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Pairwise Sequence Alignment
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WHAT?
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WHAT? Given any two sequences (DNA or protein) Seq 1: Seq 2:
CATATTGCAGTGGTCCCGCGTCAGGCT Seq 2: TAAATTGCGTGGTCGCACTGCACGCT we are interested to know to what extent they are similar? CATATTGCAGTGGTCCCGCGTCAGGCT TAAATTGCGT-GGTCGCACTGCACGCT
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WHY?
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Discover function Study evolution Find crucial features within a sequence Identify cause of diseases
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Discover function Sequences that are similar probably have the same function
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Study evolution If two sequences from different organisms are similar , they may have a common ancestor
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Find crucial features Conservation of the IGFALS (Insulin-like growth factor) Between human and mouse. Regions in the sequences that are strongly conserved between different sequences can indicate their functional importance CATATTGCAGTGGTCCCGCGTCAGGCT TAAATTGCGT-GGTCGCACTGCACGCT
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Identify cause of disease
Comparison of sequences between individuals can detect changes that are related to diseases
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Sickle Cell Anemia Due to 1 swapping an A for a T, causing inserted amino acid to be valine instead of glutamine in hemoglobin Image source:
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Healthy Individual >gi| |ref|NM_ | Homo sapiens hemoglobin, beta (HBB), mRNA ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA GGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGC AGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATG CTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGC TCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGAT CCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCA CCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCA CTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACT GGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC >gi| |ref|NP_ | beta globin [Homo sapiens] MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH
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Diseased Individual >gi| |ref|NM_ | Homo sapiens hemoglobin, beta (HBB), mRNA ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA GGTGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGC AGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATG CTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGC TCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGAT CCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCA CCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCA CTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACT GGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC >gi| |ref|NP_ | beta globin [Homo sapiens] MVHLTPVEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH
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How do sequences change?
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Sequence Modifications
Three types of changes Substitution (point mutation) Insertion Deletion Indel (replication slippage) TCCGT TCAGT TCAGT TCGAGT TCGT
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In order to align two sequences we need a quantitive model to evaluate similarity between sequences.
How do we quantitate sequence similarity ?
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Substitutions Only not including indels
Sequences compared base-by-base Count the number of matches and mismatches For example :Matches score +2, Mismatches score -1 TTCGTCGTAGTCGGCTCGACCTG GTACGTCTAGCGAGCGTGATCCT 9 matches +18 14 mismatches -14 Total score +4 A weak match
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TT-CGTCGTAGTCG-GC-TCGACC-TG GTACGTC-TAG-CGAGCGT-GATCCT-
Including Indels Create an ‘alignment’ Count matches within alignment Indels are scored as mismatches -1 TT-CGTCGTAGTCG-GC-TCGACC-TG GTACGTC-TAG-CGAGCGT-GATCCT- 17 matches +34 2 mismatches - 2 8 indels - 8 Total score +24 A strong match
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Choosing an Alignment Many different alignments are possible Should consider all possible Take the best score found There may be more than one best alignment TT-CGTCGTAGTCG-GC-TCGACC-TG GTACGTC-TAG-CGAGCGT-GATCCT- +24 -TTCGT-CGTAGTC-GGCTCG-ACCTG GTAC-GTCTA-GCGAGCGT-GATCC-T
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Why is it hard ? Alignment requires an algorithm that performs a number of comparisons roughly proportional to the square of the average sequence length n2.
