Applied genomics for identification of virulent biothreats and for disease outbreak surveillance

Accelerating genomic workflows using NVIDIA Parabricks

BACKGROUND

As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster. We benchmarked six variant calling pipelines, including two germline callers (HaplotypeCaller and DeepVariant) and four somatic callers (Mutect2, Muse, LoFreq, SomaticSniper).

RESULTS

We achieved up to 65 × acceleration with germline variant callers, bringing HaplotypeCaller runtimes down from 36 h to 33 min on AWS, 35 min on GCP, and 24 min on the NVIDIA DGX. Somatic callers exhibited more variation between the number of GPUs and computing platforms. On cloud platforms, GPU-accelerated germline callers resulted in cost savings compared with CPU runs, whereas some somatic callers were more expensive than CPU runs because their GPU acceleration was not sufficient to overcome the increased GPU cost.

CONCLUSIONS

Germline variant callers scaled well with the number of GPUs across platforms, whereas somatic variant callers exhibited more variation in the number of GPUs with the fastest runtimes, suggesting that, at least with the version of Parabricks used here, these workflows are less GPU optimized and require benchmarking on the platform of choice before being deployed at production scales. Our study demonstrates that GPUs can be used to greatly accelerate genomic workflows, thus bringing closer to grasp urgent societal advances in the areas of biosurveillance and personalized medicine.

RNA-seq analysis of phagocytic cells from murine epididymal white adipose tissue shows immunosenescence and age-related phosphorus metabolism

The underlying state of alterations in adipose tissue is hypothesized to be as a result of age-related changes. Young and aged mice have been documented to show distinct gene expression and distinct macrophage-specific adipose tissue regulation. However, more biological understanding is required to know the processes associated with these conditions in relation to the aging process. Transcriptional profiling with RNA-seq analysis was used to determine differentially expressed genes in young (2 months old) and aged (20 months old) mice macrophage-enriched phagocytic stromal vascular fractions of pooled epididymal white adipose tissue using data obtained from gene expression omnibus. Results showed distinct differentially expressed genes in young and aged mice with a p value cutoff of 0.05 and dissimilarities in the young and aged epididymal white adipose tissue phagocytic cells. Functional enrichment showed activation of cytokine-cytokine receptor interaction pathways, phosphorus metabolic processes and inflammatory pathways such as IL-17, TNF, NF-kappa B, and TGF-β, while AMPK, PPAR and oxidative phosphorylation were suppressed. The analysis showed that aging is linked with phagocytic cell decline, accumulated cellular damages, inflammation, immunosenescence and increased phosphorus metabolism. Interventions that reduce phosphate-containing compound could improve phosphorus metabolism in old age to prolong lifespan and better health.