ALIGNMENT

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ALIGNMENT

• How do we tell whether two sequences are similar?
  

Prev. reading Ch 1, Ch 3 Assigned reading Ch 11
BIO520 Bioinformatics Jim Lund
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Alignments

• DNADNA
• polypeptidepolypeptide

THE BASIC Sequence Analysis Operation
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Alignments

• Pairwise sequence alignments
• One-to-One
• One-to-Database
• Multiple sequence alignments
• Many-to-Many

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Origins of Sequence Similarity

• Homology
• common evolutionary descent
• Similarity in function
• Convergence (very rare)
• Chance
• Short similar segments are very common.

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Visual sequence comparison Dotplot
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Visual sequence comparison Filtered dotplot
4 bp window, 75 identity cutoff
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Visual sequence comparison Dotplot
4 bp windw, 75 identity cutoff
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Similarity
GAACAAT 7/7 OR 100 GAACAAT
Which is BETTER? How do we SCORE?
GAACAAT 1/7 or 14GAACAAT
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Similarity
GAACAAT 7/7 OR 100 GAACAAT
GAACAAT 6/7 OR 84 GAATAAT
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Mismatches
GAACAAT 6/7 OR 84 GAATAAT
Same??
GAACAAT 6/7 OR 84 GAAGAAT
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Terminal Mismatch
GAACAATttttt aaaccGAATAAT 6/7
OR 84
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INDELS
GAAgCAAT 7/7 OR 100 GAACAAT
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Indels, contd
GAAgCAAT GAACAAT
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Similarity Scoring

• Common Method
• Terminal mismatches (0)
• Match score (5)
• Mismatch penalty (-4)
• Gap penalty (-5)
• Gap extension penalty (-3)

DNA Defaults
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DNA Scoring
GGGGGGGGGG 5(5)5(-4)5 GGGGGAAAAA
GGGGG
GGGGGGGGGG
10(5)(-5)5(-3)30 GGGGGAAAAAGGGGG
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Absurdity of Low Gap Penalty
GATCGCTACGCTCAGC A.C.C..C..T
Perfect similarity, Every time!
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Sequence alignment algorithms

• Local alignment
• Smith-Waterman
• Global alignment
• Needleman-Wunsch

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Alignment Programs

• Local alignment (Smith-Waterman)
• BLAST (simplified Smith-Waterman)
• FASTA (simplified Smith-Waterman)
• BESTFIT (GCG program)
• Global alignment (Needleman-Wunsch)
• GAP

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Local vs. global alignment
10 gaggc 15 3 gaggc 7
Local alignment alignment of regions of
substantial similarity
1 gggggaaaaaggggccccc 19
1 gggggttttttttggggtttcc 22
Global alignment alignment of the full length of
the sequences
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BLAST Algorithm

• Look for local alignment, a High Scoring Pair
  (HSP)
• Finding word (W) in query and subject. Score gt
  T.
• Extend local alignment until score reaches
  maximum-X.
• Keep High Scoring Segment Pairs (HSPs) with
  scores gt S.
• Find multiple HSPs per query if present
• Expectation value (E value) using Karlin-Altschul
  stats

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BLAST statistical significance assessing the
likelihood a match occurs by chance
Karlin-Altschul statistic E k m N exp(-Lambda
S) m Size of query seqeunce N Size of
database k Search space scaling
parameter Lambda scoring scaling parameter S
BLAST HSP score Low E -gt good match
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BLAST statistical significance

• Rule of thumb for a good match
• Nucleotide match
• E lt 1e-6
• Identity gt 70
• Protein match
• E lt 1e-3
• Identity gt 25

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Protein Similarity

• Identity-Easy
• WEAK Alignments
• Chemical Similarity
• L vs I, K vs R
• Evolutionary Similarity
• How do proteins evolve?
• How do we infer similarities?

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Single-base evolution changes the encoded AA

• CAUH
• CACH CGUR UAUY
• CAAQ CCUP GAUD
• CAGQ CUUL AAUN

Selection Drift...etc
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Substitution Matrices

• Two main classes
• PAM-Dayhoff
• BLOSUM-Henikoff

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PAM-Dayhoff

• Built from closed related proteins, substitutions
  constrained by evolution and function
• accepted by evolution (Point Accepted
  MutationPAM)
• 1 PAM1 divergence
• PAM120closely related proteins
• PAM250divergent proteins

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BLOSUM-HenikoffHenikoff

• Built from ungapped alignments in proteins
  BLOCKS
• Merge blocks at given similar to one sequence
• Calculate target frequencies
• BLOSUM6262 similar blocks
• good general purpose
• BLOSUM30
• Detects weak similarities, used for distantly
  related proteins

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BLOSUM62
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Gapped alignments

• No general theory for significance of matches!!
• GL(n)
• indel mutations rare
• variation in gap length easy, G gt L

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Real Alignments
Protein-Protein
Close-Distant
DNA-DNA
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Phylogeny
Myoglobin
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Cow-to-Pig
88 identical
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Cow-to-Pig cDNA
80 Identity (88 at aa!)
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DNA similarity reflects polypeptide similarity
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Coding vs Non-coding Regions
90 in Coding 74 in Non-coding
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Third Base of Codon Hypervariable
28 third base 11 second 8 first
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Cow-to-Fish Protein
42 identity 51 similairity
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Cow-to-Fish DNA
48 similairity Significant
30-NOT significant
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Protein vs DNAAlignments

• Polypeptide similarity gt DNA
• Coding DNA gt Non-coding
• 3rd base of codon hypervariable
• Moderate Distance ?
• poor DNA similarity

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Rules of Thumb

• DNA-DNA similarities
• 50 significant if long
• E lt 1e-6, 70 identity
• Protein-protein similarities
• 80 end-end same structure, same function
• 30 over domain, similar function, structure
  overall similar
• 15-30 twilight zone
• Short, strong matchcould be a motif

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Finding similar sequencesDatabase searches

• DNA
• Polypeptide

• FASTA
• BLAST

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Basic BLAST Family

• BLASTN
• DNA to DNA database
• BLASTP
• protein to protein database
• TBLASTN
• DNA (translated) to protein database
• BLASTX
• protein to DNA database (translated)
• TBLASTX
• DNA (translated) to DNA database (translated)

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DNA Databases

• nr (non-redundantish merge of Genbank, EMBL,
  etc)
• EXCLUDES EST, STS,GSS
• est (expressed sequence tags)
• htgs (high throughput genome seq.)
• gss (genome survey sequence)
• vector, yeast, ecoli, mito
• chromosome (complete genomes)
• And more

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Protein Databases

• nr (non-redundant Swiss-prot, PIR, PDF, PDB,
  Genbank CDS)
• swissprot
• ecoli, yeast, fly
• month
• And more

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BLAST Input

• Program
• Database
• Options-see more
• Sequence
• FASTA
• gi or accession

gtone line gggtcgagtac
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BLAST Options

• Algorithm and output options
• descriptions, alignments returned
• probability cutoff
• Strand
• Alignment parameters
• Scoring Matrix
• PAM10, PAM40, PAM120, PAM250, BLOSUM62
• Filter (low complexity) PPPPP-gtXXXXX

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Extended BLAST Family

• Gapped Blast (default)
• PSI-Blast (Position-specific iterated blast)
• self generated scoring matrix
• PHI BLAST (motif plus BLAST)
• BLAST2 client (align two seqs)
• megablast (genomic sequence)
• rpsblast (search for domains)