Pairwise Sequence Alignment Part 1

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Pairwise Sequence AlignmentPart 1

• VIBE Education Edition (VIBE-Ed) Initiative

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Overview

• Part 1 Introduction
• Why compare sequences?
• Dynamic Programming Algorithms (Global vs. Local)
• Heuristic algorithms (K-tuple / Word-size)
• Scoring Matrices
• Part 2 Statistics of Similarity Searches
• Scoring Matrices, contd
• Statistics of similarity searching

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Why compare sequences?

• Nature is conservative
• Incremental modifications give rise to genetic
  diversity and novel function
• Detection of similarity between sequences allows
  us to transfer information about one sequence to
  other similar sequences with reasonable, though
  not always total, confidence

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Sequence Alignment

• Before we can make comparative statements about
  two (nucleic acid or protein) sequences, we have
  to produce a pairwise sequence alignment
• What is the optimal alignment between two
  sequences?
• Quantitative? Match/mismatch? Gaps/extensions? Is
  an optimal alignment always significant? Random
  sequences?

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Protein Evolution

• For many proteins, evolutionary history can be
  traced back gt 1 billion years
• Evolutionary time scales / Tree of Life
• Sequence Homology vs. Sequence Similarity

• Homology means common ancestry

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Three alignments, three meanings

• HBA_HUMAN GSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDL
  HAHKL
• G VKHGKKV AAHD LSLH KL
• HBB _HUMAN GNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSEL
  HCDKL
• HBA_HUMAN GSAQVKGHGKKVADALTNAVAHV---D--DMPNALS
  ALSDLHAHKL
• H KV A L LH K
• LGB2_LUPLU NNPELQAHAGKVFKLVYEAAIQLQVTGVVVTDATLKNLG
  SVHVSKG
• HBA_HUMAN GSAQVKGHGKKVADALTNAVAHVDDMPNALSALSD-
  ---LHAHKL
• GS G D L H D A AL D AH
• F11G11.2 GSGYLVGDSLTFVDLL--VAQHTADLLAANAALLDEFPQFK
  AHQE

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Pairwise Sequence Alignment Methods

• Dynamic Programming
• Global Alignment (Needleman-Wunsch)
• Local Alignment (Smith-Waterman)
• Word, or k-tuple methods
• FASTA
• BLAST

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Dynamic Programming Algorithm

• Provides the best (optimal) alignment between two
  sequences
• Includes matches, mismatches and gaps to maximize
  the number of matched characters
• Score match, mismatch, gap (non-affine vs.
  affine)

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Example

• Find optimal global alignment for sequences
• GAAGA
• and
• GTTTAAG

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Define Rules

• Score for match 1
• Score for mismatch -1
• Score for gap -3

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Implement Rules

• When Moving Horizontally (gap)
• Alignment Score Existing Score Gap Score
• When Moving Vertically (gap)
• Alignment Score Existing Score Gap Score
• When Move Diagonally (match or mismatch)
• Alignment Score Existing Score Corner Value

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Optimal Global Alignments
1) GAAGA__ -7 GTTTAAG
5) GAAA_G_ -7 GTTTAAG
6) GAA_GA_ -7 GTTTAAG
2) G_A_AGA -7 GTTTAAG
3) G__AAGA -7 GTTTAAG
4) G_AAGA_ -7 GTTTAAG
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Global vs. Local Alignment
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Smith Waterman

• Dynamic programming - local alignment
• Compares query to each sequence in database.
• Performs full pairwise comparison.
• More sensitive, but much slower than heuristic
  algorithms (BLAST or FASTA)

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Optimal Local Alignment
GAAGA 3 GTTTAAG
AAG 3
AAG
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Heuristic (word or k-tuple based) algorithms

• Make reasonable assumptions about nature of
  sequence alignments and try out only most
  likely alignments
• Find perfect match (word, k-tuple)
• Extend alignment until
• One or the other sequences ends
• Score drops below a threshold
• Much faster than dynamic programming methods, but
  less sensitive

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FASTA (Pearson and Lippman 1988)

• Combination of word (k-tup) search and
  Smith-Waterman algorithm
• The query sequence is divided into small words of
  certain size.
• The initial comparison of the query sequence to
  the database is performed using these words.
• If these words are located on the same diagonal
  in an array the region surrounding the diagonals
  are analyzed further.
• Search time is only proportional to size of
  database not (databasequery sequence)

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The ktup value

• The ktup (for k-tuples) value stands for the
  length of the word used
• to search for identity.
• For proteins a ktup value of 3 would give a hash
  table of 203
• elements (8000 entries).
• The higher the ktup value the less likely you
  will get a match
• unless it is identical (remember the dot
  plots).
• The lower the ktup value the more background you
  will have
• The higher the ktup value the faster analysis
  (fewer diagonals).

