DNA storage reads reconstructed faster with streaming consensus method
ACOR: incremental evidence accumulation towards real-time DNA storage reconstruction
Information Retrieval
Summary
Storing data in DNA strands is promising because DNA holds lots of information in a tiny space and lasts a long time. However, reading data back is tricky because the DNA strands can be noisy, so multiple copies need to be combined carefully. The authors developed ACOR, which processes sequence data as it is being read instead of waiting until all data is collected. This streaming approach speeds up reconstruction dramatically, making it practical to handle millions of DNA reads quickly while sequencing continues.
What this means in practice
- •For biotech engineers: Accelerate DNA data retrieval by reconstructing sequences in real-time during sequencing runs.
- •For data storage architects: Enable design of DNA-based archival systems with inline reconstruction to reduce decoding delays.
Authors
Linxuan Han, Huaming Wu, Shi Jia, Guanjin Qu
Abstract
DNA offers high-density, durable storage by encoding digital information into synthetic strands and retrieving it through sequencing. Sequence reconstruction combines noisy copies of each strand before downstream decoding. Conventional offline methods typically reconstruct completed read clusters, separating reconstruction from ongoing read collection. Streaming reconstruction offers a way to overlap evidence processing with sequencing and reduce the computation remaining before downstream decoding. We present ACOR, a streaming consensus reconstruction method that records $k$-mer transitions and boundary support in a persistent ledger, then decodes through length-constrained beam search and priority-based selection at cluster closure. Evaluation on real and simulated DNA-storage datasets showed competitive recovery. On our own short-read next-generation sequencing (NGS) wet-lab library, ACOR combined near-complete recovery with second-scale reconstruction of more than ten million reads. A smaller public benchmark was completed in a fraction of a second. Parallel ACOR achieved speedups of tens to over a thousand-fold relative to the tested serial baseline configurations. Sparse storage reduced high-context memory requirements, while parallel execution accelerated reconstruction computation. ACOR is designed for streaming readout pipelines that process assigned reads while sequencing continues.