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A Hidden Markov model for progressive multiple alignment

A Hidden Markov model for progressive multiple alignment. -Ari Loytynoja and Michel C.Milinkovitch Presnted by Santosh Kumar Kodicherla. HMM Applications . Hidden Markov Model is used to find optimal value in many applications like: 1. In Membrane Helix

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A Hidden Markov model for progressive multiple alignment

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  1. A Hidden Markov model for progressive multiple alignment -Ari Loytynoja and Michel C.Milinkovitch Presnted by Santosh Kumar Kodicherla

  2. HMM Applications • Hidden Markov Model is used to find optimal value in many applications like: 1. In Membrane Helix 2. In finding a dice whether its Fair dice or not. 3.Decesion tree applications, Neural Networks etc.

  3. Working of HMM for Simple Pair wise Alignment • We check The two sequences and built the unknown parent. (Similarity is maximum). • This forms the basis for Current Algorithm. Parent Seq1 Seq2

  4. Steps in HMM Works

  5. Alignments • Pairwise Alignment PDGIVTSIGSNLTIACRVS PPLASSSLGATIRLSCTLS Multiple Alignment DREIYGAVGSQVTLHCSFW TQDERKLLHTTASLRCSLK PAWLTVSEGANATFTCSLS LPDWTVQNGKNLTLQCFAD LDKKEAIQGGIVRVNCSVP SSFTHLDQGERLNLSCSIP DAQFEVIKGQTIEVRCESI LSSKVVESGEDIVLQCAVN PAVFKDNPTEDVEYCCVAD

  6. Systems and Models • Building Multiple alignment with Decreasing Similarity. • Compute probabilistic alignment • Keep Track of child pointers. • For each site Vector probabilities of alternate characters A/C/G/T/- is calculated. • New node generated is aligned with another internal sequence and cont. • Once root node is defined for multiple alignments ,we use recursive back tracking to generate multiple alignments.

  7. Substitution Model • Consider Seqx, Seqy- generate Seqz(Parent) Terms: Pa(Xi) –Probability Seq Xi has character ‘ a ‘. If a char is observed it is given a prob=1. Character ‘a’ has a background probability qa a Evolves b, this represented as Sab. Comparing characters, Substitution. GAP: Pxi,yi= represents prob. Xi,Yi are aligned and generate Zi. For all the character states ‘a’ in Zk- • pxi ,y j= pzk (xi , y j ) =∑pzk=a(xi , y j ). • pzk=a(xi , y j ) = qa ∑b sab pb(xi ) ∑b sab pb(y j )

  8. Steps in Algorithm: • Look back HM Model. • Pair wise alignment • Calculate Posterior Probability. • Multiple Alignment • Testing Algorithm

  9. Look back HM Model • Defines 3 states, Match M, x-insert ,y-insert. -Calculate probabilities of Moving from M to X or Y represented as δ. -Probability to stay at insert ‘ε ‘. -Probability to move back to M.

  10. Pair wise alignment : • In Dynamic prog, we define matrix and makes recursive calls, by choosing best path. • Use Backtracking to find the best path. • Veterbi path to get the best alignment path. • Used to find the parent vector which represents both childs.

  11. Forward and backward recursions.

  12. Multiple Alignment Observations. • The pair wise algorithm works progressively from tip of the node to root of tree. • Once root node is defined multiple alignments can be generated. • If a gap is introduced in the process , the recursive call does not proceed. • At a given column most of sequences are well aligned except few which may contain Gaps.

  13. Testing the new Algorithm

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