A Hidden Markov model for progressive multiple alignment

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.

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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
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Steps in HMM Works
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Alignments

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

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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.

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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 prob1.
• 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 ) ?pzka(xi , y j ).
• pzka(xi , y j ) qa ?b sab pb(xi ) ?b sab pb(y
  j )

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

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• 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 d.
• -Probability to stay at insert e .
• -Probability to move back to M.

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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.

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Forward and backward recursions.
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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.

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Testing the new Algorithm