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.
@article{arxiv.1207.1312,
title = {Quantitative DMS mapping for automated RNA secondary structure inference},
author = {Pablo Cordero and Wipapat Kladwang and Christopher C. VanLang and Rhiju Das},
journal= {arXiv preprint arXiv:1207.1312},
year = {2012}
}