Sequence Alignments

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Sequence Alignments

• BIOL/CHEM 4900

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Reading

• Chapter 2 in your textbook

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Sequence Alignments

• Alignment between characters found in two or more
  nucleotide or amino acid sequences
• Amino Acids?residues Nucleic Acids
• Similarity between sequences
• What can this tell you?
• In this chapter
• How do we align two or more sequences?
• How do we evaluate these alignments?
• What conclusions can we make based on these
  alignments?

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Dot Plots

• Used to visualize regions of similarity
• One sequence placed on the x-axis, the other on
  the y-axis
• Dots are placed in the plot where the two
  sequences are identical
• Diagonal lines in plot indicate regions of
  similarity
• Example compare ATCG to GATC
• Advantages easy, quick
• Disadvantages only gives regions of similarity,
  not actual alignment
• What would the dot plot look like with longer
  sequences?

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Noise in Dot Plots

• Control by adjusting the following
• Window size
• Similarity cutoff
• Removing too much noise might conceal small
  region of similarity
• Example GCTAGTCAGA and GATGGTCACA

Complete this plot!
Window of 1 Similarity cutoff of 1
Window of 4 Similarity cutoff of 3
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Dot Plots in Excel
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Try the DotPlot Program

• Download the program from this link
• It will automatically save the program and
  several files to your desktop
• Open DotPlot application
• Load sequences as FASTA text files
• File, Open Horizontal, Browse
• File, Open Vertical, Browse
• Parameters menu changes length and cutoff
• Draw, Identities shows plot
• Clear screen when change parameters to visualize
• Example Bos taurus and porcine myoglobin mRNA
  sequences (sequences on course website)

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Simple Alignments

• Molecular changes occur when organisms evolve
• Mutation
• Most common
• Insertion
• Deletion
• Gaps in alignments
• Added to account for insertions/deletions
• Goal to obtain optimal alignment
• Most likely to represent the true relationship
  between homologous sequences
• Consider the following sequences AATCTATA and
  AAGATA
• Either 2 insertions in first sequence or 2
  deletions in second sequence
• What is the optimal alignment?

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• If no gaps allowed, there are three ways the
  sequences can be aligned
• AATCTATA AATCTATA AATCTATA
• AAGATA AAGATA AAGATA
• Which alignment is optimal?
• Scoring alignments
• Match score credit for identical aligned pair
• Mismatch score penalty for nonidentical
  residues
• Total score sum of match and mismatch scores
• Higher score better alignment

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• If gaps are allowed, there are many more ways the
  sequences can be aligned
• Three examples
• AATCTATA AATCTATA AATCTATA
• AAG-AT-A AA-G-ATA AA--GATA
• Scoring must now account for gaps
• Gap penalty penalty for each residue aligned
  with
• Total score match mismatch gap penalty

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• If match 1, mismatch 0, and gap penalty -1,
  what are the scores for these three alignments?
• AATCTATA AATCTATA AATCTATA
• AAG-AT-A AA-G-ATA AA--GATA
• Score 1 3
  3

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Gap Penalties

• Is it more likely to have one longer
  insertion/deletion, or multiple smaller ones?
• Two types of gap penalties
• Length penalty
• Penalty for each residue aligned with -
• Origination penalty
• Penalty for presence of a gap
• Allows differentiation between alignments with
  many short gaps and those with fewer, longer gaps
• Further penalizes for rare insertion/deletion
  (indel) events

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• If match 1, mismatch 0, length penalty -1,
  and origination penalty -2, what are the scores
  for these three alignments?
• AATCTATA AATCTATA AATCTATA
• AAG-AT-A AA-G-ATA AA--GATA
• Score -3 -1
  1

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Terminal Gaps

• Might not actually be indels
• Data could be incomplete
• Sometimes ignored in scoring
• AATCTATAGC
• AAG--ATA--

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Mismatch Penalties

• Different mismatch scores depending on particular
  nucleotide or amino acid that is mismatched
• Reward mismatches that are more likely to occur
  (common substitutions)
• Nucleotides
• Purine vs. pyrimidine
• Transitions vs. transversions

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Scoring Matrices

• Show scores for all non-gap positions in
  alignment
• For nucleotide sequences

Identity (Sparse)
BLAST
Transition/transversion
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Matrices for Proteins

• Amino acids
• 1. Structure and properties
• Substitution of similar AAs
• more likely to retain protein function
  (conservative substitution)
• 2. Genetic code
• Minimum number of nucleotide substitutions needed
  to convert a codon

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Matrices for Proteins

• 3. Actual observed substitution rates
• Point accepted mutation (PAM)
• Alignment constructed with high similarity (gt85)
• Calculate relative mutability (mj)
• Number of times one amino acid (j) is substituted
  by any other
• Calculate specific substitution (Aij)
• Number of times j is substituted by a specific
  amino acid i
• See Box 2.1 (page 40)

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PAM Example

• Ambiguities
• X ambiguous amino acid
• B Asn or Asp
• Z Gln or Glu
• Some algorithms take ambiguities into account and
  score some count them as identical others
  ignore them
• If the sequence has lots of ambiguities scores
  may not be reliable with certain types of software

