Protein-RNA interactions play vital roles in many cellular processes, and as a result are the main focus of many biological studies. Biologists would like to efficiently measure protein-RNA interactions in high-throughput, and based on these high-throughput experimental measurements train accurate machine-learning models to predict interactions to new RNA sequences. In the talk, I will present solutions to both challenges: design of efficient high-throughput experiments, and training highly accurate predictive models on high-throughput genomic data. First, I will present DeCoDe, a new method based on Integer Linear Programming to design protein-coding templates to efficiently cover many proteins in a single high-throughput experiment. DeCoDe outperforms extant methods for the task, and newly enables features that were not possible before, such as covering variable-length proteins and optimizing globally over multiple templates. Second, I will present DeepUTR, a new method based on Deep Learning to predict mRNA degradation dynamics based on the 3’-UTR sequence of an mRNA. DeepUTR outperforms extant methods for the task, and newly enables prediction of mRNA levels at various time points. Moreover, we extended the Integrated Gradients interpretability approach to handle multiple input types, and using the extended approach discovered known and novel regulatory 3’-UTR elements associated with mRNA degradation. I will conclude my talk with future plans on both sequence design problems, and deep neural networks applications in genomics.