Towards Context-Aware DNA Sequence Compression for Efficient Data Exchange

DNA sequencing has emerged as one of the principal research directions in systems biology because of its usefulness in predicting the provenance of disease but also has profound impact in other fields like biotechnology, biological systematic and forensic medicine. The experiments in high throughput DNA sequencing technology are notorious for generating DNA sequences in huge quantities, and this poses a challenge in the computation, storage and exchange of sequence data. Computing on the Cloud helps mitigate the first two challenges because it gives on-demand machines through which we are able to save cost and it gives flexibility to balance the load, both computation- and storage-wise. The problem with data exchange could be mitigated to an extent through the use of data compression. This work proposes a context-aware framework that decides the compression algorithm which can minimize the time-to-completion and efficiently utilize the resources by performing experiments on different Cloud and algorithm combinations and configurations. The results obtained from this framework and experimental setup shows that DNAX is better than rest of the algorithms in any context, but if the file size is less than 50kb then one can go for CTW or Gencompress. The Gzip algorithm which is used in the NCBI repository to store the sequences has the worst compression ratio and time.