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- 1. Pairwise Sequence Alignment Misha Kapushesky Slides: Stuart M. Brown, Fourie Joubert, NYU St. Petersburg Russia 2010
- 2. Protein Evolution “For many protein sequences, evolutionary history can be traced back 1-2 billion years” -William Pearson • When we align sequences, we assume that they share a common ancestor • They are then homologous • Protein fold is much more conserved than protein sequence • DNA sequences tend to be less informative than protein sequences
- 3. Definition • Homology: related by descent • Homologous sequence positions ATTGCGC à ATTGCGC à ATCCGCC ATTGCGC AT-CCGCà ATTGCGC
- 4. Orthologous and paralogous • Orthologous sequences differ because they are found in different species (a speciation event) • Paralogous sequences differ due to a gene duplication event • Sequences may be both orthologous and paralogous
- 5. Pairwise Alignment • The alignment of two sequences (DNA or protein) is a relatively straightforward computational problem. • There are lots of possible alignments. • Two sequences can always be aligned. • Sequence alignments have to be scored. • Often there is more than one solution with the same score. •
- 6. Methods of Alignment • By hand - slide sequences on two lines of a word processor • Dot plot • with windows • Rigorous mathematical approach • Dynamic programming (slow, optimal) • Heuristic methods (fast, approximate) • BLAST and FASTA • Word matching and hash tables0
- 7. Align by Hand GATCGCCTA_TTACGTCCTGGAC <-- --> AGGCATACGTA_GCCCTTTCGC You still need some kind of scoring system to find the best alignment
- 8. Percent Sequence Identity • The extent to which two nucleotide or amino acid sequences are invariant A C C T G A G – A G A C G T G – G C A G 70% identical mismatch indel
- 9. Dotplot: A l l l l T l l l l T l l l l C l l l A l l l l C l l l A l l l l T l l l l A l l l l T A C A T T A C G T A C Sequence 1 Sequence 2 A dotplot gives an overview of all possible alignments
- 10. Dotplot: A l l l l T l l l l T l l l l C l l l A l l l l C l l l A l l l l T l l l l A l l l l T A C A T T A C G T A C T A C A T T A C G T A C A T A C A C T T A Sequence 1 Sequence 2 One possible alignment: In a dotplot each diagonal corresponds to a possible (ungapped) alignment
- 11. Insertions / Deletions in a Dotplot T A C T G T C A T T A C T G T T C A T Sequence 1 Sequence 2 T A C T G - T C A T | | | | | | | | | T A C T G T T C A T
- 12. Hemoglobin α-chain Hemoglobin β-chain Dotplot (Window = 130 / Stringency = 9)
- 13. Word Size Algorithm T A C G G T A T G A C A G T A T C T A C G G T A T G A C A G T A T C T A C G G T A T G A C A G T A T C T A C G G T A T G A C A G T A T C C T A T G A C A T A C G G T A T G Word Size = 3
- 14. PTHPLASKTQILPEDLASEDLTI PTHPLAGERAIGLARLAEEDFGM Score = 7 PTHPLASKTQILPEDLASEDLTI PTHPLAGERAIGLARLAEEDFGM Score = 11 Matrix: PAM250 Window = 12 Stringency = 9 Scoring Matrix Filtering PTHPLASKTQILPEDLASEDLTI PTHPLAGERAIGLARLAEEDFGM Score = 11 Window / Stringency
- 15. Dotplot (Window = 18 / Stringency = 10) Hemoglobin β-chain Hemoglobin α-chain
- 16. Considerations • The window/stringency method is more sensitive than the wordsize method (ambiguities are permitted). • The smaller the window, the larger the weight of statistical (unspecific) matches. • With large windows the sensitivity for short sequences is reduced. • Insertions/deletions are not treated explicitly.
- 17. Alignment methods • Rigorous algorithms = Dynamic Programming • Needleman-Wunsch (global) • Smith-Waterman (local) • Heuristic algorithms (faster but approximate) • BLAST • FASTA
- 18. The Rocks game • N rocks, 2 piles, 2 players • Player can • Remove 1 rock from either pile • Remove 1 rock from each pile • Last to remove a rock wins • Assume 10 rocks in each pile – winning strategy?
