--0.lima.IsoSeqX_bc01_5p--IsoSeqX_3p.refined.grch38.mm2.splitN.fc.depth.10x.exons.bed

Running DeepVariant MAS-Seq on a CPU-only machine

The command below will run the DeepVariant MAS-Seq model and produce an output VCF.

BIN_VERSION="1.8.0"

sudo docker run \
  -v "${PWD}/input":"/input" \
  -v "${PWD}/output":"/output" \
  -v "${PWD}/reference":"/reference" \
  google/deepvariant:"${BIN_VERSION}" \
  run_deepvariant \
    --model_type=MASSEQ \
    --ref=/reference/GRCh38_no_alt_analysis_set.fasta \
    --reads=/input/HG004.giab_na24143.hifi_reads.lima.0--0.lima.IsoSeqX_bc01_5p--IsoSeqX_3p.refined.grch38.mm2.splitN.fc.chr20.bam \
    --output_vcf=/output/HG004.output.vcf.gz \
    --num_shards=$(nproc) \
    --regions=chr20 \
    --intermediate_results_dir=/output/intermediate_results_dir

Flag summary

  • --model_type - Sets the model and options for MAS-Seq data.
  • --customized_model - Points to a model trained using MAS-Seq data.
  • --ref - Specifies the reference sequence.
  • --reads - Specifies the input bam file.
  • --output_vcf - Specifies the output variant file.
  • --num_shards - Sets the number of shards to the number of available processors ($(nproc)). This is used to perform parallelization.
  • --regions - Restricts to chr20 to make this case study faster.
  • --intermediate_results_dir - Outputs results to an intermediate directory. This is optional. If you don't need the intermediate files, no need to specify this flag.

For running on GPU machines, or using Singularity instead of Docker, see Quick Start.

Benchmark on chr20

sudo docker run \
  -v "${PWD}/benchmark":"/benchmark" \
  -v "${PWD}/input":"/input" \
  -v "${PWD}/output":"/output" \
  -v "${PWD}/reference":"/reference" \
  -v "${PWD}/happy:/happy" \
  jmcdani20/hap.py:v0.3.12 /opt/hap.py/bin/hap.py \
    /benchmark/HG004_GRCh38_1_22_v4.2.1_benchmark.vcf.gz \
    /output/HG004.output.vcf.gz \
    -f /benchmark/HG004_GRCh38_1_22_v4.2.1_benchmark_noinconsistent.bed \
    -r /reference/GRCh38_no_alt_analysis_set.fasta \
    -o /happy/happy.output \
    --engine=vcfeval \
    --pass-only \
    -l chr20 \
    --target-regions=/input/HG004.giab_na24143.hifi_reads.lima.0--0.lima.IsoSeqX_bc01_5p--IsoSeqX_3p.refined.grch38.mm2.splitN.fc.depth.10x.exons.bed \
    --threads=$(nproc)

Flag summary

  • -f - Sets the benchmark regions (regions of interest that we want to benchmark.)
  • -r - Sets the reference genome.
  • -o - Specifies the output location.
  • --engine - Sets the variant comparison engine. See hap.py documentation for details.
  • --pass-only - Restricts benchmarking to variants that have passed all filters.
  • -l - Comma-separated list of locations.
  • --target-regions - Restricts analysis to given regions only.
  • --threads - Level of parallelization to use.

Output:

The above command should output the following results:

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          135       106        29          161        13         39      7      2       0.785185          0.893443        0.242236         0.835823                     NaN                     NaN                   2.628571                   2.651163
INDEL   PASS          135       106        29          161        13         39      7      2       0.785185          0.893443        0.242236         0.835823                     NaN                     NaN                   2.628571                   2.651163
  SNP    ALL         1002       935        67          978        12         31      7      1       0.933134          0.987328        0.031697         0.959466                2.795455                2.761538                   2.083077                   2.046729
  SNP   PASS         1002       935        67          978        12         31      7      1       0.933134          0.987328        0.031697         0.959466                2.795455                2.761538                   2.083077                   2.046729