Using the basic computational framework provided by Hidden Markov Models, we’ve learned how to infer the most likely set of hidden states underlying a sequence of observed characters. In particular, a combination of the forward and backward algorithms enabled one form of this inference, i.e. posterior decoding, in O(KN2) time. We also learned how either unsupervised or supervised learning can be used to identify the best parameters for an HMM when provided with an unlabelled or labelled dataset. The combination of these decoding and parameter estimation methods enable the application of HMM’s to a wide variety of problems in computational biology, of which CpG island and gene identification form a small subset. Given the flexibility and analytical power provided by HMM’s, these methods will play an important role in computational biology for the foreseeable future.