title: An approximate Bayesian approach for mapping paired-end DNA reads to a reference genome.
abstract: CONCLUSIONS
  Many high-throughput sequencing experiments produce paired DNA reads. Paired-end DNA reads provide extra positional information that is useful in reliable mapping of short reads to a reference genome, as well as in downstream analyses of structural variations. Given the importance of paired-end alignments, it is surprising that there have been no previous publications focusing on this topic. In this article, we present a new probabilistic framework to predict the alignment of paired-end reads to a reference genome. Using both simulated and real data, we compare the performance of our method with six other read-mapping tools that provide a paired-end option. We show that our method provides a good combination of accuracy, error rate and computation time, especially in more challenging and practical cases, such as when the reference genome is incomplete or unavailable for the sample, or when there are large variations between the reference genome and the source of the reads. An open-source implementation of our method is available as part of Last, a multi-purpose alignment program freely available at http://last.cbrc.jp.
  
  
  BACKGROUND
  martin@cbrc.jp
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Computational Biology Research Center, National Institute for Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo, Japan.
authors: Anish Man Singh
authors: AM
authors: Shrestha
authors: Martin C
authors: MC
authors: Frith
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002874:Chromosome Mapping; D041321:Chromosomes, Human, X; D015894:Genome, Human; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008253:Macaca mulatta; D015233:Models, Statistical; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software