85% Reduction in Genomics
Data Storage Costs

The Challenge

Dayhoff Technologies was operating a genomics analysis platform that faced two compounding problems: the cost of storing large-scale genomic datasets was growing unsustainably, and the processing pipelines for genomic analysis (FASTQ processing, species identification with Kraken2, and genomic benchmarking) were too slow to scale with their research workloads.

Genomic data is inherently large — a single sequencing run can produce hundreds of gigabytes of raw data. At the scale Dayhoff was operating, cloud storage costs were a significant and accelerating line item. Existing compression approaches weren't domain-optimized for genomic data formats, leaving substantial cost savings on the table.

Our Approach

Green Platform designed and built a secure, high-performance bioinformatics platform addressing both dimensions of the problem simultaneously: storage efficiency and compute performance.

Advanced genomic compression: We implemented domain-specific compression algorithms optimized for genomic data formats — FASTQ, BAM, and related file types. Generic compression tools like gzip are not designed for the redundancy patterns in genomic sequences. Purpose-built compression for this data type achieved dramatically better ratios, directly translating to storage cost reduction.

GPU-accelerated compute: The bioinformatics pipelines — species identification, genomic benchmarking, and sequence alignment — were refactored to leverage GPU acceleration. Workloads that are inherently parallelizable, like sequence comparison operations, see substantial throughput gains on GPU architectures compared to CPU-only computation.

Secure platform architecture: Genomic data is sensitive personal health information under most privacy frameworks. The platform was designed with appropriate access controls, encryption at rest and in transit, and audit logging from the ground up — not retrofitted after initial development.

The Technology Stack

The Outcome

The combination of domain-optimized compression and GPU-accelerated processing delivered an 85% reduction in data storage costs — a transformative infrastructure saving that improved unit economics for the entire platform. Processing throughput improved significantly, enabling Dayhoff to expand their analytical capabilities without proportional infrastructure cost increases.

The platform provides a secure, scalable foundation for Dayhoff's continued growth in the bioinformatics space, with architecture designed to handle increasing data volumes without the cost escalation that plagued the previous system.