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Strong correlation between SHAPE chemistry and the generalized NMR order parameter (S2) in RNA.

ABSTRACT: The functions of most RNA molecules are critically dependent on the distinct local dynamics that characterize secondary structure and tertiary interactions and on structural changes that occur upon binding by proteins and small molecule ligands. Measurements of RNA dynamics at nucleotide resolution set the foundation for understanding the roles of individual residues in folding, catalysis, and ligand recognition. In favorable cases, local order in small RNAs can be quantitatively analyzed by NMR in terms of a generalized order parameter, S2. Alternatively, SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) chemistry measures local nucleotide flexibility in RNAs of any size using structure-sensitive reagents that acylate the 2'-hydroxyl position. In this work, we compare per-residue RNA dynamics, analyzed by both S2 and SHAPE, for three RNAs: the HIV-1 TAR element, the U1A protein binding site, and the Tetrahymena telomerase stem loop 4. We find a very strong correlation between the two measurements: nucleotides with high SHAPE reactivities consistently have low S2 values. We conclude that SHAPE chemistry quantitatively reports local nucleotide dynamics and can be used with confidence to analyze dynamics in large RNAs, RNA-protein complexes, and RNAs in vivo.

SUBMITTER: Gherghe CM

PROVIDER: S-EPMC2712629 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

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Strong correlation between SHAPE chemistry and the generalized NMR order parameter (S2) in RNA.

Gherghe Costin M CM Shajani Zahra Z Wilkinson Kevin A KA Varani Gabriele G Weeks Kevin M KM

Journal of the American Chemical Society 20080819 37

The functions of most RNA molecules are critically dependent on the distinct local dynamics that characterize secondary structure and tertiary interactions and on structural changes that occur upon binding by proteins and small molecule ligands. Measurements of RNA dynamics at nucleotide resolution set the foundation for understanding the roles of individual residues in folding, catalysis, and ligand recognition. In favorable cases, local order in small RNAs can be quantitatively analyzed by NMR ...[more]

PMID: 18710236

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Project description:Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data.

Dataset Information

Strong correlation between SHAPE chemistry and the generalized NMR order parameter (S2) in RNA.

Publications

Strong correlation between SHAPE chemistry and the generalized NMR order parameter (S2) in RNA.

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