Next-Generation Sequencing Data Compression and Storage Solutions
Abstract
They produce huge amount of data which require efficient compression and management mechanisms hence the need to adopt Next-Generation Sequencing (NGS). In this paper, the performance of different compression algorithms and their implementation with local and cloud-based storage systems are assessed and compared using the lossless and lossy storage efficiency analysis. This study reveals that reference-based compression shows the highest compression ratio on the whole with lossy methods having shorter processing time ahead of other methods. A comparison of the costs shows the feasibility of local and cloud storage options to bear substantial costs. The study further supports that sophisticated algorithms as well as universally recognized formats are crucial for organizing NGS data Stream.