Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction

By Robin D. Dowell, Sean R. Eddy. BMC Bioinformatics, 2004

301 citations recorded by the source corpus.

BackgroundRNA secondary structure prediction methods based on probabilistic modeling can be developed using stochastic context-free grammars (SCFGs). Such methods can readily combine different sources of information that can be expressed probabilistically, such as an evolutionary model of comparative RNA sequence analysis and a biophysical model of structure plausibility. However, the number of free parameters in an integrated model for consensus RNA structure prediction can become untenable if the underlying SCFG design is too complex. Thus a key question is, what small, simple SCFG designs perform best for RNA secondary structure prediction?ResultsNine different small SCFGs were implemented to explore the tradeoffs between model complexity and prediction accuracy. Each model was tested for single sequence structure prediction accuracy on a benchmark set of RNA secondary structures.ConclusionsFour SCFG designs had prediction accuracies near the performance of current energy minimization programs. One of these designs, introduced by Knudsen and Hein in their PFOLD algorithm, has only 21 free parameters and is significantly simpler than the others.

Subfields: Astronomy and Astrophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9b41104dd6db920352c12d908ffdc4fc2865d816

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In Astronomy and Astrophysics

D. Mathews

In Molecular Biology

Citations in the corpus

New metrics for comparing and assessing discrepancies between RNA 3D structures and models. (2009, unknown): “…2002; Yang et al. 2003; Djelloul and Denise 2008), discover and analyze structural motifs (Huang et al. 2005; Lemieux and Major 2006; Lisi and Major 2007; Abraham et al. 2008; Xin et al. 2008), and formally represent RNA structures (Dowell and Eddy 2004; St-Onge et al. 2007) have been developed.”
RNAstructure: software for RNA secondary structure prediction and analysis (2010, background): “The accuracy, however, benchmarked lower when longer sequences were included [20,21].”
A comprehensive comparison of comparative RNA structure prediction approaches (2004, background): “Selectivity is also known as the "specificity" [28] and "positive predictive value" [64,65].”
RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble. (2005, unknown): “These two complementary measures have become the standards for measuring predictive accuracy (Mathews et al. 1999; Dowell and Eddy 2004; Mathews 2004).”
PicXAA-R: Efficient structural alignment of multiple RNA sequences using a greedy approach (2011, unknown): “Hence, several studies have proposed various approximations to the Sankoff algorithm [5–19].”
RNA secondary structure prediction using deep learning with thermodynamic integration (2020, unknown): “TrainSetA and TestSetA were collected from the literature (10, 15, 16, 29, 30, 31).”
ViennaRNA Package 2.0 (2011, methodology): “These numbers are then used to compute the sensitivity , also known as true positive rate (TPR), and precision , also known as positive predictive value (PPV) [105].”
CentroidFold: a web server for RNA secondary structure prediction (2009, background): “Alternative approach is based on probabilistic frameworks, including stochastic contextfree grammars (SCFGs), which can model RNA secondary structures without pseudoknots ( 3 ).”
Accurate SHAPE-directed RNA structure determination (2009, unknown): “We predicted the secondary structure of 16S rRNA by using the program RNAstructure (11), whose algorithm is among the most accurate currently available (8).”