Markov_Chains.ppt
- 2. 2 Markov Chains A Markov process is a stochastic process (random process) in which the probability distribution of the current state is conditionally independent of the path of past states, a characteristic called the Markov property. Markov chain is a discrete-time stochastic process with the Markov property I will use a gene finding example (to be exactly, CpG islands identification) to show Markov chains, since it is a simple and well-studied case. The same approach can be used to other problems.
- 3. 3 Markov Chains The CG island is a short stretch of DNA in which the frequency of the CG sequence is higher than other regions. It is also called the CpG island, where "p" simply indicates that "C" and "G" are connected by a phosphodiester bond. Whenever the dinucleotide CpG occurs, the C nucleotide is typically chemically modified by methylation. C of CpG is methylated into methyl-C. methyl-C mutates into T relatively easily.
- 4. 4 Markov Chains Thus, in general, CpG dinuclueotides are rarer in the genome. F (CpG) < f(C) * f(G). Methylation process is supressed before the “starting point” of many genes. These regions (CpG islands) have more CpG than elsewhere. Usually, CpG islands are a few hundred to a few thousand bases long. Identification of CpG islands is important for gene finding.
- 6. 6 Markov Chains We want to develop a probabilistic model for CpG islands, such that every CpG island sequence is generated by the model. Since dinucleotides are important, we want a model that generates sequences in which the probability of a symbol depends on the previous symbol. The simplest one is a Markov chain.
- 8. 8 Markov Chains Training the model, i.e., estimate the transition probabilities ` ` t st st st c c a Where Cst is the number of times that letter t followed letter s Maximum likelihood (ML) approach is used to estimated the transition probabilities
- 9. 9 Prediction Using Data-Mining Approach is
- 10. 10 Markov Chains The probability that a sequence x is generated by a Markov chain model By applying many times of ) | ( ) ( ) , ( X Y P X P Y X P
- 11. 11 Markov Chains One assumption of Markov chain is that the probability of xi only depend on the previous symbol xi-1, i.e., Thus,
- 12. 12 Markov Chains Given a sequence x, does it belong to CpG islands? If the log likelihood ratio >0, then x belongs to CpG islands.
- 13. 13 Markov Chains In this model, we must specify the probability P(x1) as well as the transition probabilities . To make the formula homogeneous (i.e., comprise of only terms in the form of ), we can introduce a begin state to the model.
- 14. 14 Markov Chains
- 15. 15 Markov Chains The probability that a sequence x is generated by a Markov chain model (with a begin state)
- 16. 16 Markov Chains Training the model, i.e., estimate the transition probabilities ` ` t st st st c c a Where Cst is the number of times that letter t followed letter s Maximum likelihood (ML) approach is used to estimated the transition probabilities
- 17. 17 Markov Chains A set of CpG islands (CpG model) 1st row: The probabilities that A is followed by each of the four bases. The sum of each row is 1 A set of sequences that are not CpG islands (Background model)
- 18. 18 Markov Chains Given a sequence x, does it belong to CpG islands? If the log likelihood ratio >0, then x belongs to CpG islands.
- 19. 19 Markov Chains
- 20. 20 Markov Chains
- 21. 21 Markov Chains
- 22. 22 Markov Chains to Hidden Markov Models