Improving RNA Secondary Structure Prediction with Structure Mapping Data

Sloma, Michael F.; Mathews, David H.

doi:10.1016/bs.mie.2014.10.053

Cited by 51 publications

(76 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, because it is impossible to faithfully recapitulate in vitro all the physicochemical and enzymatic aspects of living systems, the most biologically informative structure-probing methods will be those that can be applied in vivo. As stated above, most RNAs cannot be well predicted using only thermodynamic parameters (so-called in silico methods); rather, experimental data are incorporated to restrain in silico prediction algorithms and yield-predicted structures (75,101). Figure 2 illustrates the dramatic differences that can arise when an RNA structure is predicted purely in silico versus when it is restrained by experimental information on folding derived from Structure-seq (21,22), one of the new in vivo structure-probing methods discussed in this article.…”

Section: Parameters That Affect Rna Structurementioning

confidence: 99%

Genome-Wide Analysis of RNA Secondary Structure

Bevilacqua¹,

Ritchey²,

et al. 2016

Annu. Rev. Genet.

210

157

View full text Add to dashboard Cite

Single-stranded RNA molecules fold into extraordinarily complicated secondary and tertiary structures as a result of intramolecular base pairing. In vivo, these RNA structures are not static. Instead, they are remodeled in response to changes in the prevailing physicochemical environment of the cell and as a result of intermolecular base pairing and interactions with RNAbinding proteins. Remarkable technical advances now allow us to probe RNA secondary structure at single-nucleotide resolution and genome-wide, both in vitro and in vivo. These data sets provide new glimpses into the RNA universe. Analyses of RNA structuromes in HIV, yeast, Arabidopsis, and mammalian cells and tissues have revealed regulatory effects of RNA structure on messenger RNA (mRNA) polyadenylation, splicing, translation, and turnover. Application of new methods for genome-wide identification of mRNA modifications, particularly methylation and pseudouridylation, has shown that the RNA "epitranscriptome" both influences and is influenced by RNA structure. In this review, we describe newly developed genome-wide RNA structure-probing methods and synthesize the information emerging from their application.

show abstract

Section: Parameters That Affect Rna Structurementioning

confidence: 99%

Genome-Wide Analysis of RNA Secondary Structure

Bevilacqua¹,

Ritchey²,

et al. 2016

Annu. Rev. Genet.

210

157

View full text Add to dashboard Cite

show abstract

“…While probing data measure local structural constraints, they only indirectly report base-pairing probabilities (Ochsenreiter 2015;Sloma and Mathews 2015). High reactivities tend to have greater free energy contributions compared to lower reactivities and they generally signify greater discriminatory power as can also be seen from prior data-driven likelihood ratio analysis (Bindewald et al 2011;Eddy 2014).…”

Section: Characterizing Shape Information Contentmentioning

confidence: 99%

“…This term is added to the NNTM free energy of a structure each time base i is involved in a nearest-neighbor stack. Consequently, the pseudo-energy term is counted once for a base involved in a helix-end pair, while it is counted twice for a stacked pair (Low and Weeks 2010;Sloma and Mathews 2015). This is because a helixend pair is involved in one stack, while a stacked pair is involved in two stacks.…”

Section: Data-directed Predictionsmentioning

confidence: 99%

“…Propelled by these experimental advances, prediction algorithms that incorporate probing data as soft constraints to direct predictions have recently emerged (Deigan et al 2009;Cordero et al 2012;Sükösd et al 2012;Washietl et al 2012;Zarringhalam et al 2012;Hajdin et al 2013;Ouyang et al 2013;Luntzer et al 2015;Wu et al 2015). While structure-probing data do not directly report pairing states of nucleotides (Sloma and Mathews 2015), they proved useful in improving the accuracy of MFE predictions (Deigan et al 2009;Hajdin et al 2013;Luntzer et al 2015). In a pioneering work, Mathews, Weeks, and colleagues proposed to incorporate SHAPE data in the form of pseudo-energy terms derived from a linear-log formula (Deigan et al 2009).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-directed RNA secondary structure prediction using probabilistic modeling

Deng

Ledda

Vaziri

et al. 2016

RNA

View full text Add to dashboard Cite

Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations.

show abstract

“…In one benchmark, 61.2% of pairs in predicted structures are present in accepted structures, and 68.9% of accepted pairs are in the predicted structure (Bellaousov and Mathews 2010), which is sufficient accuracy to develop testable hypotheses about the structure. Incorporation of sequence comparison information by using multiple homologous sequences (Seetin and Mathews 2012;Schirmer et al 2014) or information from experimental data (Deigan et al 2009;Cordero et al 2012;Sloma and Mathews 2015) into the structure prediction results in excellent accuracy, with up to 90% of predicted pairs being correct.…”

Section: Introductionmentioning

confidence: 99%

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures

Sloma

Mathews

2016

RNA

Self Cite

View full text Add to dashboard Cite

RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures.

show abstract

Improving RNA Secondary Structure Prediction with Structure Mapping Data

Cited by 51 publications

References 100 publications

Genome-Wide Analysis of RNA Secondary Structure

Genome-Wide Analysis of RNA Secondary Structure

Data-directed RNA secondary structure prediction using probabilistic modeling

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures

Contact Info

Product

Resources

About