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Hidden Markov Models for CpG Island Detection at Tamkang University

Explore methods using Hidden Markov Models to identify CpG islands in genomic sequences. Learn about state path probabilities, Viterbi algorithm, and calculation of posterior state probabilities.

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Hidden Markov Models for CpG Island Detection at Tamkang University

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  1. Hidden Markov models Tamkang University Chichang Jou

  2. Questions • Given a short stretch of genomic sequence, how to decide whether it is from a CpG island? • Section 3.1 • Given a long sequence, how do we find the CpG islands in it? • Section 3.2 • Windows of 100 unsatisfactory if CpG islands have sharp boundaries and are of variable lengths

  3. the state sequence is hidden

  4. Questions • Given an HMM and a sequence, what is the most • probable state path • Given an HMM, how likely is a sequence, • what is the overall probability of all the probable • path for this sequence • Given an HMM and a sequence, what is the most • probable state for the i-th position of the sequence • (posterior state probability) • Given an HMM without probability parameters and a • set of sequences, how to estimate the parameters

  5. The value may be very small. Veterbi is normally done in log space

  6. Posterior State Probabilities: by backward algorithms • First calculate the probability of producing the sequence with the i-th symbol • in state k bk(i) fk(i)

  7. Posterior Decoding

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