- 19. Dynamic Programming
- 20. Basic principles of dynamic programming - Creation of an alignment path matrix - Stepwise calculation of score values - Backtracking (evaluation of the optimal path)
- 21. Dynamic Programming • Dynamic Programming is a very general programming technique. • It is applicable when a large search space can be structured into a succession of stages, such that: • the initial stage contains trivial solutions to sub- problems • each partial solution in a later stage can be calculated by recurring a fixed number of partial solutions in an earlier stage • the final stage contains the overall solution
- 22. Creation of an alignment path matrix Idea: Build up an optimal alignment using previous solutions for optimal alignments of smaller subsequences • Construct matrix F indexed by i and j (one index for each sequence) • F(i,j) is the score of the best alignment between the initial segment x1...i of x up to xi and the initial segment y1...j of y up to yj • Build F(i,j) recursively beginning with F(0,0) = 0
- 23. • If F(i-1,j-1), F(i-1,j) and F(i,j-1) are known we can calculate F(i,j) • Three possibilities: • xi and yj are aligned, F(i,j) = F(i-1,j-1) + s(xi ,yj) • xi is aligned to a gap, F(i,j) = F(i-1,j) - d • yj is aligned to a gap, F(i,j) = F(i,j-1) - d • The best score up to (i,j) will be the largest of the three options Creation of an alignment path matrix
- 24. H E A G A W G H E E 0 -8 -16 -24 -32 -40 -48 -56 -64 -72 -80 P -8 -2 -9 -17 -25 -33 -42 -49 -57 -65 -73 A -16 -10 -3 -4 -12 -20 -28 -36 -44 -52 -60 W -24 -18 -11 -6 -7 -15 -5 -13 -21 -29 -37 H -32 -14 -18 -13 -8 -9 -13 -7 -3 -11 -19 E -40 -22 -8 -16 -16 -9 -12 -15 -7 3 -5 A -48 -30 -16 -3 -11 -11 -12 -12 -15 -5 2 E -56 -38 -24 -11 -6 -12 -14 -15 -12 -9 1 Backtracking -5 1 - A E E H H G - W W A A G - A P E - H - 0 -25 -5 -20 -13 -3 3 -8 -16 -17 Optimal global alignment: E E
- 25. Global vs. Local Alignments • Global alignment algorithms start at the beginning of two sequences and add gaps to each until the end of one is reached. • Local alignment algorithms finds the region (or regions) of highest similarity between two sequences and build the alignment outward from there.
- 26. needle (Needleman & Wunsch) creates an end-to-end alignment. Global Alignment Two closely related sequences:
- 27. Two sequences sharing several regions of local similarity: 1 AGGATTGGAATGCTCAGAAGCAGCTAAAGCGTGTATGCAGGATTGGAATTAAAGAGGAGGTAGACCG.... 67 1 AGGATTGGAATGCTAGGCTTGATTGCCTACCTGTAGCCACATCAGAAGCACTAAAGCGTCAGCGAGACCG 70 |||||||||||||| | | | |||| || | | | || Global Alignment
- 28. Global Alignment (Needleman-Wunsch) • The the Needleman-Wunsch algorithm creates a global alignment over the length of both sequences (needle) • Global algorithms are often not effective for highly diverged sequences - do not reflect the biological reality that two sequences may only share limited regions of conserved sequence. • Sometimes two sequences may be derived from ancient recombination events where only a single functional domain is shared. • Global methods are useful when you want to force two sequences to align over their entire length
- 29. Local Alignment (Smith-Waterman) • Local alignment • Identify the most similar sub-region shared between two sequences • Smith-Waterman • EMBOSS: water
- 30. Parameters of Sequence Alignment Scoring Systems: • Each symbol pairing is assigned a numerical value, based on a symbol comparison table. Gap Penalties: • Opening: The cost to introduce a gap • Extension: The cost to elongate a gap
- 31. DNA Scoring Systems -very simple actaccagttcatttgatacttctcaaa taccattaccgtgttaactgaaaggacttaaagact Sequence 1 Sequence 2 A G C T A 1 0 0 0 G 0 1 0 0 C 0 0 1 0 T 0 0 0 1 Match: 1 Mismatch: 0 Score = 5
- 32. Protein Scoring Systems PTHPLASKTQILPEDLASEDLTI PTHPLAGERAIGLARLAEEDFGM Sequence 1 Sequence 2 Scoring matrix T:G = -2 T:T = 5 Score = 48 C S T P A G N D . . C 9 S -1 4 T -1 1 5 P -3 -1 -1 7 A 0 1 0 -1 4 G -3 0 -2 -2 0 6 N -3 1 0 -2 -2 0 5 D -3 0 -1 -1 -2 -1 1 6 . . C S T P A G N D . . C 9 S -1 4 T -1 1 5 P -3 -1 -1 7 A 0 1 0 -1 4 G -3 0 -2 -2 0 6 N -3 1 0 -2 -2 0 5 D -3 0 -1 -1 -2 -1 1 6 . .
