Understanding the errors of SHAPE-directed RNA structure modeling

Abstract

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.

Figure 1

SHAPE reactivities measured at single–nucleotide resolution for six non-coding RNAs of known structure. Black lines mark residues that are paired or unpaired in the crystallographic models with values of 0.0 or 1.0, respectively.

Figure 2

Crystallographic (left) and SHAPE-directed (right) secondary structure models for a benchmark of non-coding RNAs. SHAPE reactivities are shown as colors on bases, and match colors in Fig. 1. Cyan lines mark incorrect base pairs; orange lines mark crystallographic base pairs missing in each model; gray lines mark base pairs in regions outside crystallized construct. Helix confidence estimates from bootstrap analyses are given as red percentage values. For clarity, flanking sequences (see SI Table S1) are not shown. Figure is in two parts.

Figure 2

Crystallographic (left) and SHAPE-directed (right) secondary structure models for a benchmark of non-coding RNAs. SHAPE reactivities are shown as colors on bases, and match colors in Fig. 1. Cyan lines mark incorrect base pairs; orange lines mark crystallographic base pairs missing in each model; gray lines mark base pairs in regions outside crystallized construct. Helix confidence estimates from bootstrap analyses are given as red percentage values. For clarity, flanking sequences (see SI Table S1) are not shown. Figure is in two parts.