Oxford Nanopore variant calling workflow (Limited support release)

In collaboration with the DeepVariant group, we are developing a haplotype-aware variant calling pipeline for Oxford Nanopore sequencing technology. We are releasing the pipeline with limited support until we finalize the pipeline.

PrecisionFDA truth challenge v2 results

We participated in the PrecisionFDA truth challenge V2 and won two awards in the ONT category.

A public table with more detailed performance metrics for all submissions is available here and a details of the table values can be found here.

Here we summarize the variant calling performance of the winning submissions of Illumina and ONT in all benchmark regions and Difficult-to-map regions category. The numbers are directly lifted from the pFDA results table.

Difficult-to-Map Regions

Type	Team	Technology	Pipeline	True positives	False negatives	False positives	Recall	Precision	F1-Score
SNP	UCSC CGL-Google Health	ONT	PEPPER-DeepVariant	257460	3841	2465	0.98528	0.99045	0.98785
SNP	DRAGEN	Illumina	DRAGEN	251058	10245	5610	0.96078	0.97811	0.96936
INDEL	UCSC CGL-Google Health	ONT	PEPPER-DeepVariant	13815	2673	670	0.83780	0.95408	0.89217
INDEL	DRAGEN	Illumina	DRAGEN	15883	605	420	0.96322	0.97447	0.96881

All benchmarking regions

Type	Team	Technology	Pipeline	True positives	False negatives	False positives	Recall	Precision	F1-Score
SNP	UCSC CGL-Google Health	ONT	PEPPER-DeepVariant	3328756	14716	8914	0.99558	0.99731	0.99645
SNP	DRAGEN	Illumina	DRAGEN	3331542	11973	7575	0.99641	0.99773	0.99707
INDEL	UCSC CGL-Google Health	ONT	PEPPER-DeepVariant	320171	187923	52012	0.63009	0.86333	0.72849
INDEL	DRAGEN	Illumina	DRAGEN	506539	1588	1437	0.99687	0.99728	0.99708

Limited support statement

We are releasing this pipeline with limited support as we are actively developing the pipeline. This pipeline is developed outside the main repository of DeepVariant. If you have issues with running this pipeline, please open a github issue in this repository (PEPPER). We will update this page as we evaluate the pipeline across all platforms.

Requirements

We are releasing this pipeline executable only via docker. Other executables will be provided as we finalize the pipeline.

Sequencing data requirements

We expect the input files meet the following requirements to produce results that we have reported.

Alignment file (BAM):
      Coverage:   ~50-80x
      Pore:       R9.4.1
      Basecaller: Guppy 3.6.0
      Platform:   PromethION or MinION

Reference file:
      Standard release of GRCh38 or GRCh37.
      We have not tested this pipeline on references with smaller contigs i.e. assemblies.

We have not yet tested the pipeline on different pores and devices. We will update this document as we validate the pipeline.

Disk-space requirements

As we use WhatsHap to phase the BAM file, please expect disk-space usage of ~4x the size of the BAM file.

HG002 chr20 case-study

NOTE: This is a chromosome-level case-study. Please see the Quickstart section of this document for demonstration of this pipeline on a smaller dataset.

We evaluated this pipeline on ~50x HG002 data. The data is publicly available, please feel free to download, run and evaluate the pipeline.

Sample:     HG002
Coverage:   ~50-80x
Basecaller: Guppy 3.6.0
Region:     chr20
Reference: GRCh38_no_alt

Command-line instructions

Step 1: Install docker

Please install docker and wget if you don't have it installed already.

# Install wget to download data files.
sudo apt-get -qq -y update
sudo apt-get -qq -y install wget

# Install docker using instructions on:
# https://docs.docker.com/install/linux/docker-ce/ubuntu/
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
docker --version

Step 2: Run PEPPER-DeepVariant

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

# Set up input data
INPUT_DIR="${BASE}/input/data"
REF="GRCh38_no_alt_chr20.fa"
BAM="HG002_prom_R941_guppy360_2_GRCh38_ch20.bam"
TRUTH_VCF="HG002_GRCh38_GIABv4.1.vcf.gz"
TRUTH_BED="HG002_GRCh38_1_22_v4.1_draft_benchmark.bed"