- 33. • Amino acids have different biochemical and physical properties that influence their relative replaceability in evolution. C P G GAVI L M F Y W H K R E Q D N S T CSH S+S positive charged polar aliphatic aromatic small tiny hydrophobic Protein Scoring Systems
- 34. • Scoring matrices reflect: – # of mutations to convert one to another – chemical similarity – observed mutation frequencies – the probability of occurrence of each amino acid • Widely used scoring matrices: • PAM • BLOSUM Protein Scoring Systems
- 35. PAM matrices • Family of matrices PAM 80, PAM 120, PAM 250 • The number with a PAM matrix represents the evolutionary distance between the sequences on which the matrix is based • Greater numbers denote greater distances
- 36. PAM (Percent Accepted Mutations) matrices • The numbers of replacements were used to compute a so-called PAM-1 matrix. • The PAM-1 matrix reflects an average change of 1% of all amino acid positions. PAM matrices for larger evolutionary distances can be extrapolated from the PAM-1 matrix. • PAM250 = 250 mutations per 100 residues. • Greater numbers mean bigger evolutionary distance
- 37. PAM (Percent Accepted Mutations) matrices • Derived from global alignments of protein families . Family members share at least 85% identity (Dayhoff et al., 1978). • Construction of phylogenetic tree and ancestral sequences of each protein family • Computation of number of replacements for each pair of amino acids
- 38. A R N D C Q E G H I L K M F P S T W Y V B Z A 2 -2 0 0 -2 0 0 1 -1 -1 -2 -1 -1 -3 1 1 1 -6 -3 0 2 1 R -2 6 0 -1 -4 1 -1 -3 2 -2 -3 3 0 -4 0 0 -1 2 -4 -2 1 2 N 0 0 2 2 -4 1 1 0 2 -2 -3 1 -2 -3 0 1 0 -4 -2 -2 4 3 D 0 -1 2 4 -5 2 3 1 1 -2 -4 0 -3 -6 -1 0 0 -7 -4 -2 5 4 C -2 -4 -4 -5 12 -5 -5 -3 -3 -2 -6 -5 -5 -4 -3 0 -2 -8 0 -2 -3 -4 Q 0 1 1 2 -5 4 2 -1 3 -2 -2 1 -1 -5 0 -1 -1 -5 -4 -2 3 5 E 0 -1 1 3 -5 2 4 0 1 -2 -3 0 -2 -5 -1 0 0 -7 -4 -2 4 5 G 1 -3 0 1 -3 -1 0 5 -2 -3 -4 -2 -3 -5 0 1 0 -7 -5 -1 2 1 H -1 2 2 1 -3 3 1 -2 6 -2 -2 0 -2 -2 0 -1 -1 -3 0 -2 3 3 I -1 -2 -2 -2 -2 -2 -2 -3 -2 5 2 -2 2 1 -2 -1 0 -5 -1 4 -1 -1 L -2 -3 -3 -4 -6 -2 -3 -4 -2 2 6 -3 4 2 -3 -3 -2 -2 -1 2 -2 -1 K -1 3 1 0 -5 1 0 -2 0 -2 -3 5 0 -5 -1 0 0 -3 -4 -2 2 2 M -1 0 -2 -3 -5 -1 -2 -3 -2 2 4 0 6 0 -2 -2 -1 -4 -2 2 -1 0 F -3 -4 -3 -6 -4 -5 -5 -5 -2 1 2 -5 0 9 -5 -3 -3 0 7 -1 -3 -4 P 1 0 0 -1 -3 0 -1 0 0 -2 -3 -1 -2 -5 6 1 0 -6 -5 -1 1 1 S 1 0 1 0 0 -1 0 1 -1 -1 -3 0 -2 -3 1 2 1 -2 -3 -1 2 1 T 1 -1 0 0 -2 -1 0 0 -1 0 -2 0 -1 -3 0 1 3 -5 -3 0 2 1 W -6 2 -4 -7 -8 -5 -7 -7 -3 -5 -2 -3 -4 0 -6 -2 -5 17 0 -6 -4 -4 Y -3 -4 -2 -4 0 -4 -4 -5 0 -1 -1 -4 -2 7 -5 -3 -3 0 10 -2 -2 -3 V 0 -2 -2 -2 -2 -2 -2 -1 -2 4 2 -2 2 -1 -1 -1 0 -6 -2 4 0 0 B 2 1 4 5 -3 3 4 2 3 -1 -2 2 -1 -3 1 2 2 -4 -2 0 6 5 Z 1 2 3 4 -4 5 5 1 3 -1 -1 2 0 -4 1 1 1 -4 -3 0 5 6 PAM 250 C -8 17 W W
