Next-Generation Sequencing (NGS) technologies have opened new perspectives to refine the process of predicting the secondary structure(s) of structured non-coding RNAs. Herein, we describe an integrated modeling strategy, based on the SHAPE chemistry, to infer structural insight from deep se-quencing data. Our approach is based on a pseudo-energy minimization, incorporating additional information from evolutionary data (compensatory mutations) and SHAPE experiments (reactivity scores) within an iterative procedure. Preliminary results reveal conserved and stable structures within UTRs of the Ebola Genome, that are both thermodynamically-stable and highly supported by SHAPE accessibility analysis.