1. Multiple sequence alignment Dr Alexei Drummond Department of Computer Science alexei@cs.auckland.ac.nz Semester 2, 2006

2. Statistics, multiple alignment and biological considerations 2 Multiple alignment software Really need approximation methods. Four techniques 1.progressive global alignment of sequences starting with an alignment of the most similar sequences and then building a full alignment by adding more sequences 2.iterative methods that make an initial alignment of groups of sequences and then refine the alignment to achieve a better result (Barton-sternberg, Simulated annealing, stochastic hill climbing) 3.(alignments based on locally conserved patterns found in the same order in the sequences), and 4.use of probabilistic models of the indel and substitution process to do statistical inference of alignment. (“Statistical alignment”)

3. Statistics, multiple alignment and biological considerations 3 Scoring a multiple alignment Usually Score for column Gaps score Column i 1 N

4. Statistics, multiple alignment and biological considerations 4 Linear gap scores & SP scoring Treat gap as separate symbol. s(a,-) = s(-,a) = gap score s(-,-) = 0 “Sum of Pairs” (SP) scoring function Column k l i 1 N

5. Statistics, multiple alignment and biological considerations 5 Multidimensional dynamic programming Define = max score of an alignment up to the sequences ending with 1 N All ways of placing gaps in this column time, space

6. Statistics, multiple alignment and biological considerations 6 MSA Carrillo and Lipman (1988), Lipman, Altschul and Kececioglu (1989). Can optimally align up to 5-7 protein sequences of up to 200 residues.

7. Statistics, multiple alignment and biological considerations 7 Progressive alignment Align sequences (pairwise) in some (greedy) order Decisions (1)Order of alignments (2)Alignment of sequence to group (only), or allow group to group (3)Method of alignment, and scoring function

8. Statistics, multiple alignment and biological considerations 8 Guide tree A B C D E A B C D F this ? or this ? E

9. Statistics, multiple alignment and biological considerations 9 Feng & Doolittle (1987) Overview (1)Calculate diagonal matrix of N(N-1)/2 distances between all pairs of N sequences by standard pairwise alignment, converting raw alignment scores to approximate pairwise “distances”. (2)Construct guide tree from the distance matrix by using appropriate clustering algorithm. (3)Starting from first node added to the tree, align the child nodes (which may be two sequences, a sequence and an alignment, or two alignments). Repeat for all other nodes in the order that they were added to tree, until all sequences have been aligned.

10. Statistics, multiple alignment and biological considerations 10 Feng & Doolittle (1987) sequence-to-group Best pairwise alignment determines alignment to group XXXX XXXXXX XXXXXXXX XX

11. Statistics, multiple alignment and biological considerations 11 Feng & Doolittle (1987) sequence-to-group Best pairwise alignment determines alignment to group X

12. Statistics, multiple alignment and biological considerations 12 Feng & Doolittle (1987) sequence-to-group Best pairwise alignment determines alignment to group X – – – – – This column is encouraged because it has no cost

13. Statistics, multiple alignment and biological considerations 13 Feng & Doolittle (1987) sequence-to-group Best pairwise alignment determines alignment to group XXXX XXXXXX XXXXXXXX XX – – – – –

14. Statistics, multiple alignment and biological considerations 14 Feng & Doolittle (1987) sequence-to-group Best pairwise alignment determines alignment to group XXXX XXXXXX XXXXXXXX XX XXXXX

15. Statistics, multiple alignment and biological considerations 15 Feng & Doolittle (1987) group-to-group Best pairwise alignment determines alignment of groups XXXX XXXXXX XXXXXXXX XX XXXX

16. Statistics, multiple alignment and biological considerations 16 Feng & Doolittle (1987) group-to-group Best pairwise alignment determines alignment of groups X XX

17. Statistics, multiple alignment and biological considerations 17 Feng & Doolittle (1987) group-to-group Best pairwise alignment determines alignment of groups X XX – – – –

18. Statistics, multiple alignment and biological considerations 18 Feng & Doolittle (1987) group-to-group Best pairwise alignment determines alignment of groups XXXX XXXXXX XXXXXXXX XX XXXX – – – ––––––––––––––

19. Statistics, multiple alignment and biological considerations 19 Feng & Doolittle (1987) group-to-group Best pairwise alignment determines alignment of groups XXXX XXXXXX XXXXXXXX XX XXXX –––––––––––––– – – –

20. Statistics, multiple alignment and biological considerations 20 Feng & Doolittle (1987) group-to-group Best pairwise alignment determines alignment of groups XXXX XXXXXX XXXXXXXX XX XXXX XXXXXXXXXXXXXX XXXXXXXX XXXXXXXX XXXXXXXX XXXXXXXX XXXXXXXX XXXXXXXX

21. Statistics, multiple alignment and biological considerations 21 Feng & Doolittle (1987) After alignment is completed gap symbols replaced by “X”. “Once a gap, always a gap”. Encourages gaps to occur in same columns in subsequent alignments. Implemented by PILEUP (from GCG package).