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Dynamic Programming A method for reducing a complex problem
to a set of identical sub-problems The best solution to one sub-problem is independent from the best solution to the other sub-problem
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Dynamic Programming A method for reducing a complex problem
to a set of identical sub-problems The best solution to one sub-problem is independent from the best solution to the other sub-problem
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What does it mean? If a path from X→Z passes through Y, the best path from X→Y is independent of the best path from Y→Z
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Sequence Global Alignment Needleman-Wunsch
Sequences: A = ACGCTG, B = CATGT A C G C T G 1 2 3 4 5 6 C 1 A 2 T 3 G 4 T Z 5
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Example -1 2 2 3 4 5 -2 ? Sequences: A = ACGCTG, B = CATGT
Score of best alignment between AC and CATG 2 …between ACG and CATG 2 3 4 5 -2 …between AC and CATGT Calculate score between ACG and CATGT ? Match:+2, Other:-1
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Example 2 3 4 5 Align the next Insertion in the letter in the
sequences Insertion in the first sequence (del) 3 5 2 3 4 5 - 5 Insertion in the Second sequence 3 -
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Example 2 3 4 5 -1 2 1 -2 Sequences: A = ACGCTG, B = CATGT
-1 from before plus -1 for mismatch of G against T -2 2 from before plus -1 for mismatch of – against T 1 2 3 4 5 -1 2 -2 from before plus -1 for mismatch of G against – -3 1 Cell gets highest score of -2,1,-3 1 -2 Sequences: A = ACGCTG, B = CATGT
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Sequences: A = ACGCTG, B = CATGT
Example 2 3 4 5 1 -1 2 -2 Sequences: A = ACGCTG, B = CATGT
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A 1 C 2 G 3 4 T 5 6 0 C 1 A 2 T 3 G 4 T 5
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A 1 C 2 G 3 4 T 5 6 0 -1 C 1 A 2 T 3 G 4 T 5 A -
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ACGCTG ------ A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4
A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4 T 5 ACGCTG ------
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----- CATGT A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4
A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4 T 5 ----- CATGT
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A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4 T 5 A C
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A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4 T 5 AC -C
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A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4 T 5 ACG -C-
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ACGC ---C ACGC -C-- A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2
A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4 T 5 ACGC ---C ACGC -C--
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A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4 T 5 ACG -CA
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A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4 T 5
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A 1 C 2 G 3 4 T 5 6 0 -1 -2 -3 -4 -5 -6 C 1 A 2 T 3 G 4 T 5
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A 1 C 2 G 3 4 T 5 6 0 -1 C 1 A 2 T 3 G 4 T 5
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A 1 C 2 G 3 4 T 5 6 0 -1 C 1 A 2 T 3 G 4 T 5 ACGCTG- -C-ATGT
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A 1 C 2 G 3 4 T 5 6 0 -1 C 1 A 2 T 3 G 4 T 5 ACGCTG- -CA-TGT
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A 1 C 2 G 3 4 T 5 6 0 -1 C 1 A 2 T 3 G 4 T 5 -ACGCTG CATG-T-
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Needleman-Wunsch Alignment
Summary Needleman-Wunsch Alignment Global alignment between sequences Compare entire sequence against another Create scoring table Sequence A across top, B down left Cell at column i and row j contains the score of best alignment between the first i elements of A and the first j elements of B Global alignment score is bottom right cell
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Global vs. Local alignment
DorothyHodkin DorothyCrowfootHodkin DOROTHY HODGKIN Global alignment: DOROTHY HODGKIN DOROTHYCROWFOOTHODGKIN Local alignment:
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Local Alignment Smith-Waterman
Best score for aligning part of sequences Often beats global alignment score Global Alignment ATTGCAGTG-TCGAGCGTCAGGCT ATTGCGTCGATCGCAC-GCACGCT Local Alignment CATATTGCAGTGGTCCCGCGTCAGGCT TAAATTGCGT-GGTCGCACTGCACGCT
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Global vs. Local alignment
Alignment of two Genomic sequences >Human DNA CATGCGACTGACcgacgtcgatcgatacgactagctagcATCGATCATA >Mouse DNA CATGCGTCTGACgctttttgctagcgatatcggactATCGATATA
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Global vs. Local alignment
Alignment of two Genomic sequences Global Alignment Human:CATGCGACTGACcgacgtcgatcgatacgactagctagcATCGATCATA Mouse:CATGCGTCTGACgct---ttttgctagcgatatcggactATCGAT-ATA ****** ***** * *** * ****** *** Human:CATGCGACTGAC Mouse:CATGCGTCTGAC Human:ATCGATCATA Mouse:ATCGAT-ATA Local Alignment
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Global vs. Local alignment
Alignment of two Genomic DNA and mRNA >Human DNA CATGCGACTGACcgacgtcgatcgatacgactagctagcATCGATCATA >Human mRNA CATGCGACTGACATCGATCATA
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Global vs. Local alignment
Alignment of two Genomic DNA and mRNA Global Alignment DNA: CATGCGACTGACcgacgtcgatcgatacgactagctagcATCGATCATA mRNA:CATGCGACTGAC ATCGATCATA ************ ********** DNA: CATGCGACTGAC mRNA:CATGCGACTGAC DNA: ATCGATCATA mRNA:ATCGATCATA Local Alignment
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