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Multiple alignment using hidden Markove models

Multiple alignment using hidden Markove models. November 21, 2001 Kim Hye Jin Intelligent Multimedia Lab marisan@postech.ac.kr. Outline. Introduction Methods and algorithm Result Discussion. IM lab. Introduction| why HMM. Introduction. Why HMM?

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Multiple alignment using hidden Markove models

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  1. Multiple alignment using hidden Markove models November 21, 2001 Kim Hye Jin Intelligent Multimedia Lab marisan@postech.ac.kr

  2. Outline • Introduction • Methods and algorithm • Result • Discussion IM lab

  3. Introduction| why HMM Introduction • Why HMM? • Mathematically consistent description of insertions and deletions • Theoretical insight into the difficulties of combining disparate forms of information Ex) sequences / 3D structures • Possible to train models from initially unaligned sequences IM lab

  4. Methods and algorithms|HMMs Methods and algorithms • State transition • State sequence is a 1st order Markov chain • Each state is hidden • match/Insert/delete state • Symbol emission States transition Symbol emission IM lab

  5. Methods and algorithms|HMMs Deletion state Match state Insertion state IM lab

  6. Methods and algorithms|HMMs Methods and algorithms • Replacing arbitrary scores with probabilities relative to consensus • Model M consists of N states S1…SN. • Observe sequence O consists of T symbols • O1… ON from an alphabet x • aij : a transition from Si to Sj • bj(x) : emission probabilities for emission of a symbol x from each state Sj IM lab

  7. Methods and algorithms|HMMs Methods and algorithms • Model of HMM : example of ACCY IM lab

  8. Methods and algorithms|HMMs Methods and algorithms • Forward algorithm • - a sum rather than a maximum IM lab

  9. Methods and algorithms|HMMs Methods and algorithms • Viterbi algorithm • the most likely path through the model • following the back pointers IM lab

  10. Methods and algorithms|HMMs Methods and algorithms • Baum-Welch algorithm • A variation of the forward algorithm • Reasonable guess for initial model and then calculates a score for each sequence in the training set using EM algorithms • Local optima problem: • forward algorithm /Viterbi algorithm • Baum-welch algorithm IM lab

  11. Methods and algorithms|HMMs Methods and algorithms • Simulated annealing • support global suboptimal • kT = 0 : standard Viterbi training procesure • kT goes down while in training IM lab

  12. Methods and algorithms|HMMs Methods and algorithms ClustalW IM lab

  13. Methods and algorithms|HMMs Methods and algorithms ClustalX IM lab

  14. Results Results • len : consensus length of the alignment • ali : the # structurally aligned sequences • %id: the percentage sequence identity • Homo: the # homologues identified in and extraced from SwissProt 30 • %id : the average percentage sequence identity in the set of homologues IM lab

  15. Results Results IM lab

  16. Discussion Discussion • HMM • a consistent theory for insertion and deletion penality • EGF : fairly difficult alignments are well done • ClusterW • progressive alignment • Disparaties between the sequence identity of the structures and the sequence identity of the homologoues • Large non-correlation between score and quality IM lab

  17. Discussion Discussion • The ability of HMM to sensitive fold recognition is apparent IM lab

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