22. Statistics, multiple alignment and biological considerations 22 Profile alignment group-to-group A B Total alignment score = score (A) + score (B) + score (A*B) XXXXXX XXXXXX XXXXXX

23. Statistics, multiple alignment and biological considerations 23 CLUSTALW Thompson, Higgins and Gibson (1994). Widely used implementation of profile-based progressive multiple alignment. Similar to Feng-Doolittle method, except for use of profile alignment methods. Overview: 1.Calculate diagonal matrix of N(N-1)/2 distances between all pairs of N sequences by standard pairwise alignment, converting raw alignment scores to approximate pairwise “distances”. 2.Construct guide tree from distance matrix by using an appropriate neighbour-joining clustering algorithm. 3.Progressively align at nodes in order of decreasing similarity, using sequence-sequence, sequence-profile, and profile-profile alignment. Plus many other heuristics.

24. Statistics, multiple alignment and biological considerations 24 CLUSTAL W heuristics Closely related sequences are aligned with hard matrices (BLOSUM80) and distant sequences are aligned with soft matrices (BLOSUM50). Hydrophobic residues (which are more likely to be buried) are given higher gap penalties than hydrophilic residues (which are more likely to be surface- accessible). Gap-open penalties are also decreased if the position is spanned by 5 or more consecutive hydrophilic residues.

25. Statistics, multiple alignment and biological considerations 25 CLUSTAL W heuristics Both gap-open penalties and gap-extend penalties are increased if there are no gaps in a column but gaps occur nearby in the alignment. This rule tries to force all gaps to occur in the same places in an alignment. In the progressive alignment stage, if the score of an alignment is low, the guide tree may be adjusted on the fly to defer the low scoring alignment until later in the progressive alignment phase when more profile information has been accumulated.

26. Statistics, multiple alignment and biological considerations 26 Iterative refinement i.e. “hill climbing”. Slightly change solution to improve score. Converge to local optimum. e.g. Barton-Sternberg (1987) multiple alignment (1)Find the two sequences with the highest pairwise similarity and align them using standard dynamic programming alignment. (2)Find sequence most similar to a profile of the alignment of the first two, and align it to first two by profile-sequence alignment. Repeat until all sequences have been included in the multiple alignment. (3)Remove sequence and realign it to a profile of the other aligned sequences by profile-sequence alignment. Repeat for sequences. (3)Repeat the previous alignment step a fixed number of times, or until the alignment score converges.

