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 AND IMPLEMENTATION: 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/.

MOTIVATION: High-fidelity simulation of nanopore sequencing signals is critical for rigorous benchmarking and validation of the nanopore signal processing pipeline. However, existing signal simulators often fail to capture the non-linear dynamics of nanopore current signals, relying on static pore models or lacking optimization objectives tied to basecalling, resulting in synthetic signals with low basecalling accuracy and fidelity. RESULTS: We introduce NanoSimFormer, an end-to-end Transformer-based signal simulator that integrates basecaller guidance during training to generate high-fidelity nanopore signals. NanoSimFormer achieves a median basecalling accuracy exceeding 99% and Q-scores above 22.8 for Oxford Nanopore Technologies' latest DNA R10.4.1 and direct RNA sequencing, closely mirroring real experimental baselines. It faithfully recapitulates experimental variant calling performance across the five human samples, achieving F1-scores of 0.9953-0.9973 and 0.7862-0.8612 for single-nucleotide polymorphisms and small indels detections, respectively. Compared with previous simulators, NanoSimFormer also substantially reduces false positives in homopolymer and short tandem repeat regions. NanoSimFormer-derived reads enable high-quality de novo bacterial assembly with consensus error rates below one mismatch per 100 kbp and maintain high correlations with experimental abundance in metagenomic and transcriptomic datasets. AVAILABILITY AND IMPLEMENTATION: NanoSimFormer is freely available on GitHub at: https://github.com/BioinfoSZU/NanoSimFormer.

Polar codes have been proven to be capable of achieving the optimal rate for the lossless compression problem. However, their finite-length performance is not satisfactory due to the insufficient polarization effect. In this work, we combine source polarization with several entropy coding techniques to improve the compression efficiency while keeping the additional complexity negligible. In our framework, the standard encoding of polar codes can be treated as a pre-transform on the source data, and only a small proportion of the transformed data needs further compression thanks to the source polarization. We show that our framework is compatible with the mainstream entropy coding schemes such as Huffman coding, arithmetic coding, and asymmetric number system (ANS). To optimize performance, an iterative algorithm is proposed for the set partitioning of the transformed data. Simulation results show that the improved scheme is superior to the original polar source coding.