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The well-known massively parallel sequencing method is efficient and it can obtain sequence data from multiple individual samples. In order to ensure that sequencing, replication, and oligonucleotide synthesis errors do not result in tags (or barcodes) that are unrecoverable or confused, the tag sequences should be abundant and sufficiently different. Recently, many design methods have been proposed for correcting errors in data using error-correcting codes. The existing tag sets contain small tag sequences, so we used a modified genetic algorithm to improve the lower bound of the tag sets in this study. Compared with previous research, our algorithm is effective for designing sets of DNA tags. Moreover, the GC content determined by existing methods includes an imprecise range. Thus, we improved the GC content determination method to obtain tag sets that control the GC content in a more precise range. Finally, previous studies have only considered perfect self-complementarity. Thus, we considered the crossover between different tags and introduced an improved constraint into the design of tag sets. [ABSTRACT FROM AUTHOR]

Biotechnological and biomolecular advances have introduced novel uses for DNA such as DNA computing, storage, and encryption. For these applications, DNA sequence design requires maximal desired (and minimal undesired) hybridizations, which are the product of a single new DNA strand from 2 single DNA strands. Here, we propose a novel constraint to design DNA sequences based on thermodynamic properties. Existing constraints for DNA design are based on the Hamming distance, a constraint that does not address the thermodynamic properties of the DNA sequence. Using a unique, improved genetic algorithm, we designed DNA sequence sets which satisfy different distance constraints and employ a free energy gap based on a minimum free energy (MFE) to gauge DNA sequences based on set thermodynamic properties. When compared to the best constraints of the Hamming distance, our method yielded better thermodynamic qualities. We then used our improved genetic algorithm to obtain lower-bound DNA sequence sets. Here, we discuss the effects of novel constraint parameters on the free energy gap. [ABSTRACT FROM AUTHOR]