title: BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution.
abstract: BACKGROUND
  Base-calling of sequencing data produced by high-throughput sequencing platforms is a fundamental process in current bioinformatics analysis. However, existing third-party probabilistic or machine-learning methods that significantly improve the accuracy of base-calls on these platforms are impractical for production use due to their computational inefficiency.
  
  
  RESULTS
  We directly formulate base-calling as a blind deconvolution problem and implemented BlindCall as an efficient solver to this inverse problem. BlindCall produced base-calls at accuracy comparable to state-of-the-art probabilistic methods while processing data at rates 10 times faster in most cases. The computational complexity of BlindCall scales linearly with read length making it better suited for new long-read sequencing technologies.
authors.affiliation: Department of Computer Science, Center for Bioinformatics and Computational Biology and Applied Mathematics and Scientific Computing, University of Maryland, College Park, USA.
authors.forename: Chengxi
authors.initials: C
authors.lastname: Ye
authors.forename: Chiaowen
authors.initials: C
authors.lastname: Hsiao
authors.forename: Héctor
authors.initials: H
authors.lastname: Corrada Bravo
mesh_terms: D000465:Algorithms; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D011336:Probability; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D013997:Time Factors