RNA secondary structure prediction based on SHAPE data in helix regions

Lotfi, Mohadeseh; Zare-Mirakabad, Fatemeh; Montaseri, Soheila

doi:10.1016/j.jtbi.2015.05.026

Cited by 4 publications

(3 citation statements)

References 16 publications

(17 reference statements)

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“…Importantly, this approach has been extensively validated and generally yields structure models with accuracies above 90% ( 44 , 46 , 51 , 52 ). Even in the case where there is not SHAPE data for the entirety of an RNA (as is common at the ends of transcripts), incorporating available SHAPE data still greatly improves the accuracy of structure predictions ( 53 , 54 ). As an internal control, we initially compared the structure models derived for the CDS regions of different transcripts.…”

Section: Resultsmentioning

confidence: 99%

An RNA structure-mediated, posttranscriptional model of human α-1-antitrypsin expression

Corley

Solem

Phillips

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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SignificanceProtein and mRNA expression are in most cases poorly correlated, which suggests that the posttranscriptional regulatory program of a cell is an important component of gene expression. This regulatory network is still poorly understood, including how RNA structure quantitatively contributes to translational control. We present here a series of structural and functional experiments that together allow us to derive a quantitative, structure-dependent model of translation that accurately predicts translation efficiency in reporter assays and primary human tissue for a complex and medically important protein, α-1-antitrypsin. Our model demonstrates the importance of accurate, experimentally derived RNA structural models partnered with Kozak sequence information to explain protein expression and suggests a strategy by which α-1-antitrypsin expression may be increased in diseased individuals.

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Section: Resultsmentioning

confidence: 99%

An RNA structure-mediated, posttranscriptional model of human α-1-antitrypsin expression

Corley

Solem

Phillips

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…SHAPE probing of RNA secondary structures is similarly enhanced by integration with eCLIP. SHAPE-eCLIP could aid discovery of the structural motifs preferred by any RBP of interest (Dominguez et al, 2018;Kazan et al, 2010;Maticzka et al, 2014;Pan et al, 2018), given that RNA structure predictions are vastly improved by incorporating structure probing data (Deigan et al, 2009;Lotfi et al, 2015;Low and Weeks, 2010;Ramachandran et al, 2013). However, we note that in vivo structure probing signal is convoluted by bases that form hydrogen bonds with protein and thus appear to be base paired (with RNA) to the naive observer.…”

Section: Discussionmentioning

confidence: 98%

Footprinting SHAPE-eCLIP Reveals Transcriptome-wide Hydrogen Bonds at RNA-Protein Interfaces

et al. 2020

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“…These techniques have been utilized to learn the rules describing mutations that affect protein behavior, and use them to infer new relevant mutations that will be resistant to certain drugs [ 9 ]. Another use is to predict the potential secondary structure formation based on primary structure sequences [ 10 – 12 ]. A different direction is to predict the discovery of single nucleotide variants in RNA sequence.…”

Section: Introductionmentioning

confidence: 99%

The prediction of virus mutation using neural networks and rough set techniques

Hassanien

Ahmad

2016

J Bioinform Sys Biology

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Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.

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RNA secondary structure prediction based on SHAPE data in helix regions

Cited by 4 publications

References 16 publications

An RNA structure-mediated, posttranscriptional model of human α-1-antitrypsin expression

An RNA structure-mediated, posttranscriptional model of human α-1-antitrypsin expression

Footprinting SHAPE-eCLIP Reveals Transcriptome-wide Hydrogen Bonds at RNA-Protein Interfaces

The prediction of virus mutation using neural networks and rough set techniques

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