DeepVariant is a deep learning-based variant caller that takes aligned reads (in BAM or CRAM format), produces pileup image tensors from them, classifies each tensor using a convolutional neural network, and finally reports the results in a standard VCF or gVCF file.
DeepVariant supports germline variant-calling in diploid organisms.
DeepVariant case-studies for germline variant calling:
- NGS (Illumina or Element) data for either a whole genome or whole exome.
- PacBio HiFi data PacBio case study.
- Oxford Nanopore R10.4.1 Simplex case study, Duplex case study.
- Complete Genomics T7 case study; G400 case study.
- Pangenome-mapping-based case-study: vg case study.
- RNA data for PacBio Iso-Seq/MAS-Seq case study and Illumina RNA-seq Case Study.
- Hybrid PacBio HiFi + Illumina WGS, see the hybrid case study.
Pangenome-aware DeepVariant case-studies:
- Pangenome-aware DeepVariant WGS (Illumina or Element): Mapped with BWA, Mapped with VG.
- Pangenome-aware DeepVariant WES (Illumina or Element): Mapped with BWA.
We have also adapted DeepVariant for somatic calling. See the DeepSomatic repo for details.
Please also note:
- DeepVariant currently supports variant calling on organisms where the ploidy/copy-number is two. This is because the genotypes supported are hom-alt, het, and hom-ref.
- The models included with DeepVariant are only trained on human data. For other organisms, see the blog post on non-human variant-calling for some possible pitfalls and how to handle them.
DeepTrio is a deep learning-based trio variant caller built on top of DeepVariant. DeepTrio extends DeepVariant's functionality, allowing it to utilize the power of neural networks to predict genomic variants in trios or duos. See this page for more details and instructions on how to run DeepTrio.
DeepTrio supports germline variant-calling in diploid organisms for the following types of input data:
- NGS (Illumina) data for either whole genome or whole exome.
- PacBio HiFi data, see the PacBio case study.
Please also note:
- All DeepTrio models were trained on human data.
- It is possible to use DeepTrio with only 2 samples (child, and one parent).
- External tool GLnexus is used to merge output VCFs.
We recommend using our Docker solution. The command will look like this:
BIN_VERSION="1.8.0"
docker run \
-v "YOUR_INPUT_DIR":"/input" \
-v "YOUR_OUTPUT_DIR:/output" \
google/deepvariant:"${BIN_VERSION}" \
/opt/deepvariant/bin/run_deepvariant \
--model_type=WGS \ **Replace this string with exactly one of the following [WGS,WES,PACBIO,ONT_R104,HYBRID_PACBIO_ILLUMINA]**
--ref=/input/YOUR_REF \
--reads=/input/YOUR_BAM \
--output_vcf=/output/YOUR_OUTPUT_VCF \
--output_gvcf=/output/YOUR_OUTPUT_GVCF \
--num_shards=$(nproc) \ **This will use all your cores to run make_examples. Feel free to change.**
--vcf_stats_report=true \ **Optional. Creates VCF statistics report in html file. Default is false.
--disable_small_model=true \ **Optional. Disables the small model from make_examples stage. Default is false.
--logging_dir=/output/logs \ **Optional. This saves the log output for each stage separately.
--haploid_contigs="chrX,chrY" \ **Optional. Heterozygous variants in these contigs will be re-genotyped as the most likely of reference or homozygous alternates. For a sample with karyotype XY, it should be set to "chrX,chrY" for GRCh38 and "X,Y" for GRCh37. For a sample with karyotype XX, this should not be used.
--par_regions_bed="/input/GRCh3X_par.bed" \ **Optional. If --haploid_contigs is set, then this can be used to provide PAR regions to be excluded from genotype adjustment. Download links to this files are available in this page.
--dry_run=false **Default is false. If set to true, commands will be printed out but not executed.
For details on X,Y support, please see DeepVariant haploid support and the case study in DeepVariant X, Y case study. You can download the PAR bed files from here: GRCh38_par.bed, GRCh37_par.bed.
To see all flags you can use, run: docker run google/deepvariant:"${BIN_VERSION}"
If you're using GPUs, or want to use Singularity instead, see Quick Start for more details.