- 39. PAM - limitations • Based on only one original dataset • Examines proteins with few differences (85% identity) • Based mainly on small globular proteins so the matrix is biased
- 40. BLOSUM matrices • Different BLOSUMn matrices are calculated independently from BLOCKS (ungapped local alignments) • BLOSUMn is based on a cluster of BLOCKS of sequences that share at least n percent identity • BLOSUM62 represents closer sequences than BLOSUM45
- 41. • Derived from alignments of domains of distantly related proteins (Henikoff & Henikoff,1992). • Occurrences of each amino acid pair in each column of each block alignment is counted. • The numbers derived from all blocks were used to compute the BLOSUM matrices. A A C E C A - C = 4 A - E = 2 C - E = 2 A - A = 1 C - C = 1 BLOSUM (Blocks Substitution Matrix) A A C E C
- 42. The Blosum50 Scoring Matrix
- 43. BLOSUM (Blocks Substitution Matrix) • Sequences within blocks are clustered according to their level of identity. • Clusters are counted as a single sequence. • Different BLOSUM matrices differ in the percentage of sequence identity used in clustering. • The number in the matrix name (e.g. 62 in BLOSUM62) refers to the percentage of sequence identity used to build the matrix. • Greater numbers mean smaller evolutionary distance.
- 44. PAM Vs. BLOSUM PAM100 = BLOSUM90 PAM120 = BLOSUM80 PAM160 = BLOSUM60 PAM200 = BLOSUM52 PAM250 = BLOSUM45 More distant sequences lBLOSUM62 for general use lBLOSUM80 for close relations lBLOSUM45 for distant relations lBLOSUM62 for general use lBLOSUM80 for close relations lBLOSUM45 for distant relations lPAM120 for general use lPAM60 for close relations lPAM250 for distant relations lPAM120 for general use lPAM60 for close relations lPAM250 for distant relations
- 45. TIPS on choosing a scoring matrix • Generally, BLOSUM matrices perform better than PAM matrices for local similarity searches (Henikoff & Henikoff, 1993). • When comparing closely related proteins one should use lower PAM or higher BLOSUM matrices, for distantly related proteins higher PAM or lower BLOSUM matrices. • For database searching the commonly used matrix is BLOSUM62.
