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-cyclohexyl(2-morpholinoethyl) carbodiimide metho-p-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.
Department of Biomedical Informatics, Stanford University, Stanford, CA 94305, USA.
Pablo
P
Cordero
Wipapat
W
Kladwang
Christopher C
CC
VanLang
Rhiju
R
Das
D000215:Acylation; D001331:Automation; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D009690:Nucleic Acid Conformation / Q000187:drug effects*; D012329:RNA, Bacterial / Q000737:chemistry* / Q000235:genetics; D013463:Sulfuric Acid Esters / Q000494:pharmacology*
D012329:RNA, Bacterial; D013463:Sulfuric Acid Esters; C007482:dimethyl sulfate