# Set the number of CPUs to use
THREADS="64"

# Set up output directory
OUTPUT_DIR="${BASE}/output"
OUTPUT_VCF="PEPPER_HP_DEEPVARIANT_FINAL_OUTPUT.vcf.gz"

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

# Download the data to input directory
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-casestudy/HG002_prom_R941_guppy360_2_GRCh38_ch20.bam
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-casestudy/HG002_prom_R941_guppy360_2_GRCh38_ch20.bam.bai
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-casestudy/GRCh38_no_alt_chr20.fa
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-casestudy/GRCh38_no_alt_chr20.fa.fai
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-casestudy/HG002_GRCh38_GIABv4.1.vcf.gz
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-casestudy/HG002_GRCh38_1_22_v4.1_draft_benchmark.bed

## Pull the docker image.
sudo docker pull kishwars/pepper_deepvariant_cpu:latest

# Run PEPPER-DeepVariant High-Accuracy mode on CPU
# For other versions and GPU specific version, please consult the How to run section of this document
sudo docker run --ipc=host \
-v "${INPUT_DIR}":"${INPUT_DIR}" \
-v "${OUTPUT_DIR}":"${OUTPUT_DIR}" \
kishwars/pepper_deepvariant_cpu:latest \
/opt/run_pepper_deepvariant.sh \
-f "${INPUT_DIR}/${REF}" \
-b "${INPUT_DIR}/${BAM}" \
-o "${OUTPUT_DIR}" \
-t ${THREADS} \
-x 1

Evaluation using hap.py (Optional)

You can evaluate the variants using hap.py.

# Pull the docker image
sudo docker pull pkrusche/hap.py:latest

# Run hap.py
sudo docker run -it \
-v "${INPUT_DIR}":"${INPUT_DIR}" \
-v "${OUTPUT_DIR}":"${OUTPUT_DIR}" \
pkrusche/hap.py:latest \
/opt/hap.py/bin/hap.py \
${INPUT_DIR}/${TRUTH_VCF} \
${OUTPUT_DIR}/${OUTPUT_VCF} \
-f "${INPUT_DIR}/${TRUTH_BED}" \
-r "${INPUT_DIR}/${REF}" \
-o "${OUTPUT_DIR}/happy.output" \
--pass-only \
-l chr20 \
--engine=vcfeval \
--threads="${THREADS}"

Run-time and performance

You can run this pipeline on run-time efficient mode or high-accuracy mode (high-accuracy mode enabled by setting parameter -x 1). The difference between these two modes is the haplotype-aware model used for DeepVariant. The high-accuracy mode uses a better feature representation known as rows in DeepVariant that results in higher accuracy but slower run-time.

We have tested the high-accuracy and run-time efficient mode of this pipeline on ~50x HG002 chr20 data.

Compute node description:

CPUs:
  CPU(s):                          32
  CPU MHz:                         1979.549
  CPU max MHz:                     3400.0000
  CPU min MHz:                     2200.0000
GPU:
  GPU(s):                          1
  GPU Model:                       GeForce GTX 1080ti
  GPU Memory:                      11177MiB

Run-time analysis:

Mode	Platform	Sample	Region	Coverage	Run-time (wall-clock)
Run-time	CPU	HG002	chr20	~50x	118.34 mins
Run-time	GPU	HG002	chr20	~50x	71.03 mins
High-accuracy	CPU	HG002	chr20	~50x	211.92 mins
High-accuracy	GPU	HG002	chr20	~50x	164.71 mins

Variant-calling performance:

Mode	Type	Truth Total	True positives	False negatives	False positives	Recall	Precision	F1-Score
Run-time	INDEL	11271	6318	4953	1826	0.560554	0.778935	0.651942
Run-time	SNP	71334	71037	297	191	0.995836	0.997319	0.996577
High-accuracy	INDEL	11271	6393	4878	1556	0.567208	0.80733	0.666295
High-accuracy	SNP	71334	71054	280	209	0.996075	0.997068	0.996571

Whole-genome evaluations

We evaluated our pipeline on three samples (HG002-HG003-HG004). At the time of this release, only v3.3.2 truth was available for HG003 and HG004. Data description:

Sample:     HG002, HG003, HG004
Coverage:   ~50x, ~80x, ~80x
Basecaller: Guppy 3.6.0
Region:     WGS
Referrence: GRCh38_no_alt

Here we report the whole genome performance on three samples.

Reference	Truth	Sample	Coverage	Type	Recall	Precision	F1 Score
GRCh37	GIAB v3.3.2	HG002	~45-55x	SNV	0.99605	0.996219	0.996135
GRCh37	GIAB v3.3.2	HG003	~75-85x	SNV	0.99546	0.997782	0.99662
GRCh37	GIAB v3.3.2	HG004	~75-85x	SNV	0.996939	0.996764	0.996852

How to run

We have combined all of the steps to run sequentially using one script. You can run the pipeline on your CPU-only or GPU machines using the command-line instructions provided here. Please see Quickstart section or HG002 chr20 case-study which provides example datasets.