- 46. T A T G T G G A A T G A Scoring Insertions and Deletions A T G T - - A A T G C A A T G T A A T G C A T A T G T G G A A T G A The creation of a gap is penalized with a negative score value. insertion / deletion
- 47. 1 GTGATAGACACAGACCGGTGGCATTGTGG 29 ||| | | ||| | || || | 1 GTGTCGGGAAGAGATAACTCCGATGGTTG 29 Why Gap Penalties? Gaps allowed but not penalized Score: 88 Gaps not permitted Score: 0 1 GTG.ATAG.ACACAGA..CCGGT..GGCATTGTGG 29 ||| || | | | ||| || | | || || | 1 GTGTAT.GGA.AGAGATACC..TCCG..ATGGTTG 29 Match = 5 Mismatch = -4
- 48. • The optimal alignment of two similar sequences is usually that which • maximizes the number of matches and • minimizes the number of gaps. •There is a tradeoff between these two - adding gaps reduces mismatches • Permitting the insertion of arbitrarily many gaps can lead to high scoring alignments of non-homologous sequences. • Penalizing gaps forces alignments to have relatively few gaps. Why Gap Penalties?
- 49. Gap Penalties • How to balance gaps with mismatches? • Gaps must get a steep penalty, or else you’ll end up with nonsense alignments. • In real sequences, multi-base (or amino acid) gaps are quite common •genetic insertion/deletion events • “Affine” gap penalties give a big penalty for each new gap, but a much smaller “gap extension” penalty.
- 50. Scoring Insertions and Deletions A T G T T A T A C T A T G T G C G T A T A Total Score: 4 Gap parameters: d = 3 (gap opening) e = 0.1 (gap extension) g = 3 (gap lenght) γ(g) = -3 - (3 -1) 0.1 = -3.2 T A T G T G C G T A T A A T G T - - - T A T A C insertion / deletion match = 1 mismatch = 0 Total Score: 8 - 3.2 = 4.8
- 51. Modification of Gap Penalties 1 V...LSPADKFLTNV 12 | |||| | | | 1 VFTELSPA.K..T.V 11 1 ...VLSPADKFLTNV 12 |||| 1 VFTELSPAKTV.... 11 gap opening penalty = 0 gap extension penalty = 0.1 score = 11.3 Score Matrix: BLOSUM62 gap opening penalty = 3 gap extension penalty = 0.1 score = 6.3
- 52. BLAST Algorithm Basic Local Alignment Search Tool • Fast alignment technique(s) • Similar to FASTA algorithms (not used much now) • There are more accurate ones, but they’re slower • BLAST makes a big use of lookup tables • Idea: statistically significant alignments (hits) • Will have regions of at least 3 letters same • Or at least high scoring with respect to BLOSUM matrix • Based on small local alignments CCNDHRKMTCSPNDNNRK TTNDHRMTACSPDNNNKH CCNDHRKMTCSPNDNNRK YTNHHMMTTYSLDNNNKK more likely than
- 53. BLAST Overview • Given a query sequence Q • Seven main stages 1. Remove (filter) low complexity regions from Q 2. Harvest k-tuples (triples) from Q 3. Expand each triple into ~50 high scoring words 4. Seed a set of possible alignments 5. Generate high scoring pairs (HSPs) from the seeds 6. Test significance of matches from HSPs 7. Report the alignments found from the HSPs
- 54. BLAST Algorithm Part 1 Removing Low-complexity Segments • Imagine matching • HHHHHHHHKMAY and HHHHHHHHURHD • The KMAY and URHD are the interesting parts • But this pair score highly using BLOSUM • It’s a good idea to remove the HHHHHHHs • From the query sequence (low complexity) • SEG program does this kind of thing • Comes with most BLAST implementations • Often doesn’t do much, and it can be turned off
- 55. Removing Low-complexity Segments • Given a segment of length L • With each amino acid occurring n1 n2 … n20 times • Use the following measure for “compositional complexity”: • To use this measure • Slide a “window” of ~12 residues along Query Sequence Q • Use a threshold to determine low complexity windows • Use a minimise routine to replace the segment • With an optimal minimised segment (or just an X)
- 56. BLAST Algorithm Part 2 Harvesting k-tuples • Collect all the k-tuples of elements in Q • k set to 3 for residues and 11 for DNA (can vary) • Triples are called ‘words’. Call this set W S T S L S T S D K L M R STS TSL SLS LST
