germline (GATK Germline Pipeline)

What is GATK?

GATK, the Genome Analysis Toolkit, is an industry standard software package developed by the Broad Institute of MIT and Harvard and designed to be used for a wide range of genomic analyses, including variant discovery, genotyping, and more. GATK is one of the most popular tools used in bioinformatics for analyzing next-generation sequencing datasets and is an industry standard for calling single nucleotide variants (SNVs) and insertions/deletions (InDels) from sequencing data in germline samples.

Why GATK?

GATK offers robust, accurate analysis of sequencing data and is frequently updated to include the latest best practices for variant discovery. With high reliability and the ability to be used for a number of use cases, GATK is a gold standard tool for any researcher working with next-generation sequencing data.

How should I use GATK?

The GATK germline workflow for variant calling can be deployed within NVIDIA’s Clara Parabricks software suite, which is designed for accelerated secondary analysis in genomics, bringing industry standard tools and workflows from CPU to GPU and delivering the same results at up to 60x faster runtimes. A 30x whole genome can be analyzed in under 25 minutes on an NVIDIA DGX system, compared to over 30 hours on a CPU instance (m5.24xlarge, 96 x vCPU), and exomes can be analyzed in just 4 minutes. This means Parabricks, running on one NVIDIA DGX A100, can analyze up to 25,000 whole genomes per year. The NVIDIA team collaborated with the GATK team at the Broad Institute to evaluate the accuracy of germline workflows. Through this rigorous process, they verified that the Parabricks workflows produce results that are functionally equivalent to the CPU-native GATK versions.

As a specific example, benchmarking on publicly available Genome in a Bottle (GIAB) samples with the fq2bam and germline caller workflows from the Parabricks suite produced variant calling results that were >0.9999 equivalent in both precision and recall to those produced by the BWA, MarkDuplicates, BQSR, and HaplotypeCaller commands in the GATK’s Whole Genome Germline Single Sample variant calling workflow.

Given one or more pairs of FASTQ files, you can run the germline variant tool to generate BAM, variants, duplicate metrics and recal.

The germline pipeline shown below resembles the GATK4 best practices pipeline. The inputs are BWA-indexed reference files, pair-ended FASTQ files, and knownSites for BQSR calculation. The outputs of this pipeline are as follows:

Aligned, co-ordinate sorted, duplicated marked BAM
BQSR report
Variants in vcf/g.vcf/g.vcf.gz format

Quick Start

Running the germline pipeline:

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            # This command assumes all the inputs are in INPUT_DIR and all the outputs go to OUTPUT_DIR.
docker run --rm --gpus all --volume INPUT_DIR:/workdir --volume OUTPUT_DIR:/outputdir \
    --workdir /workdir \
    nvcr.io/nvidia/clara/clara-parabricks:4.1.1-1 \
    pbrun germline \
    --ref /workdir/${REFERENCE_FILE} \
    --in-fq /workdir/${INPUT_FASTQ_1} /workdir/${INPUT_FASTQ_2} \
    --knownSites /workdir/${KNOWN_SITES_FILE} \
    --out-bam /outputdir/${OUTPUT_BAM} \
    --out-variants /outputdir/${OUTPUT_VCF} \
    --out-recal-file /outputdir/${OUT_RECAL_FILE}

Specifying Haplotype Caller options

Several original HaplotypeCaller options are supported by Clara Parabricks. To specify the inclusion or exclusion of several haplotype caller annotations, use the --haplotypecaller-options option:

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            # This command assumes all the inputs are in INPUT_DIR and all the outputs go to OUTPUT_DIR.
docker run --rm --gpus all --volume INPUT_DIR:/workdir --volume OUTPUT_DIR:/outputdir \
    --workdir /workdir \
    nvcr.io/nvidia/clara/clara-parabricks:4.1.1-1 \
    pbrun haplotypecaller \
     ...
     --haplotypecaller-options '-min-pruning 4 -A AS_BaseQualityRankSumTest -A TandemRepeat'
     ...

Annotations may be excluded in the same manner using the -AX option. There should be a space between the -A/-AX flag and its value.

The following are supported options and their allowed values:

-A
- AS_BaseQualityRankSumTest
- AS_FisherStrand
- AS_InbreedingCoeff
- AS_MappingQualityRankSumTest
- AS_QualByDepth
- AS_RMSMappingQuality
- AS_ReadPosRankSumTest
- AS_StrandOddsRatio
- BaseQualityRankSumTest
- ChromosomeCounts
- ClippingRankSumTest
- Coverage
- DepthPerAlleleBySample
- DepthPerSampleHC
- ExcessHet
- FisherStrand
- InbreedingCoeff
- MappingQualityRankSumTest
- QualByDepth
- RMSMappingQuality
- ReadPosRankSumTest
- ReferenceBases
- StrandBiasBySample
- StrandOddsRatio
- TandemRepeat
-AX
- (same as for the -A option)
--output-mode
- EMIT_VARIANTS_ONLY
- EMIT_ALL_CONFIDENT_SITES
- EMIT_ALL_ACTIVE_SITES
-max-reads-per-alignment-start
- a positive integer
-min-dangling-branch-length
- a positive integer
-min-pruning
- a positive integer
-pcr-indel-model
- NONE
- HOSTILE
- AGGRESSIVE
- CONSERVATIVE
-standard-min-confidence-threshold-for-calling
- a positive integer

Compatible CPU-based BWA-MEM, GATK4 Commands

The commands below are the bwa-0.7.12 and GATK4 counterpart of the Parabricks command above. The output from these commands will be identical to the output from the above command. See the Output Comparison page for comparing the results.

