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.

MOTIVATION: The data deluge of high-throughput sequencing (HTS) has posed great challenges to data storage and transfer. Many specific compression tools have been developed to solve this problem. However, most of the existing compressors are based on central processing unit (CPU) platform, which might be inefficient and expensive to handle large-scale HTS data. With the popularization of graphics processing units (GPUs), GPU-compatible sequencing data compressors become desirable to exploit the computing power of GPUs. RESULTS: We present a GPU-accelerated reference-free read compressor, namely CURC, for FASTQ files. Under a GPU-CPU heterogeneous parallel scheme, CURC implements highly efficient lossless compression of DNA stream based on the pseudogenome approach and CUDA library. CURC achieves 2-6-fold speedup of the compression with competitive compression rate, compared with other state-of-the-art reference-free read compressors. AVAILABILITY AND IMPLEMENTATION: CURC can be downloaded from https://github.com/BioinfoSZU/CURC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.