Sequence Alignments

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Sequence Alignments

• BIOL/CHEM 3100

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Reading

• Chapter 2 in your textbook

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Sequence Alignments

• Alignment between two or more nucleotide or amino
  acid sequences
• 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
  alignmets?

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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 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

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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

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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 default

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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 1 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