title: SAMQA: error classification and validation of high-throughput sequenced read data.
abstract: BACKGROUND
  The advances in high-throughput sequencing technologies and growth in data sizes has highlighted the need for scalable tools to perform quality assurance testing. These tests are necessary to ensure that data is of a minimum necessary standard for use in downstream analysis. In this paper we present the SAMQA tool to rapidly and robustly identify errors in population-scale sequence data.
  
  
  RESULTS
  SAMQA has been used on samples from three separate sets of cancer genome data from The Cancer Genome Atlas (TCGA) project. Using technical standards provided by the SAM specification and biological standards defined by researchers, we have classified errors in these sequence data sets relative to individual reads within a sample. Due to an observed linearithmic speedup through the use of a high-performance computing (HPC) framework for the majority of tasks, poor quality data was identified prior to secondary analysis in significantly less time on the HPC framework than the same data run using alternative parallelization strategies on a single server.
  
  
  CONCLUSIONS
  The SAMQA toolset validates a minimum set of data quality standards across whole-genome and exome sequences. It is tuned to run on a high-performance computational framework, enabling QA across hundreds gigabytes of samples regardless of coverage or sample type.
authors.affiliation: Institute for Systems Biology, 401 Terry Ave N, Seattle, WA 98109, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Robinson
authors.forename: Sarah
authors.initials: S
authors.lastname: Killcoyne
authors.forename: Ryan
authors.initials: R
authors.lastname: Bressler
authors.forename: John
authors.initials: J
authors.lastname: Boyle
mesh_terms: D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods* / Q000592:standards; D006801:Humans; D011786:Quality Control; D012107:Research Design