@InProceedings{chitsaz09:_birna,
  author =       {Hamidreza Chitsaz and Rolf Backofen and S. Cenk Sahinalp},
  title =        {{biRNA}: Fast {RNA-RNA} Binding Sites Prediction},
  booktitle = {Proc. of the 9th Workshop on Algorithms in
                  Bioinformatics {(WABI)}},
  editor    = {Steven Salzberg and Tandy Warnow},
  series    = {Lecture Notes in Computer Science},
  publisher = {Springer Berlin / Heidelberg},
  volume    = 5724,
  year      = 2009,
  pages     = {25-36},
  isbn      = {978-3-642-04240-9},
  doi        = {10.1007/978-3-642-04241-6},
  abstract =  {We present biRNA, a novel algorithm for prediction of
                  binding sites between two RNAs based on minimization
                  of binding free energy. Similar to RNAup approach, we
                  assume the binding free energy is the sum of
                  accessibility and the interaction free energies. Our
                  algorithm maintains tractability and speed and also
                  has two important advantages over previous similar
                  approaches: 1) it is able to predict multiple
                  simultaneous binding sites and 2) it computes a more
                  accurate interaction free energy by considering both
                  intramolecular and intermolecular base
                  pairing. Moreover, biRNA can handle crossing
                  interactions as well as hairpins interacting in a
                  zigzag fashion. 

                  To deal with simultaneous
                  accessibility of binding sites, our algorithm models
                  their joint probability of being unpaired. Since
                  computing the exact joint probability distribution is
                  intractable, we approximate the joint probability by a
                  polynomially representable graphical model namely a
                  Chow-Liu tree-structured Markov Random
                  Field. Experimental results show that biRNA
                  outperforms RNAup and also support the accuracy of our
                  approach. Our proposed Bayesian approximation of the
                  Boltzmann joint probability distribution provides a
                  powerful, novel framework that can also be utilized in
                  other applications.} ,
  user =	 {backofen}
}