title: Data-dependent bucketing improves reference-free compression of sequencing reads.
abstract: BACKGROUND
  The storage and transmission of high-throughput sequencing data consumes significant resources. As our capacity to produce such data continues to increase, this burden will only grow. One approach to reduce storage and transmission requirements is to compress this sequencing data.
  
  
  RESULTS
  We present a novel technique to boost the compression of sequencing that is based on the concept of bucketing similar reads so that they appear nearby in the file. We demonstrate that, by adopting a data-dependent bucketing scheme and employing a number of encoding ideas, we can achieve substantially better compression ratios than existing de novo sequence compression tools, including other bucketing and reordering schemes. Our method, Mince, achieves up to a 45% reduction in file sizes (28% on average) compared with existing state-of-the-art de novo compression schemes.
  
  
  METHODS
  Mince is written in C++11, is open source and has been made available under the GPLv3 license. It is available at http://www.cs.cmu.edu/∼ckingsf/software/mince.
  
  
  BACKGROUND
  carlk@cs.cmu.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Computer Science, Stony Brook University, Stony Brook, NY 11794-4400, USA and.
authors.forename: Rob
authors.initials: R
authors.lastname: Patro
authors.affiliation: Department Computational Biology, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.
authors.forename: Carl
authors.initials: C
authors.lastname: Kingsford
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D044962:Data Compression / Q000379:methods*; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software