deepsomatic - NVIDIA Docs

GPU-accelerated DeepSomatic.

What is DeepSomatic?

DeepSomatic builds on the deep learning-based variant caller DeepVariant. It processes aligned reads from tumor and normal samples (in BAM or CRAM format), generates pileup image tensors, classifies these tensors using a convolutional neural network, and outputs somatic variants in standard VCF or gVCF files.

DeepSomatic is designed for somatic variant calling using tumor-normal sequencing data.

Parabricks has enhanced Google DeepSomatic to leverage GPUs extensively. The Parabricks version of DeepSomatic operates similarly to other common command line tools: it accepts two BAM files and a reference file as inputs and generates variants in a VCF file as output.

See the deepsomatic Reference section for a detailed listing of all available options.

Quick Start

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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:4.3.2-1 (OR nvcr.io/nvidia/clara/clara-parabricks:4.3.2-1.grace) \
    pbrun deepsomatic \
    --ref /workdir/${REFERENCE_FILE} \
    --in-tumor-bam /workdir/${INPUT_TUMOR_BAM} \
    --in-normal-bam /workdir/${INPUT_NORMAL_BAM} \
    --out-variants /outputdir/${OUTPUT_VCF}

Compatible Google DeepVariant Commands

The commands below are the Google 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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            docker run \
--interactve \
--tty \
--rm \
--volume ${INPUT_DIR}:${INPUT_DIR} \
--volume ${OUTPUT_DIR}:${OUTPUT_DIR} \
--workdir /workdir google/deepvariant:1.6.1 \
run_deepsomatic \
--ref ${REFERENCE_FILE} \
--reads_tumor ${TUMOR_BAM} \
--reads_normal ${NORMAL_BAM}  \
--customized_model ${DEEPSOMATIC_WGS_MODEL_FILE} \
--output_vcf ${OUTPUT_VCF} \
--make_examples_extra_args "ws_use_window_selector_model=true" \
--num_shards=$(nproc)

Source of Mismatches

While Parabricks DeepSomatic does not lose any accuracy in functionality when compared with Google DeepSomatic there is one reason that can result in different output files.

CNN Inference

Google DeepSomatic uses a CNN (convolutional neural network) to predict the possibilities of each variant candidate. The model is trained, and does inference through, Keras. In Parabricks DeepSomatic we convert this Keras model to an engine file with TensorRT to perform accelerated deep learning inferencing on Nvidia GPUs. Because of the optimizations from TensorRT there is a small difference in the final possibility scores after inferencing (10^-5), which could cause a few different variants in the final VCF output. Based on current observations the mismatches only happen to RefCalls with a quality score of zero.

deepsomatic Reference

Run DeepSomatic to convert BAM/CRAM to VCF.

Input/Output file options

--ref REF

Path to the reference file. (default: None)

Option is required.

--in-tumor-bam IN_TUMOR_BAM

Path to the input tumor BAM/CRAM file for somatic variant calling. (default: None)

Option is required.

--in-normal-bam IN_NORMAL_BAM

Path to the input normal BAM/CRAM file for somatic variant calling. (default: None)

Option is required.

--interval-file INTERVAL_FILE

Path to a BED file (.bed) for selective access. This option can be used multiple times. (default: None)

--out-variants OUT_VARIANTS

Path of the vcf/g.vcf/g.vcf.gz file after variant calling. (default: None)

Option is required.

--pb-model-file PB_MODEL_FILE

Path to a non-default parabricks model file for deepsomatic. (default: None)

Tool Options:

--disable-use-window-selector-model: Change the window selector model from Allele Count Linear to Variant Reads. This option will increase the accuracy and runtime. (default: None)
--no-channel-insert-size: If True, don't add insert_size channel into the pileup image. (default: False)
-L INTERVAL, --interval INTERVAL: Interval within which to call the variants from the BAM/CRAM file. Overlapping intervals will be combined. Interval files should be passed using the --interval-file option. This option can be used multiple times (e.g. "-L chr1 -L chr2:10000 -L chr3:20000+ -L chr4:10000-20000"). (default: None)

Performance Options:

--num-cpu-threads-per-stream NUM_CPU_THREADS_PER_STREAM: Number of CPU threads to use per stream. (default: 6)
--num-streams-per-gpu NUM_STREAMS_PER_GPU: Number of streams to use per GPU. (default: 2)
--run-partition: Divide the whole genome into multiple partitions and run multiple processes at the same time, each on one partition. (default: None)
--gpu-num-per-partition GPU_NUM_PER_PARTITION: Number of GPUs to use per partition. (default: None)

Common options:

--logfile LOGFILE: Path to the log file. If not specified, messages will only be written to the standard error output. (default: None)
--tmp-dir TMP_DIR: Full path to the directory where temporary files will be stored.
--with-petagene-dir WITH_PETAGENE_DIR: Full path to the PetaGene installation directory. By default, this should have been installed at /opt/petagene. Use of this option also requires that the PetaLink library has been preloaded by setting the LD_PRELOAD environment variable. Optionally set the PETASUITE_REFPATH and PGCLOUD_CREDPATH environment variables that are used for data and credentials (default: None)
--keep-tmp: Do not delete the directory storing temporary files after completion.
--no-seccomp-override: Do not override seccomp options for docker (default: None).
--version: View compatible software versions.

GPU options:

--num-gpus NUM_GPUS: Number of GPUs to use for a run. GPUs 0..(NUM_GPUS-1) will be used.