Typical ktup values
ktup analysis____________________
1 proteins- distantly related 2
proteins- somewhat related (default)
3 DNA-default
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FASTA Steps
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Different offset values
1
Identical offset values in a contiguous sequence
Diagonals are extended
Local regions of identity are found
Rescore the local regions using scoring matrices
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3
Create a gapped alignment in a narrow segment and
then perform S-W alignment
Eliminate short diagonals below a cutoff score
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Summary of FASTA steps

• 1. Analyzes database for identical matches that
  are contiguous.
• 2. Longest diagonals are scored again using the
  PAM matrix (or other matrix). The best scores
  are saved as init1 scores.
• 3. Short diagonals are removed.
• 4. Long diagonals that are neighbors are joined.
  The score for this joined region is initn.
  This score may be lower due to a penalty for a
  gap.
• 5. A S-W dynamic programming alignment is
  performed around the joined sequences to give an
  opt score.
• Thus, the time-consuming S-W step is performed
  only on top scoring sequences

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FASTA Versions
fasta compares a protein sequence to a protein
database or nucleotide sequence to a nucleotide
database fastx compares a translated query
sequence fasty to a protein sequence database
(forward or backward translation of the
query) tfastx compares protein query sequence
to tfasty nucleotide sequence database that
has been translated into three forward
and three reverse reading frames
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BLAST(Karlin and Altschul 1990)

• Basic Local Alignment Search Tool
• Database is pre-indexed to increase speed
• The initial search is done for a word of length
  "W" that scores at least "T" when compared to the
  query using a substitution matrix.
• Word hits are then extended in either direction
  in an attempt to generate an alignment with a
  score exceeding the threshold of "S".
• The "T" parameter dictates the speed and
  sensitivity of the search.

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BLAST Versions
BLASTN Compares a nucleotide query to a
nucleotide database BLASTP Compares a protein
query to a protein database BLASTX Compares
a translated nucleotide query to a protein
database. TBLASTN Compares a protein query to a
translated nucleotide database. TBLASTX Compare
s a translated nucleotide query to a translated
nucleotide database.
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Scoring Matrices

• The alignment score represent odds of obtaining
  that score between sequences known to be related
  to that obtained by chance alignment between
  unrelated sequences
• When the correct scoring matrix is used,
  alignment statistics are meaningful

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Dayhoff PAM Matrix(Point Accepted Mutation)

• Lists the likelihood of change from one amino
  acid to another in homologous protein sequences
  during evolution
• PAM 1 estimated using 1572 changes in 71 groups
  of protein sequences that were at least 85
  similar
• Assumes each amino acid change at a site is
  independent of previous changes at the site
• PAM 250 (20 similarity) obtained by multiplying
  PAM1 by itself 250 times

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Blocks Amino Acid Substitution (BLOSUM) Matrix

• Based on the observed amino acid substitutions in
  blocks (large set of 2000 conserved amino acid
  patters)
• Used 500 families of related proteins
• Not based on explicit evolutionary model, but
  from considering all amino acid changes observed
  in an aligned region from a related family of
  proteins.

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PAM250 Scoring Matrix
A R N D C Q E G H I L K M F P
S T W Y V B Z A 2 -2 0 0 -2 0 0 1 -1
-1 -2 -1 -1 -3 1 1 1 -6 -3 0 2 1 R -2
6 0 -1 -4 1 -1 -3 2 -2 -3 3 0 -4 0 0 -1 2
-4 -2 1 2 N 0 0 2 2 -4 1 1 0 2 -2
-3 1 -2 -3 0 1 0 -4 -2 -2 4 3 D 0 -1
2 4 -5 2 3 1 1 -2 -4 0 -3 -6 -1 0 0 -7 -4
-2 5 4 C -2 -4 -4 -5 12 -5 -5 -3 -3 -2 -6
-5 -5 -4 -3 0 -2 -8 0 -2 -3 -4 Q 0 1 1
2 -5 4 2 -1 3 -2 -2 1 -1 -5 0 -1 -1 -5 -4 -2
3 5 E 0 -1 1 3 -5 2 4 0 1 -2 -3 0
-2 -5 -1 0 0 -7 -4 -2 4 5 G 1 -3 0 1
-3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5 -1
2 1 H -1 2 2 1 -3 3 1 -2 6 -2 -2 0 -2
-2 0 -1 -1 -3 0 -2 3 3 I -1 -2 -2 -2 -2
-2 -2 -3 -2 5 2 -2 2 1 -2 -1 0 -5 -1 4 -1
-1 L -2 -3 -3 -4 -6 -2 -3 -4 -2 2 6 -3 4
2 -3 -3 -2 -2 -1 2 -2 -1 K -1 3 1 0 -5 1
0 -2 0 -2 -3 5 0 -5 -1 0 0 -3 -4 -2 2 2
M -1 0 -2 -3 -5 -1 -2 -3 -2 2 4 0 6 0 -2
-2 -1 -4 -2 2 -1 0 F -3 -4 -3 -6 -4 -5 -5 -5
-2 1 2 -5 0 9 -5 -3 -3 0 7 -1 -3 -4 P
1 0 0 -1 -3 0 -1 0 0 -2 -3 -1 -2 -5 6 1 0
-6 -5 -1 1 1 S 1 0 1 0 0 -1 0 1 -1
-1 -3 0 -2 -3 1 2 1 -2 -3 -1 2 1 T 1
-1 0 0 -2 -1 0 0 -1 0 -2 0 -1 -3 0 1 3
-5 -3 0 2 1 W -6 2 -4 -7 -8 -5 -7 -7 -3
-5 -2 -3 -4 0 -6 -2 -5 17 0 -6 -4 -4 Y -3
-4 -2 -4 0 -4 -4 -5 0 -1 -1 -4 -2 7 -5 -3 -3
0 10 -2 -2 -3 V 0 -2 -2 -2 -2 -2 -2 -1 -2 4
2 -2 2 -1 -1 -1 0 -6 -2 4 0 0 B 2 1
4 5 -3 3 4 2 3 -1 -2 2 -1 -3 1 2 2 -4 -2
0 6 5 Z 1 2 3 4 -4 5 5 1 3 -1 -1
2 0 -4 1 1 1 -4 -3 0 5 6
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Summary

• Choose appropriate algorithm (speed vs.
  sensitivity)
• Use smallest database that will answer your
  question
• Default matrices may not always give a meaningful
  score