- 57. BLAST Algorithm Part 3 Finding High Scoring Triples • Given a word w from W • Find all other words w’ of same length (3), which: • Appear in some database sequence • Blosum(w,w’) > a threshold T • Choose T to limit number to around 50 • Call these the high scoring triples (words) for w • Example: letting w=PQG, set T to be 13 • Suppose that PQG, PEG, PSG, PQA are found in database • Blosum(PQG,PQG) = 18, Blosum(PQG,PEG) = 15 • Blosum(PQG,PSG) = 13, Blosum(PQG,PQA) = 12 • Hence, PQG and PEG only are kept
- 58. Finding High Scoring Triples • For each w in W, find all the high scoring words • Organise these sets of words • Remembering all the places where w was found in Q • Each high scoring triple is going to be a seed • In order to generate possible alignment(s) • One seed can generate more than one alignment • End of the first half of the algorithm • Going to find alignments now
- 59. BLAST Algorithm Part 4 Seeding Possible Alignments • Look at first triple V in query sequence Q • Actually from Q (not from W - which has omissions) • Retrieve the set of ~50 high scoring words • Call this set HV • Retrieve the list of places in Q where V occurs • Call this set PV • For every pair (word, pos) • Where word is from HV and pos is from PV • Find all the database sequences D • Which have an exact match with word at position pos’ • Store an alignment between Q and D • With V matched at pos in Q and pos’ in D • Repeat this for the second triple in Q, and so on
- 60. Seeding Possible Alignments Example • Suppose Q = QQGPHUIQEGQQG • Suppose V = QQG, HV = {QQG, QEG} • Then PV = {1, 11} • Suppose we are looking in the database at: • D = PKLMMQQGKQEG • Then the alignments seeded are: QQGPHUIQEGQQG word=QQG QQGPHUIQEGQQG word=QQG PKLMMQQGKQEG pos=1 PKLMMQQGKQEG pos=11 QQGPHUIQEGQQG word=QEG QQGPHUIQEGQQG word=QEG PKLMMQQGKQEG pos=1 PKLMMQQGKQEG pos=11
- 61. BLAST Algorithm Part 5 Generating High Scoring Pairs (HSPs) • For each alignment A • Where sequences Q and D are matched • Original region matching was M • Extend M to the left • Until the Blosum score begins to decrease • Extend M to the right • Until the Blosum score begins to decrease • Larger stretch of sequence now matches • May have higher score than the original triple • Call these high scoring pairs • Throw away any alignments for which the score S of the extended region M is lower than some cutoff score
- 62. Extending Alignment Regions Example QQGPHUIQEGQQGKEEDPP Blosum(QQG,QQG) = 16 PKLMMQQGKQEGM QQGPHUIQEGQQGKEEDPP Blosum(QQGK,QQGK) = 21 PKLMMQQGKQEGM QQGPHUIQEGQQGKEEDPP Blosum(QQGKE,QQGKQ) = 23 PKLMMQQGKQEGM QQGPHUIQEGQQGKEEDPP Blosum(QQGKEE,QQGKQE) = 28 PKLMMQQGKQEGM QQGPHUIQEGQQGKEEDPP Blosum(QQGKEED,QQGKQEG) = 27 PKLMMQQGKQEGM So, the extension to the right stops here HSP (before left extension) is QQGKEE, scoring 28
- 63. BLAST Algorithm Part 6 Checking Statistical Significance • Reason we extended alignment regions • Give a more accurate picture of the probability of that BLOSUM score occurring by chance • Question: is a HSP significant? • Suppose we have a HSP such that • It scores S for a region of length L in sequences Q & D • Then the probability of two random sequences Q’ and D’ scoring S in a region of length L is calculated • Where Q’ is same length as Q and D’ is same length as D • This probability needs to be low for significance
- 64. BLAST Algorithm Part 7 Reporting the Alignments • For each statistically significant HSP • The alignment is reported • If a sequence D has two HSPs with Query Q • Two different alignments are reported • Later versions of BLAST • Try and unify the two alignments

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