WBFQC: A new approach for compressing next-generation sequencing data splitting into homogeneous streams

摘要

Genomic data nowadays is playing a vital role in number of fields such as personalized medicine, forensic, drug discovery, sequence alignment and agriculture, etc. With the advancements and reduction in the cost of next-generation sequencing (NGS) technology, these data are growing exponentially. NGS data are being generated more rapidly than they could be significantly analyzed. Thus, there is much scope for developing novel data compression algorithms to facilitate data analysis along with data transfer and storage directly. An innovative compression technique is proposed here to address the problem of transmission and storage of large NGS data. This paper presents a lossless non-reference-based FastQ file compression approach, segregating the data into three different streams and then applying appropriate and efficient compression algorithms on each. Experiments show that the proposed approach (WBFQC) outperforms other state-of-the-art approaches for compressing NGS data in terms of compression ratio (CR), and compression and decompression time. It also has random access capability over compressed genomic data. An open source FastQ compression tool is also provided here (http://www.algorithm-skg.com/wbfqc/home.html).