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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Colly Mitchell - Events Manager
Summary Sentence: "RNA Profiling: Extracting Structural Signals from Noisy Distributions" - Christine Heitsch, Ph.D. - Georgia Tech
Full Summary: No summary paragraph submitted.
Petit Institute Breakfast Club Seminar Series"RNA Profiling: Extracting Structural Signals from Noisy Distributions"
Christine Heitsch, Ph.D.
Professor, School of Mathematics
Georgia Tech
Accurate RNA structural prediction remains challenging, despite its increasing biomedical importance. Sampling secondary structures from the Gibbs distribution yields a strong signal of high probability base pairs. However, identifying higher order substructures requires further analysis. Profiling (Rogers & Heitsch, NAR, 2014) is a novel method which identifies the most probable combinations of base pairs across the Boltzmann ensemble. This combinatorial approach is straightforward, stable, and clearly separates structural signal from thermodynamic noise. Moreover, it can be extended to predict consensus stems for an RNA family with high accuracy via unsupervised clustering of unaligned homologous sequences.
