title: CWig: compressed representation of Wiggle/BedGraph format.
abstract: BACKGROUND
  BigWig, a format to represent read density data, is one of the most popular data types. They can represent the peak intensity in ChIP-seq, the transcript expression in RNA-seq, the copy number variation in whole genome sequencing, etc. UCSC Encode project uses the bigWig format heavily for storage and visualization. Of 5.2 TB Encode hg19 database, 1.6 TB (31% of the total space) is used to store bigWig files. BigWig format not only saves a lot of space but also supports fast queries that are crucial for interactive analysis and browsing. In our benchmark, bigWig often has similar size to the gzipped raw data, while is still able to support ∼ 5000 random queries per second.
  
  
  RESULTS
  Although bigWig is good enough at the moment, both storage space and query time are expected to become limited when sequencing gets cheaper. This article describes a new method to store density data named CWig. The format uses on average one-third of the size of existing bigWig files and improves random query speed up to 100 times.
  
  
  METHODS
  http://genome.ddns.comp.nus.edu.sg/∼cwig.
authors.affiliation: Department of Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672 and Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417.
authors.forename: Do
authors.initials: D
authors.lastname: Huy Hoang
authors.affiliation: Department of Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672 and Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417 Department of Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672 and Department of Computer Science, School of Computing, National University of Singapore, Singapore 117417.
authors.forename: Wing-Kin
authors.initials: WK
authors.lastname: Sung
mesh_terms: D056915:DNA Copy Number Variations; D044962:Data Compression / Q000379:methods*; D057225:Data Mining; D030541:Databases, Genetic; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D017423:Sequence Analysis, RNA