Clara for Genomics
Power unprecedented speed and accuracy in precision genomics with the NVIDIA Clara™ for Genomics software suite.
Fast, Accurate, and Scalable Solutions for Bioinformatics Workflows
NVIDIA Parabricks
Accelerate Genomics Analysis
NVIDIA® Parabricks® is a scalable genomics software suite for secondary analysis that provides GPU-accelerated versions of trusted, open-source tools.
Compatible with leading sequencing instruments, researchers, bioinformaticians, and data scientists can use Parabricks across diverse bioinformatics workflows.
Parabricks increases speed with faster runtimes and reduces cost—while enhancing accuracy and ensuring transparency. As a result, researchers can uncover biological insights and discoveries faster.
NVIDIA AI Blueprints
Easily Deploy Genomics and Single-Cell Solutions
NVIDIA AI Blueprints are lightweight, easily deployable blueprints for genomics and single-cell analysis.
Built for bioinformaticians and genomics platform providers, AI Blueprints allow users to easily deploy and test NVIDIA Parabricks and NVIDIA RAPIDS™—including RAPIDS-single-cell developed by scverse—without requiring GPUs or cloud provisions.
By easily evaluating and expanding the accessibility of accelerated genomics libraries, scientists and researchers can further genomic understanding by getting up and running faster.
NVIDIA NIM Microservices
Optimize Alignment and Variant Calling
NVIDIA NIM™ microservices are production-ready, optimized, and composable microservices with FQ2BAM for alignment and DeepVariant for universal variant calling.
Data scientists and developers can gain access to the latest advances in generative AI with industry-standard APIs that make it easy to create and deploy cloud-native applications.
Reduce time-consuming analysis. For alignment, production-ready, industry-standard alignment occurs in minutes instead of hours. Plus, optimize performance of variant calling from different sequencing platforms.
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FAQ
NVIDIA Parabricks is a GPU-accelerated software suite for secondary genomic analysis. Parabricks is compatible with all leading sequencing instruments and supports diverse bioinformatics workflows.
Parabricks increases speed, reduces cost, and boosts accuracy for secondary genomic analysis.
- Boost accuracy: Deploy high-accuracy deep learning for all major sequencers.
- Increase speed: Experience 108X faster analysis of whole-genome sequencing (WGS) compared to CPU-only solutions.
- Reduce cost: Lower compute cost by up to 50 percent for WGS compared to CPU-only solutions.
Yes, Parabricks matches the results of open-source tools, so you can switch out open-source tools and get the exact same results with Parabricks. The latest benchmarks from the 4.3.1 release can be found in the table below.
Performance time in minutes.
30x whole genome sequenced for FQ2BAM, BWA-Meth, DeepVariant, and Haplotype Caller with Illumina data.
50x tumor-normal whole genome sequenced for Mutect2 with Illumina data.
Parabricks is available as a public container on NVIDIA NGC™ for use on premises or any cloud service platform or provider.
For customers who need support for Parabricks in clinical workflows, large sequencing projects, high-throughput platforms, or other workflows that require immediate assistance, NVIDIA offers NVIDIA AI Enterprise.
Parabricks can be run on public cloud providers, platforms and partners, and on premises.
- Public cloud providers: AWS, Google Cloud, Oracle Cloud, Microsoft Azure, Naver Cloud Platform, Baidu AI Cloud, Alibaba Cloud
- Platforms and partners: DNAnexus, AWS, Terra, Velsera, FormBio, Lifebit
- On premises: NVIDIA DGX systems, NVIDIA-Certified servers, Dell Technologies, Hewlett Packard Enterprise, Lenovo, Cisco, Atos, Supermicro
Hardware Requirements
- Any NVIDIA GPU that supports CUDA architecture 70, 75, 80, 86, 89, or 90 and has at least 16 gigabytes (GB) of GPU RAM. NVIDIA Parabricks has been tested on the following NVIDIA Tensor Core GPUs:
- V100
- T4
- A10, A30, A40, A100, A6000
- L4, L40
- H100, H200
- Grace Hopper™ Superchip
- The fq2bam tool requires at least 24GB of GPU memory by default; the low-memory option will reduce this to 16GB of GPU memory at the cost of slower processing. All other tools require at least 16GB of GPU memory per GPU.
- System Requirements:
- A two-GPU system should have at least 100GB CPU RAM and at least 24 CPU threads.
- A four-GPU system should have at least 196GB CPU RAM and at least 32 CPU threads.
- An eight-GPU system should have at least 392GB CPU RAM and at least 48 CPU threads.
Software Requirements
- An NVIDIA driver with version 525.60.13 or greater
- Any Linux operating system that supports nvidia-docker2 Docker version 20.10 (or higher)
- Fastest speed: NVIDIA H100 Tensor Core GPUs
- Lowest cost per sample: NVIDIA L4 Tensor Core GPUs
- Brand new support: NVIDIA Grace Hopper Superchip
Parabricks is supported on most NVIDIA enterprise GPUs, including A100, A6000, A40, A30, A10, L40, T4, and V100 Tensor Core GPUs.
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