Quantitative DMS mapping for automated RNA secondary structure inference

TL;DR

This paper demonstrates that DMS chemical probing data can be effectively integrated into automated RNA secondary structure inference, achieving high accuracy comparable to SHAPE data and establishing DMS as a quantitative tool for unbiased RNA modeling.

Contribution

The study introduces a pseudo-energy framework for incorporating DMS data into automated RNA structure prediction, with validation on multiple RNAs showing high accuracy and confidence estimation.

Findings

DMS-guided modeling achieves 9.5% false negatives.

DMS-guided modeling achieves 11.6% false discoveries.

Integrating DMS and SHAPE data improves accuracy slightly.

Abstract

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.