title: New insights from cluster analysis methods for RNA secondary structure prediction.
abstract: A widening gap exists between the best practices for RNA secondary structure prediction developed by computational researchers and the methods used in practice by experimentalists. Minimum free energy predictions, although broadly used, are outperformed by methods which sample from the Boltzmann distribution and data mine the results. In particular, moving beyond the single structure prediction paradigm yields substantial gains in accuracy. Furthermore, the largest improvements in accuracy and precision come from viewing secondary structures not at the base pair level but at lower granularity/higher abstraction. This suggests that random errors affecting precision and systematic ones affecting accuracy are both reduced by this 'fuzzier' view of secondary structures. Thus experimentalists who are willing to adopt a more rigorous, multilayered approach to secondary structure prediction by iterating through these levels of granularity will be much better able to capture fundamental aspects of RNA base pairing. WIREs RNA 2016, 7:278-294. doi: 10.1002/wrna.1334 For further resources related to this article, please visit the WIREs website.
authors.affiliation: School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0765, USA.
authors.forename: Emily
authors.initials: E
authors.lastname: Rogers
authors.affiliation: School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332-0160, USA.
authors.forename: Christine
authors.initials: C
authors.lastname: Heitsch
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry*
chemical_list: D012313:RNA