Year: 2024
Type: article
Abstract: The rapid advancements in high-throughput DNA sequencing technologies have ushered in a transformative era in genomics and bioinformatics. The generation of vast DNA sequence datasets has become commonplace, contributing to critical areas such as personalized medicine, drug discovery, and agricultural biotechnology. However, the storage, transmission, and processing of these massive datasets pose significant challenges due to the inherently large size of DNA sequences. To address these challenges, various DNA sequence compression algorithms have been proposed. This paper introduces DNASeqCompress, an innovative algorithm tailored specifically for compressing DNA and RNA sequences. DNASeqCompress employs a statistical model-based approach to identify and compress repetitive sub-sequences efficiently. It selects frequent sub-sequences and stores them along with their positional information, resulting in reduced storage and transmission sizes. We implement and evaluate DNASeqCompress on various DNA sequences, comparing its performance with the existing GenCompress algorithm. Through our experiments, we demonstrate that DNASeqCompress outperforms GenCompress in terms of compression and ease of implementation, particularly for sequences with repetitive patterns. The average percentage of improvement of the proposed algorithm (DNASeqCompress) over GenCompress is approximately 15.52% observed across a diverse dataset of DNA sequences. This research provides a comprehensive analysis and comparison of DNASeqCompress and GenCompress, contributing valuable insights into DNA sequence compression algorithms.
DOI: 10.22399/ijcesen.675
Institution: Andhra University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405330290