title: CODOC: efficient access, analysis and compression of depth of coverage signals.
abstract: Current data formats for the representation of depth of coverage data (DOC), a central resource for interpreting, filtering or detecting novel features in high-throughput sequencing datasets, were primarily designed for visualization purposes. This limits their applicability in stand-alone analyses of these data, mainly owing to inaccurate representation or mediocre data compression. CODOC is a novel data format and comprehensive application programming interface for efficient representation, access and analysis of DOC data. CODOC compresses these data ∼ 4-32× better than the best current comparable method by exploiting specific data characteristics while at the same time enabling more-exact signal recovery for lossy compression and very fast query answering times.
  
  
  METHODS
  Java source code and binaries are freely available for non-commercial use at http://purl.org/bgraph/codoc.
authors.affiliation: Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna and Medical University of Vienna, Dr. Bohrgasse 9, 1030 Vienna, Austria.
authors.forename: Niko
authors.initials: N
authors.lastname: Popitsch
mesh_terms: D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D057225:Data Mining; D059014:High-Throughput Nucleotide Sequencing; D013223:Statistics as Topic / Q000379:methods*; D013997:Time Factors; D014584:User-Computer Interface