Analysis of omics experiments usually identifies lists of molecular entities (genes, transcripts, other RNA molecules, proteins, or metabolites) that need to be functionally interpreted, not as individual molecules but as ensembles that are functionally related. Multiple computational methods have been developed to study the functions or pathways that are more relevant in those lists of biomolecules. In this chapter, we describe the principles behind these methods and discuss their use in the functional interpretation of omics data. First, we describe the two main approaches to identify the most relevant functional annotations in a list of biomolecules: Over-Representation Analysis (ORA) and Functional Class Scoring (FCS). Afterward, we introduce basic concepts of network biology and discuss how network analysis methods, such as Centrality Analysis, Network Clustering, and Network diffusion, can be used to potentiate the functional interpretation of omics data.