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Abstract
Slow speed of the Next-Generation sequencing data analysis, compared to the latest high throughput sequencers such as HiSeq X system, using the current industry standard genome analysis pipeline, has been the major factor of data backlog which limits the real-time use of genomic data for precision medicine. This study demonstrates the DRAGEN Bio-IT Processor as a potential candidate to remove the “Big Data Bottleneck”. DRAGENTM accomplished the variant calling, for ~40× coverage WGS data in as low as ~30 minutes using a single command, achieving the over 50-fold data analysis speed while maintaining the similar or better variant calling accuracy than the standard GATK Best Practices workflow. This systematic comparison provides the faster and efficient NGS data analysis alternative to NGS-based healthcare industries and research institutes to meet the requirement for precision medicine based healthcare.
Highlights
With the emergence of the 2nd generation high throughput Generation Sequencing (NGS) platforms as well as accurate and consistent identification of the genomic variants, the use of the personal genome sequencing information for the diagnostic and prognostic purpose has become the reality [1] [2]
Slow speed of the Next-Generation sequencing data analysis, compared to the latest high throughput sequencers such as HiSeq X system, using the current industry standard genome analysis pipeline, has been the major factor of data backlog which limits the real-time use of genomic data for precision medicine
The variant calling efficiencies of the two pipelines were evaluated by comparing variants with the GIABv2.19 high confidence call-set [12] [13]. These studies demonstrate that the employment of the DRAGEN Bio-IT processor decreased the Whole Genome Sequencing (WGS) Next Generation Sequencing (NGS)-data analysis time to just ~40 minute while achieving the equivalent or better genotype variant calling accuracy than the standard Genome Analysis Toolkit (GATK) Best Practices workflow
Summary
With the emergence of the 2nd generation high throughput Generation Sequencing (NGS) platforms as well as accurate and consistent identification of the genomic variants, the use of the personal genome sequencing information for the diagnostic and prognostic purpose has become the reality [1] [2]. The most commonly used Genome Analysis Toolkit (GATK) best practice pipelines requires several hours to several days to analyze one human whole genome sequencing data, depending on the available processors. Several cloud-based solutions, such as GenomePilot by Appistry [9], etc., to accelerate NGS-data analysis platform to speed-up the analysis has been introduced This conventional cluster approach requires expensive computer system, maintenance and monitoring. The variant calling efficiencies of the two pipelines were evaluated by comparing variants with the GIABv2.19 high confidence (truth) call-set [12] [13] These studies demonstrate that the employment of the DRAGEN Bio-IT processor decreased the WGS NGS-data analysis time to just ~40 minute while achieving the equivalent or better genotype variant calling accuracy than the standard GATK Best Practices workflow
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