Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs

Sloma, Michael F.; Mathews, David H.

doi:10.1371/journal.pcbi.1005827

Cited by 28 publications

(27 citation statements)

References 70 publications

(88 reference statements)

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“…Because correct folding is crucial for the function of CDEs, we have calculated the folding probability of all predicted CDEs. These calculations used an exact calculation of the estimated thermodynamics and expanded on prior work that calculated loop probabilities (see Supplementary Methods) (52). Known CDEs, e.g.…”

Section: Resultsmentioning

confidence: 99%

Identification of new high affinity targets for Roquin based on structural conservation

Braun

Fischer

et al. 2018

Nucleic Acids Research

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Post-transcriptional gene regulation controls the amount of protein produced from a specific mRNA by altering both its decay and translation rates. Such regulation is primarily achieved by the interaction of trans-acting factors with cis-regulatory elements in the untranslated regions (UTRs) of mRNAs. These interactions are guided either by sequence- or structure-based recognition. Similar to sequence conservation, the evolutionary conservation of a UTR’s structure thus reflects its functional importance. We used such structural conservation to identify previously unknown cis-regulatory elements. Using the RNA folding program Dynalign, we scanned all UTRs of humans and mice for conserved structures. Characterizing a subset of putative conserved structures revealed a binding site of the RNA-binding protein Roquin. Detailed functional characterization in vivo enabled us to redefine the binding preferences of Roquin and identify new target genes. Many of these new targets are unrelated to the established role of Roquin in inflammation and immune responses and thus highlight additional, unstudied cellular functions of this important repressor. Moreover, the expression of several Roquin targets is highly cell-type-specific. In consequence, these targets are difficult to detect using methods dependent on mRNA abundance, yet easily detectable with our unbiased strategy.

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Section: Resultsmentioning

confidence: 99%

Identification of new high affinity targets for Roquin based on structural conservation

Braun

Fischer

et al. 2018

Nucleic Acids Research

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“…We compared SPOT-RNA with 12 best available predictors. We downloaded the stand-alone version of mxfold 33 (available at https://github.com/keio-bioinformatics/mxfold), ContextFold 16 (available at https://www.cs.bgu.ac.il/negevcb/contextfold/), CONTRAfold 14 (available at http://contra.stanford.edu/contrafold/), Knotty 24 (available at https://github.com/HosnaJabbari/Knotty), IPknot 23 (available at http://rtips.dna.bio.keio.ac.jp/ipknot/), RNAfold 11 (available at https://www.tbi.univie.ac.at/RNA/), ProbKnot 22 (available at http://rna.urmc.rochester.edu/RNAstructure.html), CentroidFold 15 (available at https://github.com/satoken/centroid-rna-package), RNAstructure 12 (available at http://rna.urmc.rochester.edu/RNAstructure.html), RNAshapes 13 (available at https://bibiserv.cebitec.uni-bielefeld.de/rnashapes), pkiss 13 (available at https://bibiserv.cebitec.uni-bielefeld.de/pkiss), and CycleFold 27 (available at http://rna.urmc.rochester.edu/RNAstructure.html). In most of the cases, we used default parameters for secondary-structure prediction except for pkiss.…”

Section: Methodsmentioning

confidence: 99%

“…These base pairs include lone (unstacked), pseudoknotted (non-nested), and noncanonical (not A–U, G–C, and G–U) base pairs as well as triplet interactions 19,20 . While some methods can predict RNA secondary structures with pseudoknots (e.g., pknotsRG 21 , Probknot 22 , IPknot 23 , and Knotty 24 ) and others can predict noncanonical base pairs (e.g., MC-Fold 25 , MC-Fold-DP 26 , and CycleFold 27 ), none of them can provide a computational prediction for both, not to mention lone base pairs and base triplets.…”

Section: Introductionmentioning

confidence: 99%

RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

et al. 2019

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The majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only 250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of 10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.

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“…Next, software tools were developed to automate the identification of non-canonical pairs from atomic coordinates of structures [29,30]. Finally, scoring functions and algorithms were developed to predict extended secondary structures from sequence [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

How to benchmark RNA secondary structure prediction accuracy

Mathews

2019

Methods

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RNA secondary structure prediction is widely used. As new methods are developed, these are often benchmarked for accuracy against existing methods. This review discusses good practices for performing these benchmarks, including the choice of benchmarking structures, metrics to quantify accuracy, the importance of allowing flexibility for pairs in the accepted structure, and the importance of statistical testing for significance. Keywords Comparative Sequence Analysis; RNA Folding RNA structure is hierarchical, from primary, to secondary, to tertiary, and to quaternary structure [9]. The primary structure is the covalent structure, i.e. the sequence of nucleotides. The secondary structure is the set of canonical base pairs, including A-U, G-C, and G-U pairs. These base pairs are organized in A-form helices, which flank nucleotides that are said to be in loops. The tertiary structure is the three-dimensional positions of the atoms in space, which demonstrate the additional intramolecular contacts beyond canonical base pairing. These contacts include non-canonical base pairs, stacks, and base-backbone interactions.

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Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs

Cited by 28 publications

References 70 publications

Identification of new high affinity targets for Roquin based on structural conservation

Identification of new high affinity targets for Roquin based on structural conservation

RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning

How to benchmark RNA secondary structure prediction accuracy

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