Problem
Research questions and friction points this paper is trying to address.
DNA data storage
codecs
standardized evaluation
benchmarking
fragmented landscape
The Unified Evaluation App for DNA Data Storage Codecs
🤖 AI Summary
This work addresses the lack of a unified evaluation framework for DNA data storage encoding schemes, which hinders fair performance comparisons. To this end, we present an open-source, modular benchmarking platform that establishes the first standardized, multidimensional, and extensible evaluation framework specifically designed for DNA storage codecs. Built upon consensus standards from the DNA Data Storage Alliance, the platform integrates diverse benchmark datasets and an automated evaluation pipeline to enable reproducible assessment across key dimensions—including throughput, computational efficiency, error-correction capability (covering substitution, insertion, and deletion errors), and cost. Experimental results demonstrate that no single algorithm dominates across all metrics. The platform supports plug-and-play integration of new algorithms and, through visual and tabular reporting, quantitatively reveals trade-offs among information density, success rate, runtime, and cost, thereby providing empirical guidance for real-world deployment and future format design.
📝 Abstract
Background: Deoxyribonucleic acid (DNA) data storage is a paradigm with great potential for ultra-dense and durable information preservation. However, the rapid proliferation of coding schemes, or codecs, each with their own design constraints and reporting practices, has led to a fragmented landscape that lacks a standardized comparative assessment.
Methods: We developed an open-source, modular benchmarking platform that systematically integrates and evaluates state-of-the-art DNA storage encoding and decoding methods (codecs). Our approach uses a curated, diverse set of baseline data and applies multidimensional assessment criteria that are aligned with the consensus standard of the DNA Data Storage Alliance. These criteria include encoding/decoding throughput, computational efficiency, error correction performance across substitutions, insertions, and deletions, and cost efficiency.
Results: The developed platform integrates standardized wrapper functions for encoding and decoding, allows for the integration of new methods, and automates reproducible evaluations with comprehensive visual and tabular reporting. Benchmarking both contemporary and classical codecs using their default parameters and multiple metrics demonstrates that no single algorithm is optimal across all evaluated dimensions. The trade-offs between information density, success rate, runtime, and cost are quantified and shown to be critical factors in the design of future-proof formats.
Conclusions: Our work establishes a rigorously standardized, open-source evaluation framework that enables reproducible benchmarking, supports evidence-based codec selection, and provides the necessary foundation for translating DNA data storage from experimental research into deployable archival systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
DNA data storage
codec benchmarking
standardized evaluation
error correction
open-source framework
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