title: GenAp: a distributed SQL interface for genomic data.
abstract: BACKGROUND
  The impressively low cost and improved quality of genome sequencing provides to researchers of genetic diseases, such as cancer, a powerful tool to better understand the underlying genetic mechanisms of those diseases and treat them with effective targeted therapies. Thus, a number of projects today sequence the DNA of large patient populations each of which produces at least hundreds of terra-bytes of data. Now the challenge is to provide the produced data on demand to interested parties.
  
  
  RESULTS
  In this paper, we show that the response to this challenge is a modified version of Spark SQL, a distributed SQL execution engine, that handles efficiently joins that use genomic intervals as keys. With this modification, Spark SQL serves such joins more than 50× faster than its existing brute force approach and 8× faster than similar distributed implementations. Thus, Spark SQL can replace existing practices to retrieve genomic data and, as we show, allow users to reduce the number of lines of software code that needs to be developed to query such data by an order of magnitude.
authors: Department of Computer Science, University of California Berkeley, Soda Hall, Berkeley, 94720, California, USA. kozanitis@eecs.berkeley.edu.
authors: Christos
authors: C
authors: Kozanitis
authors: Department of Computer Science, University of California Berkeley, Soda Hall, Berkeley, 94720, California, USA. pattersn@eecs.berkeley.edu.
authors: David A
authors: DA
authors: Patterson
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D014584:User-Computer Interface