Modeling Biological Sequences using Hidden Markov Models
The hidden Markov models are applied in different biological sequence analysis. For example, hidden Markov models have been used for predicting genes. If we have a new sequence and we want to identify the genes inside that sequence, we can use hidden Markov models to annotate the regions first and then identify the location of a gene. Hidden Markov models can provide very good genetic models for performing different dynamic alignment algorithms for pairwise as well as multiple sequence alignment. We can also use hidden Markov models for building a profile for a sequence family, base calling, determining DNA sequencing errors, prediction of the secondary structure of protein, identifying copy number variations and many more. In other words, hidden Markov models are very important mathematical models that have been applied in various biological sequence analysis.
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