Pairwise Alignment

How do we tell whether two sequences are

similar?

BIO520 Bioinformatics Jim Lund

Assigned reading:Ch 4.1-4.7, Ch 5.1, get what you can out of 5.2, 5.4

Pairwise alignment

• DNA:DNA

• polypeptide:polypeptide

The BASIC Sequence Analysis Operation

Alignments

• Pairwise sequence alignments

–One-to-One

–One-to-Database• Multiple sequence alignments

–Many-to-Many

Origins of Sequence Similarity

• Homology– common evolutionary descent

• Chance– Short similar segments are very

common.

• Similarity in function– Convergence (very rare)

Visual sequence comparison: Dotplot

Visual sequence comparison: Filtered dotplot

4 bp window, 75% identity cutoff

Visual sequence comparison: Dotplot

4 bp windw, 75% identity cutoff

Dotplots of sequence rearrangements

Assessing similarity

GAACAAT||||||| 7/7 OR 100%GAACAAT

GAACAAT | 1/7 or 14%GAACAAT

Which is BETTER?How do we SCORE?

Similarity

GAACAAT||||||| 7/7 OR 100%GAACAAT

GAACAAT||| ||| 6/7 OR 84%GAATAAT

MISMATCH

Mismatches

GAACAAT||| ||| 6/7 OR 84%GAATAAT

GAACAAT||| ||| 6/7 OR 84%GAAGAAT

Terminal Mismatch

GAACAATttttt ||| |||aaaccGAATAAT 6/7 OR 84%

INDELS

GAAgCAAT||| |||| 7/7 OR 100%GAA*CAAT

Indels, cont’d

GAAgCAAT||| ||||GAA*CAAT

GAAggggCAAT||| ||||GAA****CAAT

Similarity Scoring

Common Method: • Terminal mismatches (0)• Match score (1)• Mismatch penalty (-3)• Gap penalty (-1)• Gap extension penalty (-1)

DNA Defaults

DNA Scoring

GGGGGGAGAA

|||||*|*|| 8(1)+2(-3)=22GGGGGAAAAAGGGGG

GGGGGGAGAA--GGG

|||||*|*|| ||| 11(1)+2(-3)+1(-1)+1(-1)=33GGGGGAAAAAGGGGG

Absurdity of Low Gap Penalty

GATCGCTACGCTCAGC A.C.C..C..T

Perfect similarity,Every time!

Sequence alignment algorithms

• Local alignment– Smith-Waterman

• Global alignment– Needleman-Wunsch

Alignment Programs

• Local alignment (Smith-Waterman)– BLAST (simplified Smith-Waterman)

– FASTA (simplified Smith-Waterman)

– BESTFIT (GCG program)

• Global alignment (Needleman-Wunsch)– GAP

Local vs. global alignment

10 gaggc 15 ||||| 3 gaggc 7

1 gggggaaaaagtggccccc 19 || |||| ||1 gggggttttttttgtggtttcc 22

Global alignment: alignment of the full length of the sequences

Local alignment: alignment of regions of substantial similarity

Local vs. global alignment

BLAST Algorithm

Look for local alignment, a High Scoring Pair (HSP)• Finding word (W) in query and subject. Score > T.• Extend local alignment until score reaches

maximum-X.• Keep High Scoring Segment Pairs (HSPs) with

scores > S.• Find multiple HSPs per query if present• Expectation value (E value) using Karlin-Altschul

stats

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 seqeunceN = Size of databasek = Search space scaling parameterLambda = scoring scaling parameterS = BLAST HSP score

Low E -> good match

BLAST statistical significance:

Rule of thumb for a good match:

•Nucleotide match•E < 1e-6•Identity > 70%

•Protein match•E < 1e-3•Identity > 25%

Protein Similarity Scoring

• Identity - Easy• WEAK Alignments• Chemical Similarity

– L vs I, K vs R…

• Evolutionary Similarity–How do proteins evolve?–How do we infer similarities?

