The Ohio State University Corporate Engagement Office

Back to All Technologies

HyperXtract: Rapid, Memory-Efficient Data Extraction for Nanopore Data

Research & Development Tools
Software
Screening Assays
Other
Algorithms
Bioinformatics
Data Analysis
Other
College
College of Arts & Sciences
Researchers
Bandara, Nuwan
Licensing Manager
Panic, Ana
(614) 292-5245
panic.2@osu.edu

T2025-093

Accelerate discovery and streamline workflows with a next-generation platform for high-throughput nanopore data extraction.

The Need

Nanopore sensing is revolutionizing genomics, proteomics, and diagnostics, but the explosive growth in data volume has outpaced the capabilities of current analysis tools. Researchers often face bottlenecks due to slow processing speeds, memory limitations, and the need for specialized hardware. This new technology directly addresses these challenges, enabling efficient, high-speed analysis of massive nanopore datasets.

.

The Technology

HyperXtract is a powerful MATLAB-based software platform that harnesses parallel computing and advanced memory management to deliver rapid, scalable, and reliable data extraction. By processing files in segments rather than loading them entirely into memory, HyperXtract empowers researchers to analyze files larger than system RAM. Further salient features include batch processing, rich auxiliary post-extraction analysis, and dramatically reduced data size for easier sharing and storage. This innovative software unlocks new potential for academic, clinical, and industrial applications where speed and accuracy are paramount.

.

Commercial Applications

Adopt HyperXtract to overcome today’s data bottlenecks and future-proof your nanopore workflows. Whether in high-throughput screening, real-time diagnostics, or advanced research, this platform delivers the speed, scalability, and simplicity needed to stay ahead in a data-driven world. Empower your team to extract more insights, faster, without the need for high-end computing resources.

.

Benefits/Advantages

Up to 50× faster than leading alternatives for large file analysis, thanks to;

  • Segmented data loading and processing, eliminating memory bottlenecks.
  • Memory mapping.
  • Batch processing of multiple files.
  • Optimized code to run efficiently on standard PCs with an intuitive MATLAB interface