MOTIVATION: Error detection/correction codes play an important role to reduce writing and/or reading costs in DNA data storage. Sequence analysis algorithms also make a crucial effect on error correction but have been executed independently from the decoding of error correction codes. In conventional sequence analysis, low-quality reads are usually discarded. For DNA data storage, low-quality reads can be constructively used to sequence analysis with the assistance of error detection/correction codes. RESULTS: We obtained the low-quality reads which failed to pass the chastity filter in Illumina NGS sequencing. We confirmed the effectiveness of the extra low-quality reads by providing error statistics and performing decoding with them. We proposed a sequence clustering algorithm for various-length reads and a consensus algorithm based on probabilistic majority and error detection to efficiently exploit the extra reads. The proposed methods reduced the reading cost by 6.83% on average and up to 19.67% while maintaining the writing cost. AVAILABILITY AND IMPLEMENTATION: https://github.com/PParkJy/SAD-DNAstorage (10.5281/zenodo.15571858).

MOTIVATION: In DNA storage systems, there are tradeoffs between writing and reading costs. Increasing the code rate of error-correcting codes may save writing cost, but it will need more sequence reads for data retrieval. There is potentially a way to improve sequencing and decoding processes in such a way that the reading cost induced by this tradeoff is reduced without increasing the writing cost. In past researches, clustering, alignment and decoding processes were considered as separate stages but we believe that using the information from all these processes together may improve decoding performance. Actual experiments of DNA synthesis and sequencing should be performed because simulations cannot be relied on to cover all error possibilities in practical circumstances. RESULTS: For DNA storage systems using fountain code and Reed-Solomon (RS) code, we introduce several techniques to improve the decoding performance. We designed the decoding process focusing on the cooperation of key components: Hamming-distance based clustering, discarding of abnormal sequence reads, RS error correction as well as detection and quality score-based ordering of sequences. We synthesized 513.6 KB data into DNA oligo pools and sequenced this data successfully with Illumina MiSeq instrument. Compared to Erlich's research, the proposed decoding method additionally incorporates sequence reads with minor errors which had been discarded before, and thus was able to make use of 10.6-11.9% more sequence reads from the same sequencing environment, this resulted in 6.5-8.9% reduction in the reading cost. Channel characteristics including sequence coverage and read-length distributions are provided as well. AVAILABILITY AND IMPLEMENTATION: The raw data files and the source codes of our experiments are available at: https://github.com/jhjeong0702/dna-storage.

UNLABELLED: EcoProDB is a web-based database for comparative proteomics of Escherichia coli. The database contains information on E. coli proteins identified on 2D gels along with other resources collected from various databases and published literature, with a special feature of showing the expression levels of E. coli proteins under different genetic and environmental conditions. It also provides comparative information of subcellular localization, theoretical 2D map, experimental 2D map and integrated protein information via an interactive web interface and application such as the Map Browser. Users can also upload their own 2D gels, extract core information associated with the proteins and 2D gel results from different experiments and consequently generate new knowledge and hypotheses for further studies. AVAILABILITY: EcoProDB database system is accessible at http://eecoli.kaist.ac.kr.