• Identical amino acids highest score
• Conservative substitution next highest score
• Non-conservative substitution lowest score

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PAM Matrices

• Pam matrix is normalized to represent
  substitution over a fixed period of evolutionary
  change
• PAM-1
• 1 substitution per 100 residues
• Matrix represents probability of AA substitution
  in time it takes for 1 of all residues to be
  substituted
• Used to compare sequences that are closely
  related
• PAM-1000
• Used for sequences with distant relationships
• PAM-250
• Commonly used middle ground

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BLOSUM Matrix

• Also derived from observing substitution rates in
  proteins
• Looks at clusters of amino acids sequences
• Lower numbered matrices used for more distantly
  related sequences
• BLOSUM-45 vs. BLOSUM-80
• BLOSUM-62 is the middle ground and default matrix
  in most protein alignment programs

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PAM and BLOSUM
BLOSUM 80
BLOSUM 62
BLOSUM 45
PAM 1
PAM 250
PAM 1000
More Divergent
Less Divergent
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Types of Scores

• Raw Score
• Protein and nucleotide alignments
• Sum the scores for matches, mismatches, and gaps
• Percent identities
• Protein and nucleotide alignments
• Ratio of residues that match up in both sequences
  to total number of residues compared
• Percent positives
• Protein alignments only
• Matrix values gt1 are called positives
• Ratio of positive values to total number of
  residues compared

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An Example

• Alignment of mouse and crayfish trypsin
• Raw score
• Identities
• Positives

Mouse I V G G Y N C E E N S V P Y
Q 5 4 5 5 -3 2 -2 2 3 0 0 -1 6
10 4 Crayfish I V G G T D A V L G E
F P Y Q
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7/15 47
8/15 53
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Algorithms for Alignments

• Global
• Dynamic programming
• Breaking a problem down into smaller subproblems,
  then rebuilding
• Needleman and Wunsch
• Aligns whole sequences
• All gaps accounted for (internal and terminal)
• Semiglobal
• Revised by Needleman and Wunsch
• Aligns whole sequences
• Only internal gaps count
• Local
• Smith and Waterman
• Aligns localized regions of similarity
• Ignore gaps

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Partial Scores Table

• Used to align sequences
• Top and left axes labeled with sequences
• Contains alignment scores for all alignment
  options
• Used to determine optimal alignment
• Example alignment of ACTCG and ACAGTAG
• Rules for global alignment
• Horizontal move -1 (indicates gap in left axis)
• Vertical move -1 (indicates gap in top axis)
• Diagonal move 1 for match or 0 for mismatch
• First row and column are initialized with
  multiples of gap penalty

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Initial Partial Scores Table
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• Start in outlined box
• Calculate the possible scores from diagonal,
  above, and left
• Put the LARGEST (best) score in the box
• Move across table to complete first row
• Move to second row, etc., until table is complete

Diagonal 0 1(match) 1 Top -1 1
-2 Left -1 1 -2
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Diagonal -1 0(mismatch) -1 Top -2 1
-3 Left 1 1 0
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Completed Table
Now, trace the optimal path. Start at the bottom
right, and move in the direction that gave that
score. End at the top left.
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Completed Table
Now, trace the optimal path. Start at the bottom
right, and move in the direction that gave that
score. End at the top left.
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Completed Path
?
?
?
?
?
?
?
Now, write the alignment
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Writing the Alignment from the Partial Scores
Table

• ? means the two residues are aligned
• ? means there is a gap in top axis
• ? means there is a gap in left axis

G G
CG AG
TCG TAG
-TCG GTAG
--TCG AGTAG
C--TCG CAGTAG
AC--TCG ACAGTAG
?
?
?
?
?
?
?
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Semiglobal Alignments

• Only internal gaps count
• Do not penalize gaps at ends of sequence
• Rules for semiglobal alignment
• Horizontal move -1 (indicates gap in left axis)
  EXCEPT in bottom row
• Vertical move -1 (indicates gap in top axis)
  EXCEPT in last column
• Diagonal move 1 for match or 0 for mismatch
• First row and column are initialized to zero
• Example align ACACTG and ACACTGATCG

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Initial Partial Scores Table
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Diagonal 0 0 (mismatch) 0 Top 0 0 (no
penalty last column) 0 Left 0 1 -1
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Diagonal 0 0 (mismatch) 0 Top 0 1
-1 Left 0 0 (no penalty last row) 0
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Completed Table
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Completed Path and Alignment
?
?
?
?
?
?
?
?
?
?
ACACTGATCG ACACTG----
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Local Alignments

• Used to find best matching subsequences within
  two sequences
• Rules for local alignment
• Horizontal move -1
• Vertical move -1
• Diagonal move 1 for match or -1 for mismatch
• First row and column are initialized to zero
• Place a zero in the table if all other scores are
  negative for that box
• When determining path, find highest number on
  table, and work back until you come to a zero
• Example GCGATATA and AACCTATAGCT

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Completed Table
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Alignment
Start with highest value continue until you
reach zero
?
?
?
TATA TATA
?
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NextBLAST!

• Lets let the computer do the work