title: Using Genome Query Language to uncover genetic variation.
abstract: BACKGROUND
  With high-throughput DNA sequencing costs dropping <$1000 for human genomes, data storage, retrieval and analysis are the major bottlenecks in biological studies. To address the large-data challenges, we advocate a clean separation between the evidence collection and the inference in variant calling. We define and implement a Genome Query Language (GQL) that allows for the rapid collection of evidence needed for calling variants.
  
  
  RESULTS
  We provide a number of cases to showcase the use of GQL for complex evidence collection, such as the evidence for large structural variations. Specifically, typical GQL queries can be written in 5-10 lines of high-level code and search large datasets (100 GB) in minutes. We also demonstrate its complementarity with other variant calling tools. Popular variant calling tools can achieve one order of magnitude speed-up by using GQL to retrieve evidence. Finally, we show how GQL can be used to query and compare multiple datasets. By separating the evidence and inference for variant calling, it frees all variant detection tools from the data intensive evidence collection and focuses on statistical inference.
  
  
  BACKGROUND
  GQL can be downloaded from http://cseweb.ucsd.edu/~ckozanit/gql.
authors.affiliation: Computer Science and Engineering, University of California San Diego, 9500 Gilman Drive, San Diego, CA 92123 and Microsoft Research, 1065 La Avenida, Mountain View, CA 94043, USA.
authors.forename: Christos
authors.initials: C
authors.lastname: Kozanitis
authors.forename: Andrew
authors.initials: A
authors.lastname: Heiberg
authors.forename: George
authors.initials: G
authors.lastname: Varghese
authors.forename: Vineet
authors.initials: V
authors.lastname: Bafna
mesh_terms: D000465:Algorithms; D014644:Genetic Variation; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D017422:Sequence Analysis, DNA; D012984:Software