A high-throughput approach to profile RNA structure

Delli Ponti, Riccardo; Marti, Stefanie; Armaos, Alexandros; Tartaglia, Gian Gaetano

We used RNAstructure with the Fold module and the minimum free energy flag to predict the best RNA secondary structure of each RNA sequence (17, 18) . To mimic experimental constraints in the RNAstructure algorithm, CROSS Global scores were normalized to lie in the range of SHAPE reactivities: first the scores were multiplied by −1, then linearly mapped to [0,1]. Scores >0.65 were then assigned a SHAPE reactivity of 1; scores <0.35 were assigned a reactivity of 0; scores >0.35 and <0.65 were linearly mapped to (0,1). We used the Partition and Probability Plot (with -text flag) modules of RNAstructure to compute the AUC based on the probabilities (17, 18) . We employed the package Scorer to calculate the positive predictive values (PPVs) and true positive rates (TPRs) for the specific structures.