Chapter 2 Data Searches and Pairwise Alignments

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Chapter 2Data Searches and Pairwise Alignments

• ??????????
• ???
• 2004/03/08

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Introduction

• What is the difference between acctga and agcta?

a c c t g a a g c t g a a g c t - a
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Nomenclature
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2.1 Dot Plots
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2.2 Simple Alignments

• No gap

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• mutation (substitution) common
• insertion
• deletion
• scoring scheme
• match score
• mismatch score

gap, indel (rare)
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2.3 Gaps
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2.3.1 Gap Penalty

• uniform gap
• affine gap
• origination penalty
• length penalty

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2.4 Scoring Matrices
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• Modeling ???
• ??????????????

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Modeling
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Define the odds ratio as
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2.4.1 PAM Matrices

• Dayhoff, Schwartz, Orcutt (1978)
• Point Accepted Mutation
• Based on observed substitution rates
• (Box. 2.1)
• Input
• A set of observed substitution rates
• Output
• PAM-1 matrix (log-odds matrix)

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Multiple Alignment

• (1) Group the sequences with high similarity (gt
  85 identity).

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Phylogenetic Tree

• (2) For each group, build the corresponding
  phylogenetic tree.

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Mutation Frequency

• A-gtG, I-gtL, A-gtG, A-gtL, C-gtS, G-gtA
• (3)
• FG,A3

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Relative Mutability

• (4)

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Mutation Probability

• (5)

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Odds Ratio

• (6)

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Log-Odds Ratio

• (7)

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• Which PAM matrix is the most appropriate?
• the length of the sequences
• How closely the sequences are believed to be
  related.
• ? PAM 120 for database search
• ? PAM 200 for comparing two specific proteins

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2.4.2 BLOSUM Matrices

• Henikoff Henikoff (1992)
• PAM-k k??, ????
• BLOSUM-k k?????
• ? BLOSUM62 for ungapped matching
• ? BLOSUM50 for gapped matching

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2.5 Dynamic Programming

• The Needleman and Wunsch Algorithm (Global
  Alignment)

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Alignment Graph
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A C - - T C G A C A G T A G
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Complexity
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2.6 Global and Local Alignments

• Semi-global alignment
• Local alignment

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2.6.1 Semi-global Alignments

• A A C A C G T G T C T
• - - - A C G T - - - -

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2.6.2 Local Alignment

• The Smith-Waterman Alignment

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2.7 Database Searches

• BLAST and its relatives
• FASTA and related algorithms

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2.7.1 BLAST and Its Relatives
Program Database Query
BLASTN Nucleotide Nucleotide
BLASTP Protein Protein
BLASTX Protein Nucleotide? Protein
TBLASTN Nucleotide? Protein Protein
TBLASTX Nucleotide? Protein Nucleotide? Protein
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BLASTP

• Using PAM or BLOSUM matrices

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2.7.2 FASTA and Related Algorithms

• ?? dot plot band search
• Preprocess the target sequence.
• Identify the position for each word.
• (for amino acid word length1, a 20-entry
  array)
• Scan the query sequence.
• Compute the shifts of query to align each word
  with the target.
• Find the mode (??) of the shifts.
• Join the possible shifts into one new target
  sequence. Perform the full local alignment
  algorithm.

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• Target FAMLGFIKYLPGCM
• QueryTGFIKYLPGACT

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2.7.3 Alignment Scores and Statistical
Significance of Database Searches

• related model v.s. random model
• S-score the alignment score
• E-score expected number of sequences with score
  gt S by random chance
• P-score probability that one or more sequences
  with score gt S would be found randomly
• ? Low E P are better.

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• length correction
• Scores

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PAM 120 (ln 2)/2 nats

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

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Applications

• Reconstructing long sequences of DNA from
  overlapping sequence fragments
• Determining physical and genetic maps from probe
  data under various experiment protocols
• Database searching
• Comparing two or more sequences for similarities

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• Protein structure prediction (building profiles)
• Comparing the same gene sequenced by two
  different labs

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2.8 Multiple Sequence Alignemnts

• CLUSTAL
• R. G. Higgins P. M. Sharp, 1988
• CLUSTALW
• Sequences are weighted according to how divergent
  they are from the most closely related pair of
  sequences.
• Gaps are weighted for different sequences.

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Summary

• notion of similarity
• the scoring system used to rank alignments
• the algorithms used to find optimal scoring
  alignment
• the statistical method used to evaluate the
  significance of an alignment score

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?????????

• Fundamental Concepts of BioinformaticsDan E.
  Krane and Michael L. Raymer, Benjamin/Cummings,
  2003.
• BLAST, by I. Korf, M. Yandell, J. Bedell,
  OReilly Associates, 2003. (????)
• Biological Sequence Analysis Probabilistic
  Models of Proteins and Nucleic AcidsR. Durbin,
  S. Eddy, A. Krogh, and G. Mitchison,Cambridge
  University Press, 1998.
• Biochemistry, by J. M. Berg, J. L. Tymoczko, and
  L. Stryer, Fith Edition, 2001.