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. 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. Seq1Seq2 Parent

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: Terms: P a (X i ) –Probability Seq X i has character ‘ a ‘. P a (X i ) –Probability Seq X i has character ‘ a ‘. If a char is observed it is given a prob=1. If a char is observed it is given a prob=1. Character ‘a’ has a background probability q a a Evolves b, this represented as S ab. Comparing characters, Substitution. GAP: P xi,yi = represents prob. X i,Y i are aligned and generate Z i. P xi,yi = represents prob. X i,Y i are aligned and generate Z i. For all the character states ‘a’ in Z k- – –p xi,y j = p zk (xi, y j ) =∑p zk=a (xi, y j ).   p zk=a (xi, y j ) = qa ∑b sab pb(xi ) ∑b sab pb(y j )

8. Steps in Algorithm: 1.Look back HM Model. 2.Pair wise alignment 3.Calculate Posterior Probability. 4.Multiple Alignment 5.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