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CWig: compressed representation of Wiggle/BedGraph format.

Do Huy Hoang1, Wing-Kin Sung2

  • 1Department 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.

Bioinformatics (Oxford, England)
|May 29, 2014
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Summary
This summary is machine-generated.

A new method called CWig offers a more efficient way to store genomic read density data. CWig files are one-third the size of BigWig files and offer 100x faster query speeds.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Data Storage

Background:

  • BigWig is a popular format for storing genomic read density data, widely used in projects like UCSC Encode.
  • It efficiently stores data for ChIP-seq, RNA-seq, and whole genome sequencing, but faces limitations with increasing data volumes.
  • BigWig files constitute 31% of the Encode hg19 database, highlighting the significant storage demands.

Purpose of the Study:

  • To address the growing storage and query time limitations of the BigWig format.
  • To introduce a novel data storage method, CWig, for improved efficiency in handling genomic density data.

Main Methods:

  • Development of the CWig (compressed Wig) data format.
  • Benchmarking CWig against BigWig for file size and random query performance.

Main Results:

  • CWig files achieve an average size reduction to one-third of BigWig files.
  • CWig demonstrates a significant improvement in random query speed, up to 100 times faster than BigWig.

Conclusions:

  • CWig presents a substantial advancement in genomic data storage and retrieval.
  • This new format is poised to become essential as sequencing costs decrease and data generation increases.