Advancing Genomic Data Management through TCompress: An Innovative Method for Sequence Compression of Biological Sequence Compression Algorithm using Text-Compression Tools

Objectives: With the rapid growth of genetic data, there is an urgent need for efficient methods to compress biological sequences. This study aims to enhance the storage and retrieval of large volumes of genetic information by introducing TCompress, a new compression algorithm specifically designed for DNA sequences. Method: We developed a custom software tool called TCompress that converts biological sequences into binary format. This binary representation enables the use of standard text compression tools such as GenCompress, 7-Zip, GZip, and PeaZip. A detailed evaluation was conducted to measure TCompress’s compression ratio and efficiency in comparison to other commonly used methods. Findings: Our results show that TCompress improves compression efficiency by up to 30% over traditional techniques. It performs especially well on longer and more complex DNA sequences, offering better precision and preserving the integrity of genomic data more effectively than existing tools. Novelty: Unlike conventional methods, TCompress uses a novel binary conversion technique specifically designed to reflect the unique structure of DNA. This customized approach not only improves compression rates but also supports secure data handling. Beyond compression, TCompress also contributes to better and safer management of genetic information. Keywords: TCompression tools, Biological Sequence Compression, DNA/RNA Sequence Compression, Compression efficiency, Compression ratio