BLOSUM62

C S T P A G N D C 9 -1 -1 -3 0 -3 -3 -3 S -1 4 1 -1 1 0 1 0 T -1 1 4 1 -1 1 0 1 P -3 -1 1 7 -1 -2 -1 -1 A 0 1 -1 -1 4 0 -1 -2 G -3 0 1 -2 0 6 -2 -1 N -3 1 0 -2 -2 0 6 1 D -3 0 1 -1 -2 -1 1 6

Single-base evolution changes the encoded

AACAU=HCAU=H

CAC=H CGU=R UAU=Y

CAA=Q CCU=P GAU=D

CAG=Q CUU=L AAU=N

Substitution Matrices

Two main classes:

• PAM-Dayhoff

• BLOSUM-Henikoff

PAM-Dayhoff

• Built from closed related proteins, substitutions constrained by evolution and function

• “accepted” by evolution (Point Accepted Mutation=PAM)

• 1 PAM::1% divergence• PAM120=closely related proteins

• PAM250=divergent proteins

BLOSUM-Henikoff&Henikoff

• Built from ungapped alignments in proteins: “BLOCKS”

• Merge blocks at given % similar to one sequence

• Calculate “target” frequencies

• BLOSUM62=62% similar blocks– good general purpose

• BLOSUM30– Detects weak similarities, used for distantly related proteins

BLOSUM62

C S T P A G N D C 9 -1 -1 -3 0 -3 -3 -3 S -1 4 1 -1 1 0 1 0 T -1 1 4 1 -1 1 0 1 P -3 -1 1 7 -1 -2 -1 -1 A 0 1 -1 -1 4 0 -1 -2 G -3 0 1 -2 0 6 -2 -1 N -3 1 0 -2 -2 0 6 1 D -3 0 1 -1 -2 -1 1 6

Gapped alignments

• No general theory for significance of matches!!

• G+L(n) – indel mutations rare

– variation in gap length “easy”, G > L

Real Alignments

Phylogeny

1 MGLSDGEWQLVLNAWGKVEADVAGHGQEVLIRLFTGHPETLEKFDKFKHL 50 ||||||||||||| |||||||||||||||||||| ||||||||||||||| 1 MGLSDGEWQLVLNVWGKVEADVAGHGQEVLIRLFKGHPETLEKFDKFKHL 50 . . . . . 51 KTEAEMKASEDLKKHGNTVLTALGGILKKKGHHEAEVKHLAESHANKHKI 100 |.| ||||||||||||||||||||||||||||||||. ||:||| |||| 51 KSEDEMKASEDLKKHGNTVLTALGGILKKKGHHEAELTPLAQSHATKHKI 100 . . . . . 101 PVKYLEFISDAIIHVLHAKHPSDFGADAQAAMSKALELFRNDMAAQYKVL 150 |||||||||:||| || .||| ||||||| |||||||||||||||.|| | 101 PVKYLEFISEAIIQVLQSKHPGDFGADAQGAMSKALELFRNDMAAKYKEL 150

151 GFHG 154 || | 151 GFQG 154

Cow-to-Pig Protein

Cow-to-Pig cDNA 1 CAGCTGTCGGAGACAGACACCCAGTCAGTCCCGCCCTTGTTCTTTTTCTC 50 | ||| ||| || | ||||| |||| ||| |||||| 1 .......CAGAGCCAGGACACCCAGTACGCCCGCACTTGCTCTGTTTCTC 43 . . . . . 51 TTCTTCAGACTGCGCCATGGGGCTCAGCGACGGGGAATGGCAGTTGGTGC 100 |||| ||||||| |||||||||||||||||||||||||||||| |||||| 44 TTCTGCAGACTGTGCCATGGGGCTCAGCGACGGGGAATGGCAGCTGGTGC 93 . . . . . 101 TGAATGCCTGGGGGAAGGTGGAGGCTGATGTCGCAGGCCATGGGCAGGAG 150 |||| | ||||||||||||||||||||||||||||||||||||||||||| 94 TGAACGTCTGGGGGAAGGTGGAGGCTGATGTCGCAGGCCATGGGCAGGAG 143 . . . . . 151 GTCCTCATCAGGCTCTTCACAGGTCATCCCGAGACCCTGGAGAAATTTGA 200 ||||||||||||||||| | ||||| ||||||||||||||||||||||| 144 GTCCTCATCAGGCTCTTTAAGGGTCACCCCGAGACCCTGGAGAAATTTGA 193 . . . . . 201 CAAGTTCAAGCACCTGAAGACAGAGGCTGAGATGAAGGCCTCCGAGGACC 250 |||||| |||||||||||| |||||| ||||||||||||||| ||||||| 194 CAAGTTTAAGCACCTGAAGTCAGAGGATGAGATGAAGGCCTCTGAGGACC 243