Running on a CPU-only machine (Whole genome run)

Running the variant calling pipeline is as simple as: On a CPU-based machine:

# For Run-time efficient mode:
docker run --ipc=host \
-v /input_dir:/input_dir \
-v /output_dir:/output_dir \
kishwars/pepper_deepvariant_cpu:latest \
/opt/run_pepper_deepvariant.sh \
-b </input/bam_file.bam> \
-f </input/reference_file.fasta> \
-o </output/output_dir/> \
-t <number_of_threads>

# For High-Accuracy mode:
docker run --ipc=host \
-v /input_dir:/input_dir \
-v /output_dir:/output_dir \
kishwars/pepper_deepvariant_cpu:latest \
/opt/run_pepper_deepvariant.sh \
-b </input/bam_file.bam> \
-f </input/reference_file.fasta> \
-o </output/output_dir/> \
-t <number_of_threads> \
-x 1

Running on a GPU machine (Whole genome run)

On a GPU based machine (make sure to install nvidia-docker):

# For Run-time efficient mode:
docker run --ipc=host \
-v /input_dir:/input_dir \
-v /output_dir:/output_dir \
--gpus all \
kishwars/pepper_deepvariant_gpu:latest \
/opt/run_pepper_deepvariant.sh \
-b </input/bam_file.bam> \
-f </input/reference_file.fasta> \
-o </output/output_dir/> \
-t <number_of_threads>

# For High-Accuracy mode:
docker run --ipc=host \
-v /input_dir:/input_dir \
-v /output_dir:/output_dir \
--gpus all \
kishwars/pepper_deepvariant_gpu:latest \
/opt/run_pepper_deepvariant.sh \
-b </input/bam_file.bam> \
-f </input/reference_file.fasta> \
-o </output/output_dir/> \
-t <number_of_threads> \
-x 1

Quickstart

Here is an example on how to run the pipeline on a small example. Please see the HG002 chr20 case-study section of this document for a chromosome level evaluation.

Step 1: Install docker

Please install docker and wget if you don't have it installed already.

# Install wget to download data files.
sudo apt-get -qq -y update
sudo apt-get -qq -y install wget

# Install docker using instructions on:
# https://docs.docker.com/install/linux/docker-ce/ubuntu/
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
docker --version

Step 2: Run PEPPER-DeepVariant

BASE="${HOME}/ont-quickstart"

# Set up input data
INPUT_DIR="${BASE}/input/data"
REF="GRCh38_no_alt_chr20.fa"
BAM="HG002_prom_R941_guppy360_2_GRCh38_TEST.bam"
TRUTH_VCF="HG002_GRCh38_GIABv4.1.TRUTH.TEST.vcf.gz"
TRUTH_BED="HG002_GRCh38_GIABv4.1.TRUTH.TEST.bed"

# Set the number of CPUs to use
THREADS="1"

# Set up output directory
OUTPUT_DIR="${BASE}/output"
OUTPUT_VCF="PEPPER_HP_DEEPVARIANT_FINAL_OUTPUT.vcf.gz"

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

# Download the data to input directory
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-quickstart/HG002_prom_R941_guppy360_2_GRCh38_TEST.bam
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-quickstart/HG002_prom_R941_guppy360_2_GRCh38_TEST.bam.bai
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-quickstart/GRCh38_no_alt_chr20.fa
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-quickstart/GRCh38_no_alt_chr20.fa.fai
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-quickstart/HG002_GRCh38_GIABv4.1.TRUTH.TEST.bed
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-quickstart/HG002_GRCh38_GIABv4.1.TRUTH.TEST.vcf.gz
wget -P ${INPUT_DIR} https://storage.googleapis.com/kishwar-helen/ont-quickstart/HG002_GRCh38_GIABv4.1.TRUTH.TEST.vcf.gz.tbi

## Pull the docker image.
sudo docker pull kishwars/pepper_deepvariant_cpu:latest

# Run PEPPER-DeepVariant High-Accuracy mode on CPU
sudo docker run --ipc=host \
-v "${INPUT_DIR}":"${INPUT_DIR}" \
-v "${OUTPUT_DIR}":"${OUTPUT_DIR}" \
kishwars/pepper_deepvariant_cpu:latest \
/opt/run_pepper_deepvariant.sh \
-f "${INPUT_DIR}/${REF}" \
-b "${INPUT_DIR}/${BAM}" \
-o "${OUTPUT_DIR}" \
-t ${THREADS} \
-r chr20:1000000-1020000 \
-x 1

