Hidden Markov Models


A hidden Markov model (HMM) is a statistical approach that is frequently used for modelling biological sequences. In applying it, a sequence is modelled as an output of a discrete stochastic process, which progresses through a series of states that are ‘hidden’ from the observer. Each such hidden state emits a symbol representing an elementary unit of the modelled data, for example, in case of a protein sequence – an amino acid. The parameters of a hidden Markov model can be estimated by learning from training data. Efficient algorithms are available to infer the most likely paths of states for given sequence data, which often lead to biological predictions and interpretations. Thanks to the well developed theories and algorithms, hidden Markov models have found wide applications in diverse areas of computational molecular biology.

Key Concepts:

  • Hidden Markov model is a statistical approach for modelling sequences with broad applications in computational biology.

  • In an HMM, a biological sequence is modelled as being generated by a stochastic process moving from one state to the next state, where each state emits one element of the sequence according to some emission probability distribution which, in general, is different in different states.

  • Training of an HMM is a process in which the parameters of the model are computed based on a training set of representative examples.

  • Overfitting/overtraining the model occurs when model parameters correctly represent the training set but the model cannot generalise the training data to a larger set.

  • Gene finding is a process of computational identification of genes, including exon/intron structure, in a genome.

Keywords: HMM; gene finding; profile HMM; training HMM; protein structure prediction; epigenetics; phylogenetics

Figure 1.

A simple hidden Markov model. The boxes correspond to states where the emission probabilities for each state are given inside each box. The transition probabilities are given above the corresponding arrows. Note that there are two state paths that can be used to generate the sequence GAGCGCT: 0,1,2,4,4,4,4,5,6 and 0,1,2,3,3,3,3,5,6. The probability of generating the sequence using the first path is 1.06×10–4 and using the second path is 1.35×10−7. The probability of generating the sequences by the model is the sum of these probabilities.

Figure 2.

Topology of a simple HMM for prokaryotic gene recognition. In practice, the topology is more complex (e.g. Krogh et al., ; Henderson et al., ).

Figure 3.

Topology of a profile HMM for a sequence family. The states labelled with M correspond to matches, the states labelled with I correspond to insertions and (silent) circle states correspond to deletions. Adopted with permission from Durbin et al., .

Figure 4.

Two related HMMs modelling two different biological processes. (a) Topology of an HMM for recognising of regions with epigenetic makers. The simple HMM model consist with the states: enriched region and background regions. (b) Topology of a phylogenetic HMM (Siepel et al., ) for the prediction of conserved genomic elements. The states labelled with C and N corresponding to conserved and nonconserved regions respectively. The block of input alignment in the box illustrates a conserved region and the corresponding alignment columns are assumed to be emitted by state C.



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Further Reading

Clote P and Backofen R (2000) Computational Molecular Biology: An Introduction. Chichester, UK: John Wiley & Sons.

Durbin R, Eddy SR, Krogh A and Mitchison G (1998) Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge, UK: Cambridge University Press.

Rasmus N (2005) Statistical Methods in Molecular Evolution. New York: Springer Verlag.

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Przytycka, Teresa M, and Zheng, Jie(May 2011) Hidden Markov Models. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0005267.pub2]