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Lesson 2

Lesson 2. Aligning sequences and searching databases . Homology and sequence alignment. Homology. Homology = Similarity between objects due to a common ancestry. Hund = Dog, Schwein = Pig. Sequence homology. Similarity between sequences as a result of common ancestry. .

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Lesson 2

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  1. Lesson 2 Aligning sequences and searching databases

  2. Homology and sequence alignment.

  3. Homology Homology = Similarity between objects due to a common ancestry Hund = Dog, Schwein = Pig

  4. Sequence homology Similarity between sequences as a result of common ancestry. VLSPAVKWAKVGAHAAGHG ||| || |||| | |||| VLSEAVLWAKVEADVAGHG

  5. Sequence alignment Alignment:Comparing two (pairwise) or more (multiple) sequences. Searching for a series of identical or similar characters in the sequences.

  6. Why align? VLSPAVKWAKV ||| || |||| VLSEAVLWAKV • To detect if two sequences are homologous. If so, homology may indicate similarity in function (and structure). • Required for evolutionary studies (e.g., tree reconstruction). • To detect conservation (e.g., a tyrosine that is evolutionary conserved is more likely to be a phosphorylation site). • Given a sequenced DNA, from an unknown region, align it to the genome.

  7. Insertions, deletions, and substitutions

  8. Sequence alignment If two sequences share a common ancestor – for example human and dog hemoglobin, we can represent their evolutionary relationship using a tree VLSPAV-WAKV ||| || |||| VLSEAVLWAKV VLSEAVLWAKV VLSPAV-WAKV

  9. Perfect match A perfect match suggests that no change has occurred from the common ancestor (although this is not always the case). VLSPAV-WAKV ||| || |||| VLSEAVLWAKV VLSEAVLWAKV VLSPAV-WAKV

  10. A substitution A substitution suggests that at least one change has occurred since the common ancestor (although we cannot say in which lineage it has occurred). VLSPAV-WAKV ||| || |||| VLSEAVLWAKV VLSEAVLWAKV VLSPAV-WAKV

  11. Indel Option 1: The ancestor had L and it was lost here. In such a case, the event was a deletion. VLSEAVLWAKV VLSPAV-WAKV ||| || |||| VLSEAVLWAKV VLSEAVLWAKV VLSPAV-WAKV

  12. Indel Option 2: The ancestor was shorter and the L was inserted here. In such a case, the event was an insertion. L VLSEAVWAKV VLSPAV-WAKV ||| || |||| VLSEAVLWAKV VLSEAVLWAKV VLSPAV-WAKV

  13. Indel Normally, given two sequences we cannot tell whether it was an insertion or a deletion, so we term the event as an indel. Deletion? Insertion? VLSEAVLWAKV VLSPAV-WAKV

  14. Indels in protein coding genes Indels in protein coding genes are often of 3bp, 6bp, 9bp, etc... Gene Search In fact, searching for indels of length 3K (K=1,2,3,…) can help algorithms that search a genome for coding regions

  15. Global and Local pairwise alignments

  16. Global vs. Local Global alignment– finds the best alignment across the entire two sequences. Local alignment– finds regions of similarity in parts of the sequences. Global alignment: forces alignment in regions which differ ADLGAVFALCDRYFQ |||| |||| | ADLGRTQN-CDRYYQ Local alignment will return only regions of good alignment ADLG CDRYFQ |||| |||| | ADLG CDRYYQ

  17. Global alignment PTK2 protein tyrosine kinase 2 of human and rhesus monkey

  18. Proteins are comprised of domains Human PTK2 : Domain A Domain B Protein tyrosine kinase domain

  19. Protein tyrosine kinase domain In leukocytes, a different gene for tyrosine kinase is expressed. Domain A Domain X Protein tyrosine kinase domain

  20. The sequence similarity is restricted to a single domain PTK2 Domain A Protein tyrosine kinase domain Domain B Domain X Protein tyrosine kinase domain Leukocyte TK

  21. Global alignment of PTK and LTK X

  22. Local alignment of PTK and LTK

  23. Conclusions Use global alignment when the two sequences share the same overall sequence arrangement. Use local alignment to detect regions of similarity.

  24. How alignments are computed

  25. Pairwise alignment AAGCTGAATTCGAA AGGCTCATTTCTGA One possible alignment: AAGCTGAATT-C-GAA AGGCT-CATTTCTGA-

  26. AAGCTGAATT-C-GAA AGGCT-CATTTCTGA- This alignment includes: 2mismatches 4 indels (gap) 10 perfect matches

  27. Choosing an alignment for a pair of sequences Many different alignments are possible for 2 sequences: AAGCTGAATTCGAA AGGCTCATTTCTGA A-AGCTGAATTC--GAA AG-GCTCA-TTTCTGA- AAGCTGAATT-C-GAA AGGCT-CATTTCTGA- Which alignment is better?

