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Multiple Sequence Alignment
 Multiple Sequence Alignment(MSA) is generally
the alignment of three or more biological sequence
(Protein or Nucleic acid) of similar length.
 From the output, homology can be inferred and the
evolutionary relationship between the sequence
studied.
Dynamic Programming approach
Progressive method
Iterative method
 In fact, dynamic programming is applicable to
align any number of sequences.
 Computes an optimal alignment for a given
score function.
 Because of its high running time, it is not
typically used in practice.
 In this method, pairwise global alignment is
performed for all the possible and these pairs are
aligned together on the basis of their similarity.
 The most similar sequences are aligned together
and thenless related sequences are added to it
progressively one-by-one until a complete
multiple query set is obtained.
 This method is also called hierarchical method
or tree method
 A method of performing a series of steps to produce
sucessively better approximation to align many
sequences step-by-step is called iterative method.
 Here the pairwise sequence alignment is totally
avoided.
 Iterative methods attempt to improve on the weak
point of the progressive methods the heavy
dependence on the accuracy of the initial pairwise
alignment.
 Clustal W
 Clustal W2
 Clustal Omega
 Kalign
 MAFFT
 MUSCLE
 M View
 T-Coffee
 Web PRANK
 MEME
 MACAW
 Detecting similarities between sequences(closely or
distinctly related).
 Detecting conserved regions or motifs in sequences.
 Detecting of structural homologies.
 Thus, assisting the improved prediction of secondary and
tertiary structures of proteins.
Multiple sequence alignment

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Multiple sequence alignment

  • 2.  Multiple Sequence Alignment(MSA) is generally the alignment of three or more biological sequence (Protein or Nucleic acid) of similar length.  From the output, homology can be inferred and the evolutionary relationship between the sequence studied.
  • 4.  In fact, dynamic programming is applicable to align any number of sequences.  Computes an optimal alignment for a given score function.  Because of its high running time, it is not typically used in practice.
  • 5.
  • 6.  In this method, pairwise global alignment is performed for all the possible and these pairs are aligned together on the basis of their similarity.  The most similar sequences are aligned together and thenless related sequences are added to it progressively one-by-one until a complete multiple query set is obtained.  This method is also called hierarchical method or tree method
  • 7.
  • 8.
  • 9.  A method of performing a series of steps to produce sucessively better approximation to align many sequences step-by-step is called iterative method.  Here the pairwise sequence alignment is totally avoided.  Iterative methods attempt to improve on the weak point of the progressive methods the heavy dependence on the accuracy of the initial pairwise alignment.
  • 10.
  • 11.  Clustal W  Clustal W2  Clustal Omega  Kalign  MAFFT  MUSCLE  M View  T-Coffee  Web PRANK  MEME  MACAW
  • 12.  Detecting similarities between sequences(closely or distinctly related).  Detecting conserved regions or motifs in sequences.  Detecting of structural homologies.  Thus, assisting the improved prediction of secondary and tertiary structures of proteins.