Small RNAs (sRNAs) are post-transcriptional regulators of gene expression that can be found both in animal and plant cells. sRNAs have been linked to the control of many biological processes and the progression of many diseases. Furthermore, it is believed that most genes are controlled by at least one sRNA. The use of computational methods has been very important in the identification of sRNA genes and in the prediction of which genes are regulated by sRNAs. However, in animals the interactions between sRNAs and other genes is thought to be mediated by highly non-specific factors at the nucleotide sequence level. . This makes it very difficult for computational methods to accurately predict interactions between sRNAs and other genes. In this thesis we develop and use computational methods to improve the comprehension of the regulatory roles of animal sRNAs by combining nucleotide sequence and expression data of the sRN As and the genes they regulate. We develop techniques to analyse data coming from two types of large scale experiments: high-throughput sequencing of sRNAs and microarrays of messenger RNAs. With the former we profile and discover new sRNAs. With the latter we infer regulatory interactions between sRNAs and other genes.