  28. Scoring system (naïve) Perfect match: +1 Mismatch: -2 Indel (gap): -1 AAGCTGAATT-C-GAA AGGCT-CATTTCTGA- A-AGCTGAATTC--GAA AG-GCTCA-TTTCTGA- Score: =(+1)x10 + (-2)x2 + (-1)x4= 2 Score: =(+1)x9 + (-2)x2 + (-1)x6 = -1 Higher score  Better alignment

  29. Alignment scoring - scoring of sequence similarity: • Assumes independence between positions: • each position is considered separately • Scores each position: • Positive if identical (match) • Negative if different (mismatch or gap) • Total score = sum of position scores • Can be positive or negative

  30. Scoring systems

  31. Scoring system • In the example above, the choice of +1 for match,-2 for mismatch, and -1 for gap is quite arbitrary • Different scoring systems  different alignments • We want a good scoring system…

  32. Scoring matrix • Representing the scoring system as a table or matrix n X n (n is the number of letters the alphabet contains. n=4 for nucleotides, n=20 for amino acids) • symmetric

  33. DNA scoring matrices Uniform substitutions between all nucleotides: Match Mismatch

  34. DNA scoring matrices Can take into account biological phenomena such as: Transition-transversion

  35. Amino-acid scoring matrices Take into account physico-chemical properties

  36. Scoring gaps (I) In advanced algorithms, two gaps of one amino-acid are given a different score than one gap of two amino acids. This is solved by giving a penalty to each gap that is opened. Gap extension penalty < Gap opening penalty

  37. Scoring gaps (II) The dependency between the penalty and the length of the gap need not to be linear. AGGGTTC—GA AGGGTTCTGA Score = -2 AGGGTT-—GA AGGGTTCTGA Score = -4 Linear penalty AGGGT--—GA AGGGTTCTGA Score = -6 AGGG---—GA AGGGTTCTGA Score = -8

  38. Scoring gaps (II) The dependency between the penalty and the length of the gap need not to be linear. AGGGTTC—GA AGGGTTCTGA Score = -4 AGGGTT-—GA AGGGTTCTGA Score = -6 Non-linear penalty AGGGT--—GA AGGGTTCTGA Score = -7 AGGG---—GA AGGGTTCTGA Score = -8

  39. PAM AND BLOSUM

  40. Amino-acid substitution matrices Actual substitutions: Based on empirical data Commonly used by many bioinformatics programs PAM & BLOSUM

  41. Protein matrices – actual substitutions The idea: Given an alignment of a large number of closely related sequences we can score the relation between amino acids based on how frequently they substitute each other M G Y D E M G Y D E M G Y E E M G Y D E M G Y Q E M G Y D E M G Y E E M G Y E E In the fourth column E and D are found in 7 / 8

  42. PAM Matrix - Point Accepted Mutations • The Dayhoff PAM matrix is based on a database of 1,572 changes in 71 groups of closely related proteins (85% identity => Alignment was easy and reliable). • Counted the number of substitutions per amino-acid pair (20 x 20) • Found that common substitutions occurred between chemically similar amino acids

  43. PAM Matrices Family of matrices PAM 80, PAM 120, PAM 250 The number on the PAM matrix represents evolutionary distance Larger numbers are for larger distances

  44. Example: PAM 250 Similar amino acids have greater score

  45. PAM - limitations Based only on a single, and limited dataset Examines proteins with few differences (85% identity) Based mainly on small globular proteins so the matrix is biased

  46. BLOSUM Henikoff and Henikoff (1992) derived a set of matrices based on a much larger dataset BLOSUM observes significantly more replacements than PAM, even for infrequent pairs

  47. BLOSUM:BlocksSubstitutionMatrix Based on BLOCKS database ~2000 blocks from 500 families of related proteins Families of proteins with identical function Blocks are short conserved patterns of 3-60 amino acids without gaps AABCDA----BBCDA DABCDA----BBCBB BBBCDA-AA-BCCAA AAACDA-A--CBCDB CCBADA---DBBDCC AAACAA----BBCCC

  48. BLOSUM Each block represents a sequence alignment with different identity percentage For each block the amino-acid substitution rates were calculated to create the BLOSUM matrix

  49. BLOSUM Matrices BLOSUMn is based on sequences that share at least n percent identity BLOSUM62 represents closer sequences than BLOSUM45

  50. Example : Blosum62 Derived from blocks where the sequences share at least 62% identity

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