Polus: A context-aware enhancement framework for DNA storage via Transformer-based soft-decision decoding

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ID: 322857
2026
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Abstract
MOTIVATION: DNA storage offers exceptional information density and archival longevity but is constrained by complex biochemical noise inherent to synthesis, storage, and sequencing. Conventional hard-decision error-correction schemes often rely on excessive redundancy to mitigate these imperfections, which significantly compromises storage efficiency and density. RESULTS: We present Polus, a Transformer-based enhancement framework that improves digital reliability through soft-decision decoding (SDD) without requiring encoder modification. At its core is SeqFormer, a Transformer-based channel model that synergizes sequence context with quality signals to generate calibrated per-base confidence scores, effectively transforming uncertain biochemical noise into informative 'soft' erasures. In in silico benchmarks, Polus significantly upgrades mainstream DNA storage codecs. It reduces the sequencing coverage required for DNA Fountain by 38.9%-increasing effective physical density by approximately 80%-and eliminates persistent indel-induced errors in the Yin-Yang codec. Furthermore, it enables a targeted resequencing strategy that achieves full recovery with 99.9% less overhead than uniform deepening. Moreover, a nine-metric evaluation suite was employed to provide multi-dimensional quantitative comparisons of DNA storage codecs across reliability, density, and cost. Collectively, Polus provides a reproducible framework for context-aware decoding and system design guidance for DNA storage. AVAILABILITY: All source code of the Polus, including the SeqFormer implementation, codec algorithms, test data used and the simulation pipeline is available on GitHub (https://github.com/dinglulu/Polus) and Zenodo (https://zenodo.org/communities/bioinfoszu/). A web-hosted instance of Polus is available at https://polus.bioailab.net/polls/home. The SeqFormer model is also released as a standalone repository at https://github.com/dinglulu/SeqFormer and https://zenodo.org/communities/bioinfoszu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7171550244 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lulu Ding, Kun Wang, Hongmei Zhang, Shaohui Xie, Jinlong Wang, Bo Liu, Guohua Wang, Ling Liu, Zexuan Zhu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag563
URL
https://doi.org/10.1093/bioinformatics/btag563
Keywords Keywords not found

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