If you are running on a machine with a GPU, an experimental mode is available
that enables running the make_examples stage on the CPU while the
call_variants stage runs on the GPU simultaneously.
For more details, refer to the Fast Pipeline case study.
For more information, also see:
- Full documentation list
- Detailed usage guide with more information on the input and output file formats and how to work with them.
- Best practices for multi-sample variant calling with DeepVariant
- (Advanced) Training tutorial
- DeepVariant's Frequently Asked Questions, FAQ
If you're using DeepVariant in your work, please cite:
A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology 36, 983–987 (2018).
Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah T. Afshar, Sam S. Gross, Lizzie Dorfman, Cory Y. McLean, and Mark A. DePristo.
doi: https://doi.org/10.1038/nbt.4235
Additionally, if you are generating multi-sample calls using our DeepVariant and GLnexus Best Practices, please cite:
Accurate, scalable cohort variant calls using DeepVariant and GLnexus.
Bioinformatics (2021).
Taedong Yun, Helen Li, Pi-Chuan Chang, Michael F. Lin, Andrew Carroll, and Cory
Y. McLean.
doi: https://doi.org/10.1093/bioinformatics/btaa1081
- High accuracy - DeepVariant won 2020 PrecisionFDA Truth Challenge V2 for All Benchmark Regions for ONT, PacBio, and Multiple Technologies categories, and 2016 PrecisionFDA Truth Challenge for best SNP Performance. DeepVariant maintains high accuracy across data from different sequencing technologies, prep methods, and species. For lower coverage, using DeepVariant makes an especially great difference. See metrics for the latest accuracy numbers on each of the sequencing types.
- Flexibility - Out-of-the-box use for PCR-positive samples and low quality sequencing runs, and easy adjustments for different sequencing technologies and non-human species.
- Ease of use - No filtering is needed beyond setting your preferred minimum quality threshold.
- Cost effectiveness - With a single non-preemptible n1-standard-16 machine on Google Cloud, it costs ~$11.8 to call a 30x whole genome and ~$0.89 to call an exome. With preemptible pricing, the cost is $2.84 for a 30x whole genome and $0.21 for whole exome (not considering preemption).
- Speed - See metrics for the runtime of all supported datatypes on a 96-core CPU-only machine. Multiple options for acceleration exist.
- Usage options - DeepVariant can be run via Docker or binaries, using both on-premise hardware or in the cloud, with support for hardware accelerators like GPUs and TPUs.
(1): Time estimates do not include mapping.
For more information on the pileup images and how to read them, please see the "Looking through DeepVariant's Eyes" blog post.
DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework. Nucleus was built with DeepVariant in mind and open-sourced separately so it can be used by anyone in the genomics research community for other projects. See this blog post on Using Nucleus and TensorFlow for DNA Sequencing Error Correction.
- Unix-like operating system (cannot run on Windows)
- Python 3.10
Below are the official solutions provided by the Genomics team in Google Health.
| Name | Description |
|---|---|
| Docker | This is the recommended method. |
| Build from source | DeepVariant comes with scripts to build it on Ubuntu 20.04. To build and run on other Unix-based systems, you will need to modify these scripts. |
| Prebuilt Binaries | Available at gs://deepvariant/. These are compiled to use SSE4 and AVX instructions, so you will need a CPU (such as Intel Sandy Bridge) that supports them. You can check the /proc/cpuinfo file on your computer, which lists these features under "flags". |
Please open a pull request if you wish to contribute to DeepVariant. Note, we have not set up the infrastructure to merge pull requests externally. If you agree, we will test and submit the changes internally and mention your contributions in our release notes. We apologize for any inconvenience.
If you have any difficulty using DeepVariant, feel free to open an issue. If you have general questions not specific to DeepVariant, we recommend that you post on a community discussion forum such as BioStars.
DeepVariant happily makes use of many open source packages. We would like to specifically call out a few key ones:
- Boost Graph Library
- abseil-cpp and abseil-py
- pybind11
- GNU Parallel
- htslib & samtools
- Nucleus
- numpy
- SSW Library
- TensorFlow
We thank all of the developers and contributors to these packages for their work.
This is not an official Google product.
NOTE: the content of this research code repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.