80% Identity (88% at aa!)

DNA similarity reflects polypeptide similarity

101 TGAATGCCTGGGGGAAGGTGGAGGCTGATGTCGCAGGCCATGGGCAGGAG 150 |||| | ||||||||||||||||||||||||||||||||||||||||||| 94 TGAACGTCTGGGGGAAGGTGGAGGCTGATGTCGCAGGCCATGGGCAGGAG 143

501 CCAGTACAAGGTGCTGGGCTTCCATGGCTAAGCCCCACCCCTGTGCCCCT 550 | ||||||||| |||||||||||| ||||||||||| | | || | 494 CAAGTACAAGGAGCTGGGCTTCCAGGGCTAAGCCCCCCAGACGCCCCTCA 543 . . . . .

Coding vs Non-coding Regions

451 CAGGCTGCCATGAGCAAGGCCCTGGAACTGTTCCGGAATGACATGGCTGC 500 |||| ||||||||||||||||||||||| |||||||| |||||||| || 444 CAGGGAGCCATGAGCAAGGCCCTGGAACTCTTCCGGAACGACATGGCGGC 493 . . . . . 501 CCAGTACAAGGTGCTGGGCTTCCATGGCTAAGCCCCACCCCTGTGCCCCT 550 | ||||||||| |||||||||||| ||||||||||| | | || | 494 CAAGTACAAGGAGCTGGGCTTCCAGGGCTAAGCCCCCCAGACGCCCCTCA 543 . . . . . 551 CAC.CCCACCCACCTGGG...........CAGGGTGGGCGGGGACTGAAT 588 | | |||| |||| |||| | || ||| ||| ||||| 544 CCCACCCATCCACTTGGGCCAGGGCCCCCCGCGGAGGGTGGGCGCTGAAG 593 . . . . . 589 CCCAAGTAGTTATAGGGTTTGCTTCTGAGTGTGTGCTTTGTTTAGGAGAG 638 | | |||| | |||||||||||||||||||| ||||||||| | ||||| 594 CTCCTGTAGCTGTAGGGTTTGCTTCTGAGTGT.TGCTTTGTTCATGAGAG 642 . . . . . 639 GTGGGTGGAAGAGGTGGATGGGTTAGGGGTGGAGG............... 673 |||||||| ||||||||| ||| | | ||||| || 643 GTGGGTGGGAGAGGTGGAGGGGCTGGTGGTGGTGGTGGGGGGGTGTTCAG 692

90% in coding (70% in non-coding)

Third Base of Codon is Hypervariable

201 CAAGTTCAAGCACCTGAAGACAGAGGCTGAGATGAAGGCCTCCGAGGACC 250 ||||||*||||||||||||*||||||*|||||||||||||||*||||||| 194 CAAGTTTAAGCACCTGAAGTCAGAGGATGAGATGAAGGCCTCTGAGGACC 243 . . . . . 251 TGAAGAAGCATGGCAACACGGTGCTCACGGCCCTGGGGGGTATCCTGAAG 300 ||||||||||*||||||||||||||*||*|||||||||||*|||||*||| 244 TGAAGAAGCACGGCAACACGGTGCTGACTGCCCTGGGGGGCATCCTTAAG 293

