title: Probabilistic single-individual haplotyping.
abstract: BACKGROUND
  Accurate haplotyping-determining from which parent particular portions of the genome are inherited-is still mostly an unresolved problem in genomics. This problem has only recently started to become tractable, thanks to the development of new long read sequencing technologies. Here, we introduce ProbHap, a haplotyping algorithm targeted at such technologies. The main algorithmic idea of ProbHap is a new dynamic programming algorithm that exactly optimizes a likelihood function specified by a probabilistic graphical model and which generalizes a popular objective called the minimum error correction. In addition to being accurate, ProbHap also provides confidence scores at phased positions.
  
  
  RESULTS
  On a standard benchmark dataset, ProbHap makes 11% fewer errors than current state-of-the-art methods. This accuracy can be further increased by excluding low-confidence positions, at the cost of a small drop in haplotype completeness.
  
  
  BACKGROUND
  Our source code is freely available at: https://github.com/kuleshov/ProbHap.
authors.affiliation: Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
authors.forename: Volodymyr
authors.initials: V
authors.lastname: Kuleshov
mesh_terms: D000465:Algorithms; D015894:Genome, Human; D023281:Genomics; D006239:Haplotypes; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D017422:Sequence Analysis, DNA / Q000379:methods*