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FrameD: framework for DNA-based data storage design, verification, and validation.

Kevin D Volkel1, Kevin N Lin2, Paul W Hook3

  • 1Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, 27606, United States.

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Summary
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FrameD is a new simulation infrastructure for DNA data storage systems. It allows for modular design and comparison of different systems, accelerating the development of next-generation archival storage.

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Data Storage

Background:

  • DNA-based data storage offers high theoretical information density for archival purposes.
  • The lack of a common simulation infrastructure hinders the comparison of diverse DNA storage system designs and error models.
  • This necessitates a standardized framework for evaluating and advancing DNA storage technologies.

Purpose of the Study:

  • To introduce FrameD, a modular simulation infrastructure for DNA storage systems.
  • To provide a framework for expressing and comparing various DNA storage designs and error models.
  • To facilitate the development and optimization of next-generation DNA data storage solutions.

Main Methods:

  • Developed FrameD, a modular simulation infrastructure for DNA storage systems.
  • Leveraged modularity to allow reuse of common components across different designs.
  • Implemented transparent parallelism for handling large datasets and fault injection iterations.

Main Results:

  • Demonstrated FrameD's utility through a case study comparing different strand copy utilization strategies.
  • Found that multiple sequence alignment's benefit is dependent on error rates and types, not always advantageous.
  • Highlighted FrameD's capability to support diverse designs and massive parallelism.

Conclusions:

  • FrameD addresses the need for a common simulation platform in the DNA storage community.
  • The modular and extensible framework accelerates the design and evaluation of DNA-based storage systems.
  • FrameD's support for parallelism aids in handling large-scale sequencing data and error analysis.