Recent advancements in sequencing methods have led to significant increase in sequencing data. Increase in sequencing data leads to research challenges such as storage, transfer, processing, etc. data compression techniques have been opted to cope with the storage of these data. There have been good achievements in compression ratio and execution time. This fast-paced advancement has raised major concerns about the security of data. Confidentiality, integrity, authenticity of data needs to be ensured. This paper presents a novel lossless reference-free algorithm that focuses on data compression along with encryption to achieve security in addition to other parameters. The proposed algorithm uses preprocessing of data before applying general-purpose compression library. Genetic algorithm is used to encrypt the data. The technique is validated with experimental results on benchmark datasets. Comparative analysis with state-of-the-art techniques is presented. The results show that the proposed method achieves better results in comparison to existing methods.

测序方法的最新进展导致测序数据显着增加。测序数据的增加带来了存储、传输、处理等研究挑战，因此选择了数据压缩技术来应对这些数据的存储。在压缩比和执行时间上都取得了不错的成绩。这种快节奏的进步引起了人们对数据安全的重大担忧。需要确保数据的机密性、完整性、真实性。本文提出了一种新颖的无损无参考算法，该算法侧重于数据压缩和加密，以实现除其他参数之外的安全性。该算法在应用通用压缩库之前对数据进行预处理。遗传算法用于加密数据。该技术通过基准数据集的实验结果得到验证。提出了与最先进技术的比较分析。结果表明，与现有方法相比，所提出的方法取得了更好的结果。