Cow-to-Fish Protein

1 MGLSDGEWQLVLNAWGKVEADVAGHGQEVLIRLFTGHPETLEKFDKFKHL 50 :. :|| || .||| | || || |||| |||||. | || : 1 ....MADFDMVLKCWGPMEADHATHGSLVLTRLFTEHPETLKLFPKFAGI 46 . . . . . 51 KTEAEMKASEDLKKHGNTVLTALGGILKKKGHHEAEVKHLAESHANKHKI 100 :: . || ||| || :|| :| | | .| |. ||| |||| 47 .AHGDLAGDAGVSAHGATVLNKLGDLLKARGAHAALLKPLSSSHATKHKI 95 . . . . . 101 PVKYLEFISDAIIHVLHAKHPSDFGADAQAAMSKALELFRNDMAAQYKVL 150 |: . |.: | |: | | | | |: : : || | || | 96 PIINFKLIAEVIGKVMEEKAGLD..AAGQTALRNVMAIIITDMEADYKEL 143

151 GFHG 154 || 144 GFTE 147

42% identity, 51% similarity

Cow-to-Fish DNA

32 .ACAGGACATTTTACTACTCTGCAGATAATGGCTGACTTTGACATGGTAC 80 | | | | | | || | | || | | |||| | 51 TTCTTCAGACTGCGCCATGGGGCTCAGCGACGGGGAATGGCAGTTGGTGC 100 . . . . . 81 TGAAGTGCTGGGGTCCAATGGAGGCGGACCACGCAACCCACGGGAGTCTG 130 |||| |||||| ||||||| || |||| ||| ||| | 101 TGAATGCCTGGGGGAAGGTGGAGGCTGATGTCGCAGGCCATGGGCAGGAG 150 . . . . . 131 GTGCTGACCCGTTTATTCACAGAGCACCCAGAAACCCTAAAGTTATTCCC 180 || || | | | | ||||||| || || || ||||| || ||| 151 GTCCTCATCAGGCTCTTCACAGGTCATCCCGAGACCCTGGAGAAATTTGA 200 . . . . . 181 CAAGTTTGCTGGC...ATCGCCCATGGGGACCTGGCCGGGGATGCAGGTG 227 |||||| | | | | | || || | | | 201 CAAGTTCAAGCACCTGAAGACAGAGGCTGAGATGAAGGCCTCCGAGGACC 250

48% similarity

Protein vs. DNAAlignments

• Polypeptide similarity > DNA• Coding DNA > Non-coding

• 3rd base of codon hypervariable• Moderate Distance poor DNA similarity

Rules of Thumb

• DNA-DNA similarities– 50% significant if “long”

– E < 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 match…could be a “motif”

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)

DNA Databases

• nr (non-redundantish merge of Genbank, EMBL, etc…)– EXCLUDES HTGS0,1,2, EST, GSS, STS, PAT, WGS

• est (expressed sequence tags)• htgs (high throughput genome seq.)• gss (genome survey sequence)• vector, yeast, ecoli, mito• chromosome (complete genomes)• And more

http://www.ncbi.nlm.nih.gov/BLAST/blastcgihelp.shtml#nucleotide_databases

Protein Databases

• nr (non-redundant Swiss-prot, PIR, PDF, PDB, Genbank CDS)

• swissprot

• ecoli, yeast, fly

• month

• And more

BLAST Input

• Program

• Database

• Options - see more

• Sequence– FASTA

– gi or accession#

BLAST Options

• Algorithm and output options– # descriptions, # alignments returned– Probability cutoff– Strand

• Alignment parameters– Scoring Matrix

• PAM30, PAM70, BLOSUM45, BLOSUM62BLOSUM62, BLOSUM80, BLOSUM80

– Filter (low complexity) PPPPP->XXXXX

Extended BLAST Family

• Gapped Blast (default)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)