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            # Run bwa-mem and pipe output to create sorted BAM
$ bwa mem \
    -t 32 \
    -K 10000000 \
    -R '@RG\tID:sample_rg1\tLB:lib1\tPL:bar\tSM:sample\tPU:sample_rg1' \
    <INPUT_DIR>/${REFERENCE_FILE} <INPUT_DIR>/${INPUT_FASTQ_1} <INPUT_DIR>/${INPUT_FASTQ_2} | \
  gatk SortSam \
      --java-options -Xmx30g \
      --MAX_RECORDS_IN_RAM 5000000 \
      -I /dev/stdin \
      -O cpu.bam \
      --SORT_ORDER coordinate

# Mark Duplicates
$ gatk MarkDuplicates \
    --java-options -Xmx30g \
    -I cpu.bam \
    -O mark_dups_cpu.bam \
    -M metrics.txt

# Generate BQSR Report
$ gatk BaseRecalibrator \
    --java-options -Xmx30g \
    --input mark_dups_cpu.bam \
    --output <OUTPUT_DIR>/${OUT_RECAL_FILE} \
    --known-sites <INPUT_DIR>/${KNOWN_SITES_FILE} \
    --reference <INPUT_DIR>/${REFERENCE_FILE}

# Run ApplyBQSR Step
$ gatk ApplyBQSR \
    --java-options -Xmx30g \
    -R <INPUT_DIR>/${REFERENCE_FILE} \
    -I mark_dups_cpu.bam \
    --bqsr-recal-file <OUTPUT_DIR>/${OUT_RECAL_FILE} \
    -O cpu_nodups_BQSR.bam

#Run Haplotype Caller
$ gatk HaplotypeCaller \
    --java-options -Xmx30g \
    --input cpu_nodups_BQSR.bam \
    --output <OUTPUT_DIR>/${OUTPUT_VCF} \
    --reference <INPUT_DIR>/${REFERENCE_FILE} \
    --native-pair-hmm-threads 16

germline Reference

Run Germline pipeline to convert FASTQ to VCF.

Input/Output file options

--ref REF
--in-fq [IN_FQ [IN_FQ ...]]
--in-se-fq [IN_SE_FQ [IN_SE_FQ ...]]
--knownSites KNOWNSITES
--interval-file INTERVAL_FILE
--out-recal-file OUT_RECAL_FILE
--out-bam OUT_BAM
--out-variants OUT_VARIANTS
--out-duplicate-metrics OUT_DUPLICATE_METRICS

Tool Options:

-L INTERVAL, --interval INTERVAL
--bwa-options BWA_OPTIONS
--no-warnings
--gpuwrite
--gpusort
--low-memory
--filter-flag FILTER_FLAG
--skip-multiple-hits
--min-read-length MIN_READ_LENGTH
--align-only
--no-markdups
--fix-mate
--markdups-assume-sortorder-queryname
--markdups-picard-version-2182
--optical-duplicate-pixel-distance OPTICAL_DUPLICATE_PIXEL_DISTANCE
--read-group-sm READ_GROUP_SM
--read-group-lb READ_GROUP_LB
--read-group-pl READ_GROUP_PL
--read-group-id-prefix READ_GROUP_ID_PREFIX
-ip INTERVAL_PADDING, --interval-padding INTERVAL_PADDING
--haplotypecaller-options HAPLOTYPECALLER_OPTIONS
--static-quantized-quals STATIC_QUANTIZED_QUALS
--gvcf
--batch
--disable-read-filter DISABLE_READ_FILTER
--max-alternate-alleles MAX_ALTERNATE_ALLELES
-G ANNOTATION_GROUP, --annotation-group ANNOTATION_GROUP
-GQB GVCF_GQ_BANDS, --gvcf-gq-bands GVCF_GQ_BANDS
--rna
--dont-use-soft-clipped-bases
--read-from-tmp-dir
--run-partition
--no-alt-contigs
--ploidy PLOIDY

Common options:

--logfile LOGFILE
--tmp-dir TMP_DIR
--with-petagene-dir WITH_PETAGENE_DIR
--keep-tmp
--no-seccomp-override
--version

GPU options:

--num-gpus NUM_GPUS

Note

The --in-fq option takes the names of two FASTQ files, optionally followed by a quoted read group. The FASTQ filenames must not start with a hyphen.

Note

In the values provided to --haplotypecaller-options --output-mode requires two leading hyphens, while all other values take a single hyphen.