Hidden Markov Models p HMM too Markov Chain

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Hidden Markov Models p. HMM too!

Hidden Markov Models p. HMM too!

Markov Chain, Markov Model • Markov Chain: describes a meaningful process that can be

Markov Chain, Markov Model • Markov Chain: describes a meaningful process that can be in any one of a number of states at any given time, Markov Chain: Jeff plays guitar. Markov Model: noun verb noun

Markov Model: noun verb noun Hidden Markov Model. . . probabilistic model of a

Markov Model: noun verb noun Hidden Markov Model. . . probabilistic model of a group of Markov Chains, Markov Chain(1): Markov Chain(2): Markov Chain(3): Hidden Markov Model: Jeff plays guitar. Jeff plays flamenco guitar poorly. (noun) (verb) (adjective) (noun) (adverb)

Profile Hidden Markov Model • A Hidden Markov Model built to describe a certain

Profile Hidden Markov Model • A Hidden Markov Model built to describe a certain set of Markov Chains, that is used to identify additional, related chains. . . Hidden Markov Model: p(Sentence) = p(Words in sentence)|(n)(v)(a)(n)(a) x p(Word order)(n)(v)(a)(n)(a)

GC Rich vs. Normal NNNNNRRRRNNNNNNNNNRRRRRRRNNNN TTACTTGACGCCAGAAATGTATATTTGGTAACCCGACGCTAA 60% AT, 40%GC Are the red regions really

GC Rich vs. Normal NNNNNRRRRNNNNNNNNNRRRRRRRNNNN TTACTTGACGCCAGAAATGTATATTTGGTAACCCGACGCTAA 60% AT, 40%GC Are the red regions really GC-rich (R)?

Training Based on real data, the relative frequencies observed for base identification given genomic

Training Based on real data, the relative frequencies observed for base identification given genomic location (state probablilties). . Based on real data, the probability of “entering”, or leaving a GC-rich or normal state… N - N (0. 9) C - C (0. 8) N - R (0. 1) C - R (0. 2)

HMM p(ACGC) N A 0. 3 T 0. 3 G C N A 0.

HMM p(ACGC) N A 0. 3 T 0. 3 G C N A 0. 3 T 0. 3 0. 2 G 0. 2 C 0. 9 0. 2 0. 1 R N A 0. 3 T 0. 3 0. 2 G 0. 2 C 0. 2 0. 9 0. 2 0. 1 R R A 0. 1 T 0. 1 G 0. 4 C 0. 4 0. 8 p(ACGC|NNNN)p(NNNN) = 0. 00175 0. 8 C 0. 4 p(ACGC|NRRR)p(NRRR) = 0. 00123

HMM p(ACGCC) N A 0. 3 T 0. 3 G C N A 0.

HMM p(ACGCC) N A 0. 3 T 0. 3 G C N A 0. 3 T 0. 3 0. 2 G 0. 2 C 0. 9 0. 2 0. 1 R N A 0. 3 T 0. 3 0. 2 G 0. 2 C 0. 2 0. 9 0. 2 0. 1 R A 0. 1 T 0. 1 G 0. 4 C 0. 4 0. 8 p(ACGCC|NNNNN)p(NNNNN) = 0. 000315 0. 8 C 0. 4 p(ACGC|NRRRR)p(NRRRR) = 0. 0004

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HMM-Others

HMM-Others

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