title: An MCMC algorithm for detecting short adjacent repeats shared by multiple sequences.
abstract: BACKGROUND
  Repeats detection problems are traditionally formulated as string matching or signal processing problems. They cannot readily handle gaps between repeat units and are incapable of detecting repeat patterns shared by multiple sequences. This study detects short adjacent repeats with interunit insertions from multiple sequences. For biological sequences, such studies can shed light on molecular structure, biological function and evolution.
  
  
  RESULTS
  The task of detecting short adjacent repeats is formulated as a statistical inference problem by using a probabilistic generative model. An Markov chain Monte Carlo algorithm is proposed to infer the parameters in a de novo fashion. Its applications on synthetic and real biological data show that the new method not only has a competitive edge over existing methods, but also can provide a way to study the structure and the evolution of repeat-containing genes.
  
  
  BACKGROUND
  The related C++ source code and datasets are available at http://ihome.cuhk.edu.hk/%7Eb118998/share/BASARD.zip.
  
  
  BACKGROUND
  xfan@sta.cuhk.edu.hk
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong.
authors.forename: Qiwei
authors.initials: Q
authors.lastname: Li
authors.forename: Xiaodan
authors.initials: X
authors.lastname: Fan
authors.forename: Tong
authors.initials: T
authors.lastname: Liang
authors.forename: Shuo-Yen R
authors.initials: SY
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001499:Bayes Theorem; D019295:Computational Biology; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D020080:Tandem Repeat Sequences