title: Light-weight reference-based compression of FASTQ data.
abstract: BACKGROUND
  The exponential growth of next generation sequencing (NGS) data has posed big challenges to data storage, management and archive. Data compression is one of the effective solutions, where reference-based compression strategies can typically achieve superior compression ratios compared to the ones not relying on any reference.
  
  
  RESULTS
  This paper presents a lossless light-weight reference-based compression algorithm namely LW-FQZip to compress FASTQ data. The three components of any given input, i.e., metadata, short reads and quality score strings, are first parsed into three data streams in which the redundancy information are identified and eliminated independently. Particularly, well-designed incremental and run-length-limited encoding schemes are utilized to compress the metadata and quality score streams, respectively. To handle the short reads, LW-FQZip uses a novel light-weight mapping model to fast map them against external reference sequence(s) and produce concise alignment results for storage. The three processed data streams are then packed together with some general purpose compression algorithms like LZMA. LW-FQZip was evaluated on eight real-world NGS data sets and achieved compression ratios in the range of 0.111-0.201. This is comparable or superior to other state-of-the-art lossless NGS data compression algorithms.
  
  
  CONCLUSIONS
  LW-FQZip is a program that enables efficient lossless FASTQ data compression. It contributes to the state of art applications for NGS data storage and transmission. LW-FQZip is freely available online at: http://csse.szu.edu.cn/staff/zhuzx/LWFQZip.
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. 2120230303@email.szu.edu.cn.
authors.forename: Yongpeng
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. li5linsen@163.com.
authors.forename: Linsen
authors.initials: L
authors.lastname: Li
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. yylszu@163.com.
authors.forename: Yanli
authors.initials: Y
authors.lastname: Yang
authors.affiliation: The Broad Institute, Cambridge, MA, 02142, USA. xiaoyang@broadinstitute.org.
authors.forename: Xiao
authors.initials: X
authors.lastname: Yang
authors.affiliation: School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK. S.He@cs.bham.ac.uk.
authors.forename: Shan
authors.initials: S
authors.lastname: He
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China. zhuzx@szu.edu.cn.
authors.forename: Zexuan
authors.initials: Z
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software