The advances in experimental techniques are allowing the collection of large amount of biological data. The accurate analysis of the available information is important to model biological processes and reduce the costs of the experiments, performing selected computationally driven tests. Two interesting examples of this trend are the large amount of human genetic variation data from high-throughput sequencing experiments and the increasing number of macromolecular structures known at atomic level deposited in the Protein Data Bank. In this talk, I will summarize the results of my research activity during the last years. First, I will present new algorithms for the prediction of effects of single amino acid polymorphisms (SAPs), describing different methods for the prediction of the protein stability change upon single point mutation [1] and for the prediction of their effects on human health [2,3] using protein sequence and structural information. In the last part of the talk I will present a new method for RNA structural alignment [4] and its possible application in prediction of the RNA structure and function [4,5].