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Smt.P.Sangeetha,
Lec. in Biotechnology,
KVRGCW(A), Kurnool
SEQUENCE
ALIGNMENT
SEQUENCE ALIGNMENT
 Sequence alignment is a way of arranging protein (or DNA)
sequences to identify regions of similarity that may be a
consequence of evolutionary relationships between the
sequences.
 Sequence alignments are useful in bioinformatics for identifying
sequence similarity, producing phylogenetic trees, and
developing homology models of protein structures.
Importance of Sequence Alignment
Quantify the
phylogenetic
distance
between two
sequences
Look for
functional
domains
Compare a mRNA
with its genomic
region
Identify
polymorphisms
and mutations
between
sequences.
Types of Sequence Alignment
1. Global
Alignment
2. Local
Alignment
Global Alignment
 In Global alignment, two sequences to be aligned are assumed
to be generally similar over their entire length.
 Alignment is carried out from beginning to the end of both
sequences to find the best possible alignment across the entire
length between the two sequences.
Applications
 Comparing two genes with the same function (in human vs.
mouse).
 Comparing two proteins with similar function.
Global alignment is based on Needleman – Wunsch Algorithm.
Local Alignment
 Local alignment does not assume that the two sequences in
question have similarity over the entire length.
 It only finds the local regions with the highest level of similarity
between the two sequences and aligns these regions without
regard for the alignment of the rest of the sequence regions.
Applications
 Searching for local similarities in large sequences (ex. Newly
sequenced genomes)
 Looking for conserved domains or motifs in two proteins.
Local alignment program is based on Smith-Waterman algorithm.
PAIR WISE SEQUENCE ALIGNMENT
 Pairwise sequence alignment methods are used to find the best
– matching piecewise (local or global) alignments of two query
sequences.
Methods of producing Pair wise alignments
1.Dot matrix method(Old method).
2.Dynamic programming method(DP Method- Advanced method).
3.Word or k – tuple methods.
Tools for Pair wise sequence alignment
BLAST, FASTA
Multiple Sequence Alignment
 Multiple Sequence Alignment (MSA) is the alignment of three or
more biological protein or nucleic acid sequences of similar
length.
Methods of MSA
 Dynamic Programming Approach
 Progressive method
 Iterative method
Tools in MSA
CLUSTAL W, CLUSTAL W2, CLUSTAL Omega, etc..
Applications of MSA
 Detecting the similarities between the sequences(closely or
distantly related).
 Detecting conserved regions or motifs in sequences.
 Detection of structural homologies.
 Improved prediction of secondary and tertiary structures of
proteins.
Thank
You

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Sequence alignment.pptx

  • 2. SEQUENCE ALIGNMENT  Sequence alignment is a way of arranging protein (or DNA) sequences to identify regions of similarity that may be a consequence of evolutionary relationships between the sequences.  Sequence alignments are useful in bioinformatics for identifying sequence similarity, producing phylogenetic trees, and developing homology models of protein structures.
  • 3. Importance of Sequence Alignment Quantify the phylogenetic distance between two sequences Look for functional domains Compare a mRNA with its genomic region Identify polymorphisms and mutations between sequences.
  • 4. Types of Sequence Alignment 1. Global Alignment 2. Local Alignment
  • 5. Global Alignment  In Global alignment, two sequences to be aligned are assumed to be generally similar over their entire length.  Alignment is carried out from beginning to the end of both sequences to find the best possible alignment across the entire length between the two sequences.
  • 6. Applications  Comparing two genes with the same function (in human vs. mouse).  Comparing two proteins with similar function. Global alignment is based on Needleman – Wunsch Algorithm.
  • 7. Local Alignment  Local alignment does not assume that the two sequences in question have similarity over the entire length.  It only finds the local regions with the highest level of similarity between the two sequences and aligns these regions without regard for the alignment of the rest of the sequence regions.
  • 8. Applications  Searching for local similarities in large sequences (ex. Newly sequenced genomes)  Looking for conserved domains or motifs in two proteins. Local alignment program is based on Smith-Waterman algorithm.
  • 9. PAIR WISE SEQUENCE ALIGNMENT  Pairwise sequence alignment methods are used to find the best – matching piecewise (local or global) alignments of two query sequences.
  • 10. Methods of producing Pair wise alignments 1.Dot matrix method(Old method). 2.Dynamic programming method(DP Method- Advanced method). 3.Word or k – tuple methods. Tools for Pair wise sequence alignment BLAST, FASTA
  • 11. Multiple Sequence Alignment  Multiple Sequence Alignment (MSA) is the alignment of three or more biological protein or nucleic acid sequences of similar length.
  • 12. Methods of MSA  Dynamic Programming Approach  Progressive method  Iterative method Tools in MSA CLUSTAL W, CLUSTAL W2, CLUSTAL Omega, etc..
  • 13. Applications of MSA  Detecting the similarities between the sequences(closely or distantly related).  Detecting conserved regions or motifs in sequences.  Detection of structural homologies.  Improved prediction of secondary and tertiary structures of proteins.