  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 a pseudo-energy framework
developed for 2'-OH acylation (SHAPE) mapping. On six non-coding RNAs with
crystallographic models, DMS- guided modeling achieves overall false negative
and false discovery rates of 9.5% and 11.6%, comparable or better than
SHAPE-guided modeling; and non-parametric bootstrapping provides
straightforward confidence estimates. Integrating DMS/SHAPE data and including
CMCT reactivities give small additional improvements. These results establish
DMS mapping - an already routine technique - as a quantitative tool for
unbiased RNA structure modeling.
