Global or local? Predicting secondary structure and accessibility in mRNAs

Lange, Sita J.; Maticzka, Daniel; Möhl, Mathias; Gagnon, Joshua N.; Brown, Chris M.; Backofen, Rolf

doi:10.1093/nar/gks181

Cited by 140 publications

(152 citation statements)

References 53 publications

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“…Our prediction used overlapping windows with a window size of L nucleotides and a step size of S = 1 nucleotide (Lange et al 2012). The folding strength of a window is defined by −ΔG, the negative of the minimum free energy of the folded RNA.…”

Section: Computational Prediction Of Nascent Rna Folding Strengthmentioning

confidence: 99%

Nascent RNA folding mitigates transcription-associated mutagenesis

2015

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Transcription is mutagenic, in part because the R-loop formed by the binding of the nascent RNA with its DNA template exposes the nontemplate DNA strand to mutagens and primes unscheduled error-prone DNA synthesis. We hypothesize that strong folding of nascent RNA weakens R-loops and hence decreases mutagenesis. By a yeast forward mutation assay, we show that strengthening RNA folding and reducing R-loop formation by synonymous changes in a reporter gene can lower mutation rate by >80%. This effect is diminished after the overexpression of the gene encoding RNase H1 that degrades the RNA in a DNA-RNA hybrid, indicating that the effect is R-loop-dependent. Analysis of genomic data of yeast mutation accumulation lines and human neutral polymorphisms confirms the generality of these findings. This mechanism for local protection of genome integrity is of special importance to highly expressed genes because of their frequent transcription and strong RNA folding, the latter also improves translational fidelity. As a result, strengthening RNA folding simultaneously curtails genotypic and phenotypic mutations.

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Section: Computational Prediction Of Nascent Rna Folding Strengthmentioning

confidence: 99%

Nascent RNA folding mitigates transcription-associated mutagenesis

2015

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“…We have determined the secondary structure of all RefSeq transcripts to predict single-stranded regions using RNAfold [24], while others have used RNA structure predictors (RNAplfold in Refs. [25,26]) in a pooling predictor using machine learning [27]. Additionally, nucleotide solvent-accessibility in RNA structures could be estimated by the neural network method of Singh [22] using models of window size 3 nt, which could be expanded to 5-9 nt windows for k length.…”

Section: Words That Are Solvent-accessiblementioning

confidence: 99%

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

Seffens¹

2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

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Resilience is a network property of systems responding under stress, which for biomedicine correlates to chronic or acute insults. Current need exists for models and algorithms to study whole transcriptome differences between tissues and disease states to understand resilience. Goal of this effort is to interpret cellular transcription in a dynamic system biology framework of RNA molecules forming an information structure with regulatory properties acting on individual transcripts. We develop and evaluate a bioinformatics framework based on information theory that utilizes RNA expression data to create a whole transcriptome model of interaction that could lead to the discovery of new biological control mechanisms. This addresses a fundamental question as to why transcription yields such a small fraction of protein products. We focus on a transformative concept that individual transcripts collectively form an "information cloud" of sequence words, which for some genes may have significant regulatory impact. Extending the concept of cis-and trans-regulation, we propose to search for RNAs that are modulated by interactions with the transcriptome cloud and calling such examples nebula regulation. This framework has implications as a paradigm change for RNA regulation and provides a deeper understanding of nucleotide sequence structure and -omic language meaning.

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“…http://dx.doi.org/10.1101/220483 doi: bioRxiv preprint first posted online Nov. 16, 2017; requires information that extends beyond the MBS and which involves the whole mRNA 349 sequence, it is particularly well suited for use as a posteriori filter in miRAW. robustness, we computed local site accessibility following the guidelines defined in [37] 354 and [36]. Specifically, we used the ViennaRNA package [26] and considered the 200nt 355 surrounding the target rather than folding the whole mRNA sequence as this may result 356 in less accurate and more complex secondary structures [36].…”

Section: /29mentioning

confidence: 99%

miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

Plà

Zhong

Rayner

2017

Preprint

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MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA "seed region" (nt 2 to 8) is required for functional targeting, but typically only identify 80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed.We present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3'UTR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process.We collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain 20,000 validated miRNA:gene exact target sites. Using this data, we implemented and trained a deep neural network -composed of autoencoders and a feed-forward network -able to automatically learn features describing miRNA-mRNA interactions and assess functionality. Predictions were then refined using information such as site location or site accessibility energy.In a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality.Data and source code available at: https://bitbucket.org/account/user/bipous/projects/MIRAW Author summary microRNAs are small RNA molecules that regulate biological processes by binding to the 3'UTR of a gene and their dysregulation is associated with several diseases. Computationally predicting these targets remains a challenge as they only partially match their target and so there can be hundreds of targets for a single microRNA.Current tools assume that most of the knowledge defining a microRNA-gene interaction can be captured by analysing the binding produced in the seed region (≈ the first 8nt in the miRNA). However, recent studies show that the whole microRNA can be important and form non-canonical targets. Here, we use a target prediction methodology miRAW: deep learning for miRNA target prediction -Pla et al.

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Global or local? Predicting secondary structure and accessibility in mRNAs

Cited by 140 publications

References 53 publications

Nascent RNA folding mitigates transcription-associated mutagenesis

Nascent RNA folding mitigates transcription-associated mutagenesis

Models of RNA Interaction from Experimental Datasets: Framework of Resilience

miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

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