Sharing and archiving nucleic acid structure mapping data

Rocca-Serra, Philippe; Bellaousov, Stanislav; Birmingham, Amanda; Chen, Chunxia; Cordero, Pablo; Das, Rhiju; Davis-Neulander, Lauren; Duncan, Caia D. S.; Halvorsen, Matthew; Knight, Rob; Leontis, Neocles B.; Mathews, David H.; Ritz, Justin; Stombaugh, Jesse; Weeks, Kevin M.; Zirbel, Craig L.; Laederach, Alain

doi:10.1261/rna.2753211

Cited by 30 publications

(37 citation statements)

References 86 publications

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“…Three probes were used: 1M7 (a SHAPE reagent, 1-methyl-7-nitroisatoic anhydride, which acylates 2 ′ -hydroxyls of flexible nucleotides); DMS (dimethyl sulfate, reacting with exposed N1/ N3 of adenosine/cytosine; and CMCT (1-cyclohexyl(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate, reacting with exposed N1/N3 of guanosine/uracil) ([Mortimer and Weeks 2007;Cordero et al 2012a] and references therein). Data were released to modelers on the RNA Mapping Database in standardized formats (Rocca-Serra et al 2011;Cordero et al 2012b) in accession codes RNAPZ5_ STD_0000, RNAPZ5_1M7_0002, RNAPZ5_DMS_0002; RNAPZ6_STD_0001, RNAPZ6_1M7_0002; RNAPZ10_ STD_0001, RNAPZ10_STD_0002. Each group was given the possibility to use those data and each group describes below at which stage and how these solution data were used during the modeling process.…”

Section: Additional Chemical-mapping Datamentioning

confidence: 99%

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

Miao

Adamiak

Blanchet

et al. 2015

RNA

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This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemicalmapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg .fr/rnapuzzles/.

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Section: Additional Chemical-mapping Datamentioning

confidence: 99%

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

Miao

Adamiak

Blanchet

et al. 2015

RNA

Self Cite

180

201

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“…ROC analysis is based on thresholding the SHAPE data (DeLong et al 1985), a Welch's t-test was used to determine significance over a minimum of three repeats per experiment. Raw SHAPE data were made available using the SNRNASM standard (Rocca-Serra et al 2011) and the data are available at http://snrnasm.bio.unc.edu.…”

Section: Shape Experimentsmentioning

confidence: 99%

Structural effects of linkage disequilibrium on the transcriptome

Martin¹,

Halvorsen²,

Davis-Neulander³

et al. 2011

RNA

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A majority of SNPs (single nucleotide polymorphisms) map to noncoding and intergenic regions of the genome. Noncoding SNPs are often identified in genome-wide association studies (GWAS) as strongly associated with human disease. Two such diseaseassociated SNPs in the 59 UTR of the human FTL (Ferritin Light Chain) gene are predicted to alter the ensemble of structures adopted by the mRNA. High-accuracy single nucleotide resolution chemical mapping reveals that these SNPs result in substantial changes in the structural ensemble in agreement with the computational prediction. Furthermore six rescue mutations are correctly predicted to restore the mRNA to its wild-type ensemble. Our data confirm that the FTL 59 UTR is a ''RiboSNitch,'' an RNA that changes structure if a particular disease-associated SNP is present. The structural change observed is analogous to that of a bacterial Riboswitch in that it likely regulates translation. These data further suggest that specific pairs of SNPs in high linkage disequilibrium (LD) will form RNA structure-stabilizing haplotypes (SSHs). We identified 484 SNP pairs that form SSHs in UTRs of the human genome, and in eight of the 10 SSH-containing transcripts, SNP pairs stabilize RNA protein binding sites. The ubiquitous nature of SSHs in the transcriptome suggests that certain haplotypes are conserved to avoid RiboSNitch formation.

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“…The SNRNASM standard was developed to share the results of high-resolution and throughput nucleic acid structure mapping data [58]. We identified RNAs that were probed using SHAPE chemical mapping under standard conditions (10 mM MgCl 2 and 100 mM NaCl), and where significant mutational information was available.…”

Section: Methodsmentioning

confidence: 99%

Evaluating our ability to predict the structural disruption of RNA by SNPs

2012

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The structure of RiboNucleic Acid (RNA) has the potential to be altered by a Single Nucleotide Polymorphism (SNP). Disease-associated SNPs mapping to non-coding regions of the genome that are transcribed into RiboNucleic Acid (RNA) can potentially affect cellular regulation (and cause disease) by altering the structure of the transcript. We performed a large-scale meta-analysis of Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data, which probes the structure of RNA. We found that several single point mutations exist that significantly disrupt RNA secondary structure in the five transcripts we analyzed. Thus, every RNA that is transcribed has the potential to be a “RiboSNitch;” where a SNP causes a large conformational change that alters regulatory function. Predicting the SNPs that will have the largest effect on RNA structure remains a contemporary computational challenge. We therefore benchmarked the most popular RNA structure prediction algorithms for their ability to identify mutations that maximally affect structure. We also evaluated metrics for rank ordering the extent of the structural change. Although no single algorithm/metric combination dramatically outperformed the others, small differences in AUC (Area Under the Curve) values reveal that certain approaches do provide better agreement with experiment. The experimental data we analyzed nonetheless show that multiple single point mutations exist in all RNA transcripts that significantly disrupt structure in agreement with the predictions.

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Sharing and archiving nucleic acid structure mapping data

Cited by 30 publications

References 86 publications

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

Structural effects of linkage disequilibrium on the transcriptome

Evaluating our ability to predict the structural disruption of RNA by SNPs

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