GATK Best Practices Workflow on DRAGEN
Accelerated and Cost-Effective
The GATK Best Practices Workflow for Germline SNPs and Indels in Whole Genomes and Exomes is available on the DRAGEN Platform for customers that have a valid GATK License from the Broad Institute. Harnessing the tremendous processing power of the DRAGEN Bio-IT Platform, the GATK Best Practices Workflow on DRAGEN reduces the time required to analyze a whole genome from FASTQ to VCF at 30x coverage to ~22 minutes. This time saving translates directly to significant cost savings both onsite and in the cloud. DRAGEN supports versions 3.1 and 3.6 of the GATK-HC VariantCaller.
GATK Best Practices on DRAGEN
GATK Best Practices describes the key principles of the processing and analysis steps required to go from raw reads coming off a sequencing instrument through to an appropriately filtered variant callset that can be used in downstream analyses. GATK Best Practices breaks the workflow into two required analysis phases and one optional phase that describes ways to handle or fine-tune the output VCF file.
Comprehensive Platform for Genomic Data Analysis and Storage
The GATK Best Practices Workflow on DRAGEN plugs into the larger DRAGEN eco-system, a comprehensive platform for analyzing, reanalyzing, storing and archiving genomic data at the lowest cost and highest fidelity. The DRAGEN Platform is based on the highly reconfigurable DRAGEN Bio-IT Processor which uses a field-programmable-gate-array (FPGA) to provide hardware-accelerated implementations of genome pipeline algorithms, such as BCL conversion, compression, mapping, alignment, sorting, duplicate marking and haplotype variant calling. The highly flexible DRAGEN Platform allows users to effortlessly set-up and manage highly complex workflows and can be utilized onsite, in the cloud or as a seamless hybrid cloud that includes a fully functional and easy-to-use Web Portal and Workflow Management System. The Hybrid Cloud configuration provides users with the flexibility to scale up to the cloud during times of high capacity and return to onsite analysis when demand is reduced.