# For run-time efficient mode on CPU-only machines:
sudo docker run --ipc=host \
-v "${INPUT_DIR}":"${INPUT_DIR}" \
-v "${OUTPUT_DIR}":"${OUTPUT_DIR}" \
kishwars/pepper_deepvariant_cpu:latest \
/opt/run_pepper_deepvariant.sh \
-f "${INPUT_DIR}/${REF}" \
-b "${INPUT_DIR}/${BAM}" \
-o "${OUTPUT_DIR}" \
-t ${THREADS} \
-r chr20:1000000-1020000

# On GPU machines with high-accuracy (please install nvidia-docker to use this)

## Pull the docker image.
sudo docker pull kishwars/pepper_deepvariant_cpu:latest

# Run PEPPER-DeepVariant High-Accuracy mode on GPU
sudo docker run --ipc=host \
--gpus all \
-v "${INPUT_DIR}":"${INPUT_DIR}" \
-v "${OUTPUT_DIR}":"${OUTPUT_DIR}" \
kishwars/pepper_deepvariant_gpu:latest \
/opt/run_pepper_deepvariant.sh \
-f "${INPUT_DIR}/${REF}" \
-b "${INPUT_DIR}/${BAM}" \
-o "${OUTPUT_DIR}" \
-t ${THREADS} \
-x 1 \
-r chr20:1000000-1020000

Evaluate the variants:

You can use hap.py to evaluate the variants.

# Pull the docker image
sudo docker pull pkrusche/hap.py:latest

# Run hap.py
sudo docker run -it \
-v "${INPUT_DIR}":"${INPUT_DIR}" \
-v "${OUTPUT_DIR}":"${OUTPUT_DIR}" \
pkrusche/hap.py:latest \
/opt/hap.py/bin/hap.py \
${INPUT_DIR}/${TRUTH_VCF} \
${OUTPUT_DIR}/${OUTPUT_VCF} \
-f "${INPUT_DIR}/${TRUTH_BED}" \
-r "${INPUT_DIR}/${REF}" \
-o "${OUTPUT_DIR}/happy.output" \
--pass-only \
-l chr20 \
--engine=vcfeval \
--threads="${THREADS}"

Expected output:

Type	Filter	TRUTH.TOTAL	TRUTH.TP	METRIC.Recall	METRIC.Precision	METRIC.F1_Score
INDEL	ALL	3	2	0.666667	1.0	0.8
SNP	ALL	45	45	1.0	1.0	1.0

Authors:

This pipeline is a collaboration between UCSC genomics institute and the genomics team at Google health.

UCSC Genomics Institute:

Kishwar Shafin
Trevor Pesout
Miten Jain
Benedict Paten

Genomics team at Google Health:

Pi-Chuan Chang
Alexey Kolesnikov
Maria Nattestad
Gunjan Baid
Sidharth Goel
Howard Yang
Andrew Carroll

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PEPPER_variant_calling.md

PEPPER_variant_calling.md

Oxford Nanopore variant calling workflow (Limited support release)

Table of contents:

PrecisionFDA truth challenge v2 results

Difficult-to-Map Regions

All benchmarking regions

Limited support statement

Requirements

Sequencing data requirements

Disk-space requirements

HG002 chr20 case-study

Command-line instructions

Step 1: Install docker

Step 2: Run PEPPER-DeepVariant

Evaluation using hap.py (Optional)

Run-time and performance

Whole-genome evaluations

How to run

Running on a CPU-only machine (Whole genome run)

Running on a GPU machine (Whole genome run)

Quickstart

Step 1: Install docker

Step 2: Run PEPPER-DeepVariant

Evaluate the variants:

Authors:

UCSC Genomics Institute:

Genomics team at Google Health:

Files

PEPPER_variant_calling.md

Latest commit

History

PEPPER_variant_calling.md

File metadata and controls

Oxford Nanopore variant calling workflow (Limited support release)

Table of contents:

PrecisionFDA truth challenge v2 results

Difficult-to-Map Regions

All benchmarking regions

Limited support statement

Requirements

Sequencing data requirements

Disk-space requirements

HG002 chr20 case-study

Command-line instructions

Step 1: Install docker

Step 2: Run PEPPER-DeepVariant

Evaluation using hap.py (Optional)

Run-time and performance

Whole-genome evaluations

How to run

Running on a CPU-only machine (Whole genome run)

Running on a GPU machine (Whole genome run)

Quickstart

Step 1: Install docker

Step 2: Run PEPPER-DeepVariant

Evaluate the variants:

Authors:

UCSC Genomics Institute:

Genomics team at Google Health: