6 research outputs found
Understanding the errors of SHAPE-directed RNA structure modeling
Single-nucleotide-resolution chemical mapping for structured RNA is being
rapidly advanced by new chemistries, faster readouts, and coupling to
computational algorithms. Recent tests have shown that selective 2'-hydroxyl
acylation by primer extension (SHAPE) can give near-zero error rates (0-2%) in
modeling the helices of RNA secondary structure. Here, we benchmark the method
using six molecules for which crystallographic data are available: tRNA(phe)
and 5S rRNA from Escherichia coli, the P4-P6 domain of the Tetrahymena group I
ribozyme, and ligand-bound domains from riboswitches for adenine, cyclic
di-GMP, and glycine. SHAPE-directed modeling of these highly structured RNAs
gave an overall false negative rate (FNR) of 17% and a false discovery rate
(FDR) of 21%, with at least one helix prediction error in five of the six
cases. Extensive variations of data processing, normalization, and modeling
parameters did not significantly mitigate modeling errors. Only one varation,
filtering out data collected with deoxyinosine triphosphate during primer
extension, gave a modest improvement (FNR = 12%, and FDR = 14%). The residual
structure modeling errors are explained by the insufficient information content
of these RNAs' SHAPE data, as evaluated by a nonparametric bootstrapping
analysis. Beyond these benchmark cases, bootstrapping suggests a low level of
confidence (<50%) in the majority of helices in a previously proposed
SHAPE-directed model for the HIV-1 RNA genome. Thus, SHAPE-directed RNA
modeling is not always unambiguous, and helix-by-helix confidence estimates, as
described herein, may be critical for interpreting results from this powerful
methodology.Comment: Biochemistry, Article ASAP (Aug. 15, 2011
Quantitative Dimethyl Sulfate Mapping for Automated RNA Secondary Structure Inference
For decades, dimethyl sulfate (DMS) mapping has informed
manual
modeling of RNA structure in vitro and in vivo. Here, we incorporate
DMS data into automated secondary structure inference using an energy
minimization framework developed for 2′-OH acylation (SHAPE)
mapping. On six noncoding RNAs with crystallographic models, DMS-guided
modeling achieves overall false negative and false discovery rates
of 9.5% and 11.6%, respectively, comparable to or better than those
of SHAPE-guided modeling, and bootstrapping provides straightforward
confidence estimates. Integrating DMS–SHAPE data and including
1-cyclohexylÂ(2-morpholinoethyl) carbodiimide metho-<i>p</i>-toluene sulfonate (CMCT) reactivities provide small additional improvements.
These results establish DMS mapping, an already routine technique,
as a quantitative tool for unbiased RNA secondary structure modeling