title: Accelerated probabilistic inference of RNA structure evolution.
abstract: BACKGROUND
  Pairwise stochastic context-free grammars (Pair SCFGs) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. The same problem is faced by other RNA alignment-and-folding algorithms based on Sankoff's 1985 algorithm. It is therefore desirable to constrain such algorithms, by pre-processing the sequences and using this first pass to limit the range of structures and/or alignments that can be considered.
  
  
  RESULTS
  We demonstrate how flexible classes of constraint can be imposed, greatly reducing the computational costs while maintaining a high quality of structural homology prediction. Any score-attributed context-free grammar (e.g. energy-based scoring schemes, or conditionally normalized Pair SCFGs) is amenable to this treatment. It is now possible to combine independent structural and alignment constraints of unprecedented general flexibility in Pair SCFG alignment algorithms. We outline several applications to the bioinformatics of RNA sequence and structure, including Waterman-Eggert N-best alignments and progressive multiple alignment. We evaluate the performance of the algorithm on test examples from the RFAM database.
  
  
  CONCLUSIONS
  A program, Stemloc, that implements these algorithms for efficient RNA sequence alignment and structure prediction is available under the GNU General Public License.
authors.affiliation: Department of Bioengineering, University of California, Berkeley, CA 94720-1762, USA. ihh@berkeley.edu
authors.forename: Ian
authors.initials: I
authors.lastname: Holmes
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D030562:Databases, Protein; D015233:Models, Statistical; D009690:Nucleic Acid Conformation; D010363:Pattern Recognition, Automated; D011336:Probability; D017434:Protein Structure, Tertiary; D012313:RNA / Q000737:chemistry*; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA; D012689:Sequence Homology, Nucleic Acid; D012984:Software; D013997:Time Factors
chemical_list: D012313:RNA