Emily Rogers’s research while affiliated with Georgia Institute of Technology and other places

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Publications (4)


Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations
  • Article

June 2017

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26 Reads

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6 Citations

Biophysical Journal

Emily Rogers

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Christine Heitsch

Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neighbor thermodynamic model remains a fundamental challenge in the field. Minimum free energy (MFE) predictions are known to be “ill conditioned” in that small changes to the thermodynamic model can result in significantly different optimal structures. Hence, the best practice is now to sample from the Boltzmann distribution, which generates a set of suboptimal structures. Although the structural signal of this Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under thermodynamic perturbations have yet to be addressed. We present here a mathematically rigorous model for conditioning inspired by numerical analysis, and also a biologically inspired definition for robustness under thermodynamic perturbation. We demonstrate the strong correlation between conditioning and robustness and use its tight relationship to define quantitative thresholds for well versus ill conditioning. These resulting thresholds demonstrate that the majority of the sequences are at least sample robust, which verifies the assumption of sampling’s improved conditioning over the MFE prediction. Furthermore, because we find no correlation between conditioning and MFE accuracy, the presence of both well- and ill-conditioned sequences indicates the continued need for both thermodynamic model refinements and alternate RNA structure prediction methods beyond the physics-based ones.


New insights from cluster analysis methods for RNA secondary structure prediction

March 2016

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30 Reads

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15 Citations

WIREs RNA

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 This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA


SUPPLEMENTARY DATA
  • Data
  • File available

November 2014

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15 Reads

Download

Figure 1. Predicted MFE structure for VcQrr3 with the conserved region (20-51 of 107 nucleotides) shown in bold. VcQrr2 has a comparable fourarmed MFE prediction while VcQrr4 has an additional helix forming a 'cumberbun' across the middle. VcQrr1 has the common first and last helices, but different base pairings forming a single middle arm. 
Figure 2. Dot plot of base pair probabilities for VcQrr3. Dot size at (x, y) corresponds to log probability of position x pairing with y. Dashed lines indicate the conserved region on each axis. While the first and fourth MFE helices are highly probable, the rest of the sequence-including the majority of the conserved region-has significant suboptimal structural alternatives, as well as many low-frequency pairings. 
Figure 3. Three structures from a Boltzmann sample for VcQrr3 generated by GTfold (43) with conserved nucleotides 20-51 in bold. Commonalities are highlighted by colored rectangles. Significant differences include pairing 29-31 with 69-71 to form a multiloop in s 1 versus with 43-45 in s 2 and s 3 to form a stem extension (yellow). In s 1 and s 2 , 48-50 are paired with 61-63 forming part of a hairpin stem-loop (purple) but are single-stranded in s 3 . 
Figure 5. VcQrr3 summary profile graph. Boxes indicate selected profiles, and dashed ovals the intersection ones. Each node is labeled with the profile, in parenthetic notation, along with its specific and general frequencies, written as a ratio. An edge from q to q is labeled with the feature(s) from q \q. Similarities between profiles are given by the greatest lower bound, aka 'last common ancestor,' with differences read from edge labels. The root is always the (possibly empty) profile common to all sampled structures. Features are listed by maximal helix with frequency. For illustrative purposes, the secondary structures from Figures 1 and 3, with features highlighted in color, are shown with their selected profile. 
Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble

November 2014

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133 Reads

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32 Citations

Nucleic Acids Research

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.

Citations (3)


... The most popular programs rely on minimum free energy (MFE) calculations, for which the accuracy of predicted base pairs has been reported to be as low as 40% for RNAs longer than 500 nt (Doshi et al., 2004;Lorenz et al., 2016). This is mainly due to an incomplete understanding of the thermodynamic parameters that govern RNA molecular interactions and stability; such that any small change to the thermodynamic models that underpin MFE calculations, can generate significantly different optimal structures (Rogers et al., 2017;Schroeder, 2018). This becomes particularly relevant in the case of longer RNAs where the number of possible folds is enormous, and many possible optimal structures can be generated with no significant differences in their MFE scores. ...

Reference:

Discovering functional motifs in long noncoding RNAs
Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations
  • Citing Article
  • June 2017

Biophysical Journal

... When introduced [4], it was established that RNAprofiling provides complementary information to both Sfold and RNAshapes. Moreover, a thorough analysis [9] compared the three, where Pv1 analyzed Boltzmann samples generated by GTfold [10]. It was found that all three improved over the MFE, but there was no clear advantage among cluster analysis methods in terms of base pair prediction accuracy. ...

New insights from cluster analysis methods for RNA secondary structure prediction
  • Citing Article
  • March 2016

WIREs RNA

... As with other probing methods, including SHAPE, lead cleavage does not unambiguously distinguish between paired and unpaired positions but provides quantitative evidence that can be converted into a probability that a nucleotide is unpaired. We emphasize that this is not a methodological shortcoming but an inevitable consequence of the fact that RNAs form a free-energy weighted ensemble of structures rather than a single, unambiguous secondary structure (8,69,70). Indeed, recently methods have become available that deconvolve multiple representative structures from a probing signal (70)(71)(72). The 'known' reference structures are therefore necessarily approximations rather than a perfect gold standard. ...

Profiling small RNA reveals multimodal substructural signals in a Boltzmann ensemble

Nucleic Acids Research