Network Coding For Data Compression In Genomic Sequencing

The exponential growth of next-generation sequencing technologies has resulted in a massive influx of genomic data, posing significant challenges in storage, transmission, and processing. Conventional compression methods, such as gzip and bzip2, are not optimized for the unique structural patterns and redundancies of genomic sequences, leading to suboptimal performance. Addressing this gap, our research introduces a novel data compression framework that leverages network coding principles to enhance efficiency, scalability, and reliability. Network coding, widely used in communication systems, is adapted here to process and compress genomic data by employing finite field arithmetic and redundancy-aware encoding techniques. Our method achieves a compression ratio of up to 50% while maintaining a data integrity level of 99.99%, as validated through extensive mathematical analysis and experimental testing on diverse genomic datasets. Furthermore, the framework demonstrates linear scalability and robust error resilience, making it particularly suited for large-scale applications in clinical genomics and research. By bridging the gap between traditional compression algorithms and the growing demands of modern genomic data, this study lays the groundwork for more efficient and reliable data management solutions in bioinformatics.