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Pairwise Sequence Analysis-I
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Analogous Concepts
Domain Concepts
Dot-matrix Analysis
Pairwise Sequence Analysis
– Why is it useful?
– What are the underlying concepts?
– What algorithms are used?
– Limitations/Open questions
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Concepts
• Pairwise “everything” analysis
– Humans are experts at pairwise comparison
– “Peas of a pod”
– Basis for pecking order in society
– Basis for notion of fairness (“He got a better
deal than I did!”)
– Basis for grouping
– Basis for sorting/ranking algorithms
– Basis for search strategies
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Concepts
• Identity versus similarity
– Brand name versus generic products
• Coincidence versus plagiarism
– Co-discoveries versus “Microsoft Explorer”
• “What you see is not what you got”
– Process not evident from product
• “If it ain’t broke, don’t fix it”
– Why messy code that works is left to remain
messy
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Domain Concepts
• Sequences change over generations
– More changes in DNA than in proteins (Why?)
• Causes of change
– Error rates of replication ~ 1 in a billion
– Exchange
• Legitimate: Recombination
• Illegitimate: Lateral transfer (a la Napster)
– Mutation
• Exposure to chemicals
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Dot-matrix Analysis
A B L E W A S O N E E R
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E O N E S A W E L B A
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Why (Motivation)
1. Given sequence A, what are its properties?
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Find any sequence(s) B similar to A, where B 
{Sequences with known properties}
2. Given sequences A & B, are they
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Identical?
Similar?
Evolutionary related?
3. Given sequence A, is this a new discovery?
4. Given a set of sequences, can they be
clustered into families of related sequences?
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Components
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Compare the two sequences and assign
score
• Evaluate the score
1. Use of a scoring scheme to assign a value
to a candidate alignment
2. Finding the best alignment between two
sequences
3. Use of a probability model to assess the
significance of the similarity (coincidence or
plagiarism?)
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Concepts - Alignment
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Sequences may be aligned ‘locally’ (look for
regions/subsequences that are similar) or
‘globally’ (align along entire length)
The result of a local alignment may differ from
that a global alignment (Why?)
Potentially large number of alignments to be
considered
Each alignment is a path through a fully
connected dot matrix graph, i.e., a subset of
the set of all diagonal edges, connected by
horizontal or vertical lines.
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Concepts – Scoring scheme
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Scores are based on amino-acid
substitution matrices (log-odds ratio of
observed versus expected random
substitutions)
– PAM (Percent amino acid substitution)
matrices: Based on evolutionary model.
E.g., PAM 1%, PAM 250%.
– BLOSUM (Blocks substitution) matrices:
Based on percent identity. E.g., Blosum50,
Blosum62.
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Concepts – Scoring scheme
Identity based scoring (match XOR non-match)
Isawa --- dancer
Isawatapdancer
Isawa ---- danc-er
Isawapinkpanther
Similarity based scoring (partial matching)
Eyl
dkv
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Algorithms
• Optimal/Exact solutions:
– Take longer time
– Typically used for comparing a small number of
sequences
– (Needleman-Wunsch; Smith-Waterman)
• Heuristic:
– Frequently close to the optimal solution
– Rapid
– Typically used for 1:n searches involving large
number of sequences
– (BLAST, FASTA)
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Open Questions/Limitations
Highly similar sequences or highly dissimilar
sequences are easily found BUT
• Gray area lies in weak similarity; probability
distribution is continuous
• Other forms of similarity (e. g., structural or
species similarity) may be necessary reach a
decision
• Sequence similarity per se does not guarantee
functional equivalence; small difference can be
responsible for large difference in function
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