Single-nucleotide-resolution chemical mapping is a classic approach to characterizing structured RNA molecules that is being advanced by new chemistries, faster readouts, and coupling to computational algorithms. Recent tests have suggested that 2'-OH acylation data (SHAPE) can give near-zero error rates (0-4\%) in modeling RNA secondary structure. Here, we benchmark the method on six RNAs for which crystallographic data are available: tRNAphe and 5S rRNA from E. 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 RNAs gave significant errors (  false negative rate of 24 \%; false discovery rate of 24\%)and in two cases, worse models than secondary structure predictions without data . Variations of data processing or modeling do not mitigate these errors. Instead, as evaluated by bootstrapping, the information content of SHAPE data appears insufficient to define these RNAs' structures. Thus, SHAPE-directed RNA modeling is not always accurate, and helix-by-helix confidence estimates, as described herein, may be critical for interpreting results from this powerful methodology.
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 suggested that 2'-OH acylation data (SHAPE) can give near-zero error rates (0-4\%) in modeling RNA secondary structure. Here, we benchmark the method on six RNAs for which crystallographic data are available: tRNAphe and 5S rRNA from E. 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 RNAs gave significant errors ( overall false negative rate of 17 \%; false discovery rate of 21\%), with at least one error in five of the six cases . Variations of data processing or modeling do not mitigate these errors. Instead, as evaluated by bootstrapping, the information content of SHAPE data appears insufficient to define these RNAs' structures. Thus, SHAPE-directed RNA modeling is not always accurate, and helix-by-helix confidence estimates, as described herein, may be critical for interpreting results from this powerful methodology.
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