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Pairwise Sequence Alignment: Understanding Similarity and Function

Learn how to quantify sequence similarity, discover functions, and identify disease causes through pairwise sequence alignment. Understand the importance of aligning sequences, detecting changes, and using dynamic programming for solutions.

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Pairwise Sequence Alignment: Understanding Similarity and Function

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  1. Lecture 2 Pairwise Sequence Alignment

  2. WHAT?

  3. WHAT? • Given any two sequences (DNA or protein) Seq 1: CATATTGCAGTGGTCCCGCGTCAGGCT Seq 2: TAAATTGCGTGGTCGCACTGCACGCT we are interested to know to what extent they are similar? CATATTGCAGTGGTCCCGCGTCAGGCT TAAATTGCGT-GGTCGCACTGCACGCT

  4. WHY?

  5. Discover function • Study evolution • Find crucial features within a sequence • Identify cause of diseases

  6. Discover function Sequences that are similar probably have the same function

  7. in the genome Find crucial features ? • Regions in the sequences that are strongly conserved between different sequences can indicate their functional importance High Low

  8. Identify cause of disease • Comparison of sequences between individuals can detect changes that are related to diseases

  9. 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: http://www.cc.nih.gov/ccc/ccnews/nov99/

  10. Healthy Individual >gi|28302128|ref|NM_000518.4| Homo sapiens hemoglobin, beta (HBB), mRNA ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA GGAGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGC AGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATG CTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGC TCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGAT CCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCA CCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCA CTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACT GGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC >gi|4504349|ref|NP_000509.1| beta globin [Homo sapiens] MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH

  11. Diseased Individual >gi|28302128|ref|NM_000518.4| Homo sapiens hemoglobin, beta (HBB), mRNA ACATTTGCTTCTGACACAACTGTGTTCACTAGCAACCTCAAACAGACACCATGGTGCATCTGACTCCTGA GGTGAAGTCTGCCGTTACTGCCCTGTGGGGCAAGGTGAACGTGGATGAAGTTGGTGGTGAGGCCCTGGGC AGGCTGCTGGTGGTCTACCCTTGGACCCAGAGGTTCTTTGAGTCCTTTGGGGATCTGTCCACTCCTGATG CTGTTATGGGCAACCCTAAGGTGAAGGCTCATGGCAAGAAAGTGCTCGGTGCCTTTAGTGATGGCCTGGC TCACCTGGACAACCTCAAGGGCACCTTTGCCACACTGAGTGAGCTGCACTGTGACAAGCTGCACGTGGAT CCTGAGAACTTCAGGCTCCTGGGCAACGTGCTGGTCTGTGTGCTGGCCCATCACTTTGGCAAAGAATTCA CCCCACCAGTGCAGGCTGCCTATCAGAAAGTGGTGGCTGGTGTGGCTAATGCCCTGGCCCACAAGTATCA CTAAGCTCGCTTTCTTGCTGTCCAATTTCTATTAAAGGTTCCTTTGTTCCCTAAGTCCAACTACTAAACT GGGGGATATTATGAAGGGCCTTGAGCATCTGGATTCTGCCTAATAAAAAACATTTATTTTCATTGC >gi|4504349|ref|NP_000509.1| beta globin [Homo sapiens] MVHLTPVEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLG AFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVAN ALAHKYH

  12. How do sequences change?

  13. Indel (replication slippage) TCCGT TCGAGT TCAGT TCGT Sequence Modifications • Three types of changes • Substitution (point mutation) • Insertion • Deletion TCAGT

  14. In order to align two sequences we need a quantitive model to evaluate similarity between sequences. How do we quantitate sequence similarity ? For example : A and A , score= 2 A and T , score= -1

  15. Total score +4 A weak match Substitutions Only Modelnot including indels • Sequences compared base-by-base • Count the number of matches and mismatches • For example :Matches score +2, Mismatches score -1 TTCGTCGTAGTCGGCTCGACCTGGTACGTCTAGCGAGCGTGATCCT 9 matches +18 14 mismatches -14

  16. Total score +24 A strong match Including Indels • Create an ‘alignment’ • Count matches within alignment • Indels are scored as mismatches -1 TT-CGTCGTAGTCG-GC-TCGACC-TGGTACGTC-TAG-CGAGCGT-GATCCT- 17 matches +34 2 mismatches - 2 8 indels - 8

  17. TT-CGTCGTAGTCG-GC-TCGACC-TGGTACGTC-TAG-CGAGCGT-GATCCT- +24 -TTCGT-CGTAGTC-GGCTCG-ACCTGGTAC-GTCTA-GCGAGCGT-GATCC-T 0 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

  18. 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.

  19. 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

  20. 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

  21. 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

  22. 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

  23. Score of best alignment between AC and CATG …between ACG and CATG -1 2 …between AC and CATGT Calculate score between ACG and CATGT -2 ? Example Sequences: A = ACGCTG, B = CATGT Match:+2, Other:-1

  24. Example Align the next letter in the sequences Insertion in the first sequence (del) 3 5 - 5 Insertion in the Second sequence 3 -

  25. -1 from before plus -1 for mismatch of G against T-2 2 from before plus -1 for mismatch of – against T1 -2 from before plus -1 for mismatch of G against –-3 Cell gets highest score of -2,1,-31 1 Example -1 2 -2 Sequences: A = ACGCTG, B = CATGT

  26. Example -1 2 -2 Sequences: A = ACGCTG, B = CATGT

  27. A -

  28. ACGCTG ------

  29. ----- CATGT

  30. A C

  31. AC -C

  32. ACG -C-

  33. ACGC ---C ACGC -C--

  34. ACG -CA

  35. ACGCTG- -C-ATGT

  36. ACGCTG- -CA-TGT

  37. -ACGCTG CATG-T-

  38. Needleman-Wunsch Global Alignment • Compare entire sequence against another • Global alignment score is bottom right cell

  39. Do we always want to do a GLOBAL alignment???

  40. DorothyHodkin DorothyCrowfootHodkin Dorothy Hodkin DorothyCrowfootHodkin DOROTHY DOROTHY HODGKIN HODGKIN Global alignment: DOROTHY--------HODGKIN DOROTHYCROWFOOTHODGKIN Local alignment:

  41. Local AlignmentSmith-Waterman • Best score for aligning part of sequences • Often beats global alignment score Global Alignment ATTGCAGTG-TCGAGCGTCAGGCT ATTGCGTCGATCGCAC-GCACGCT Local Alignment CATATTGCAGTGGTCCCGCGTCAGGCT TAAATTGCGT-GGTCGCACTGCACGCT

  42. Global vs. Local alignment Alignment of two Genomic sequences >Human DNA CATGCGACTGACcgacgtcgatcgatacgactagctagcATCGATCATA >Mouse DNA CATGCGTCTGACgctttttgctagcgatatcggactATCGATATA

  43. 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

  44. Global vs. Local alignment Alignment of two Genomic DNA and mRNA >Human DNA CATGCGACTGACcgacgtcgatcgatacgactagctagcATCGATCATA >Human mRNA CATGCGACTGACATCGATCATA

  45. 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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