Increasing of Compression Efficiency for Genomic Data by Manipulating Empirical Entropy

Abstract

Sharing bio-informatics data is the key point to constructing a mobile and effective telemedicine network that brings with it various difficulties. A crucial challenge with this tremendous amount of information is storing it reversibly and analysing terabytes of data. Robust compression algorithms come up with a high rate of text and image compression ratios. However, the achievement of these advanced techniques has remained in a limited range since, intrinsically, the entropy contained by the raw data primarily determines the efficiency of compression. To enhance the performance of a compression algorithm, entropy of raw data needs to be reduced before any basic compression which reveals more effective redundancy. In this study, we use reversible sorting techniques to reduce the entropy thus providing higher efficiency in the case of integrating into compression technique for raw genomic data. To that end, permutation-based reversible sorting algorithms, such as Burrow-wheeler, are designed as a transform for entropy reduction. The algorithm achieves a low-entropy sequence by reordering raw data reversibly with low complexity and a fast approach. The empirical entropy, a quantitative analysis, shows a significant reduction of uncertainty has been achieved.