Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble.
As the biomedical impact of small RNAs grows, so does the need to understand competing structural alternatives for regions of functional interest. Suboptimal structure analysis provides significantly more RNA base pairing information than a single minimum free energy prediction. Yet computational enhancements like Boltzmann sampling have not been fully adopted by experimentalists since identifying meaningful patterns in this data can be challenging. Profiling is a novel approach to mining RNA suboptimal structure data which makes the power of ensemble-based analysis accessible in a stable and reliable way. Balancing abstraction and specificity, profiling identifies significant combinations of base pairs which dominate low-energy RNA secondary structures. By design, critical similarities and differences are highlighted, yielding crucial information for molecular biologists. The code is freely available via http://gtfold.sourceforge.net/profiling.html.
School of Computational Science and Engineering, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA 30332-0765, USA.
Emily
E
Rogers
School of Mathematics, Georgia Institute of Technology, 686 Cherry St., Atlanta, GA 30332-0160, USA heitsch@math.gatech.edu.
Christine E
CE
Heitsch
D020029:Base Pairing; D003627:Data Interpretation, Statistical; D008958:Models, Molecular; D009690:Nucleic Acid Conformation; D012329:RNA, Bacterial / Q000737:chemistry; D058727:RNA, Small Untranslated / Q000737:chemistry*; D017423:Sequence Analysis, RNA / Q000379:methods*; D014734:Vibrio cholerae / Q000235:genetics
D012329:RNA, Bacterial; D058727:RNA, Small Untranslated