27. Statistics, multiple alignment and biological considerations 27 Clustal X

28. Statistics, multiple alignment and biological considerations 28 Clustal X

29. Statistics, multiple alignment and biological considerations 29 CLUSTALX

30. Statistics, multiple alignment and biological considerations 30 CLUSTALX

31. Statistics, multiple alignment and biological considerations C_aminophilum AGCT.YCGCA TGRAGCAGTG TGAAAA................ACTCCGGT GGTACAGGAT C_colinum AGTA..GGCA TCTACAAGTT GGAAAA................ACTGAGGT GGTATAGGAG C_lentocellum GGTATTCGCT TGATTATNAT AGTAAA................GATTTATC GCCATAGGAT C_botulinum_D TTTA.TGGCA TCATACATAA AATAATCAAA..........GGAGCAATCC GCTTTGAGAT C_novyi_A TTTA.CGGCA T....CGTAG AATAATCAAA..........GGAGCAATCC GCTTTGAGAT C_gasigenes AGTT.TCGCA TGAAACA... GC.AATTAAA..........GGAGAAATCC GCTATAAGAT C_aurantibutyricum A.NT.TCGCA TGGAGCA... AC.AATCAAA..........GGAGCAAT.C ACTATAAGAT C_sp_C_quinii AGTT.T.GCA TGGGACA... GC.AATTAAA..........GGAGCAATCC GCTATGAGAT C_perfringens AAGA.TGGCA T.CATCA... TTCAACCAAA..........GGAGCAATCC GCTATGAGAT C_cadaveris TTTT.CTGCA TGGGAAA... GTC.ATGAAA..........GGAGCAATCC GCTGTAAGAT C_cellulovorans ATTC.TCGCA TGAGAGA....TGTATCAAA..........GGAGCAATCC GCTATAAGAT C_K21 TTGR.TCGCA TGATCKAAAC ATCAAAGGAT..TTTTCTTTGGAAAATTCC ACTTTGAGAT C_estertheticum TTGA.TCGCA TGATCTTAAC ATCAAAGGAA..TTT..TTCGG..AATTTC ACTTTGAGAT C_botulinum_A AGAA.TCGCA TGATTTTCTT ATCAAAGATT..T............ATT.. GCTTTGAGAT C_sporogenes AGAA.TCGCA TGATTTTCTT ATCAAAGATT..T............ATT.. GCTTTGAGAT C_argentinense AAGG.TCGCA TGACTTTTAT ACCAAAGGAG..T............AATCC GCTATGAGAT C_subterminale AAGG.TCGCA TGACTTTTAT ACCAAAGGAG..T............AATCC GCTATGAGAT C_tetanomorphum TTTT.CCGCA TGAAAAACTA ATCAAAGGAG..T............AAT.C GCTTTGAGAT C_pasteurianum AGTT.TCACA TGGAGCTTTA ATTAAAGGAG..T............AATCC GCTTTGAGAT C_collagenovorans TTGA.TCGCA TGGTCGAAAT ATTAAAGGAG..T............AATCC GCTTACAGAT C_histolyticum TTTA.ATGCA TGTTAGAAAG ATTAAAGGAG..............CAATCC GCTTTGAGAT C_tyrobutyricum AGTT.TCACA TGGAATTTGG ATGAAAGGAG..T............AATTC GCTTTGAGAT C_tetani GGTT.TCGCA TGAAACTTTA ACCAAAGGAG..T............AATCT GCTTTGAGAT C_barkeri GACA.TCGCA TGGTGTT....TTAATGAAA............ACTCCGGT GCCATGAGAT C_thermocellum GGCA.TCGTC CTGTTAT....CAAAGGAGA............AATCCGGT...ATGAGAT Pep_prevotii AGTC.TCGCA TGGNGTTATC ATCAAAGA................TTTATC GGTGTAAGAT C_innocuum ACGGAGCGCA TGCTCTGTAT ATTAAAGCGC CCTTCAAGGCGTGAAC........ATGGAT S_ruminantium AGTTTCCGCA TGGGAGCTTG ATTAAAGATG GCCTCTACTTGTAAGCTATC GCTTTGCGAT

32. Statistics, multiple alignment and biological considerations C_aminophilum AGCT.YCGCA TGRAGCAGTG TGAAAA................ACTCCGGT GGTACAGGAT C_colinum AGTA..GGCA TCTACAAGTT GGAAAA................ACTGAGGT GGTATAGGAG C_lentocellum GGTATTCGCT TGATTATNAT AGTAAA................GATTTATC GCCATAGGAT C_botulinum_D TTTA.TGGCA TCATACATAA AATAATCAAA..........GGAGCAATCC GCTTTGAGAT C_novyi_A TTTA.CGGCA T....CGTAG AATAATCAAA..........GGAGCAATCC GCTTTGAGAT C_gasigenes AGTT.TCGCA TGAAACA... GC.AATTAAA..........GGAGAAATCC GCTATAAGAT C_aurantibutyricum A.NT.TCGCA TGGAGCA... AC.AATCAAA..........GGAGCAAT.C ACTATAAGAT C_sp_C_quinii AGTT.T.GCA TGGGACA... GC.AATTAAA..........GGAGCAATCC GCTATGAGAT C_perfringens AAGA.TGGCA T.CATCA... TTCAACCAAA..........GGAGCAATCC GCTATGAGAT C_cadaveris TTTT.CTGCA TGGGAAA... GTC.ATGAAA..........GGAGCAATCC GCTGTAAGAT C_cellulovorans ATTC.TCGCA TGAGAGA....TGTATCAAA..........GGAGCAATCC GCTATAAGAT C_K21 TTGR.TCGCA TGATCKAAAC ATCAAAGGAT..TTTTCTTTGGAAAATTCC ACTTTGAGAT C_estertheticum TTGA.TCGCA TGATCTTAAC ATCAAAGGAA..TTT..TTCGG..AATTTC ACTTTGAGAT C_botulinum_A AGAA.TCGCA TGATTTTCTT ATCAAAGATT..T............ATT.. GCTTTGAGAT C_sporogenes AGAA.TCGCA TGATTTTCTT ATCAAAGATT..T............ATT.. GCTTTGAGAT C_argentinense AAGG.TCGCA TGACTTTTAT ACCAAAGGAG..T............AATCC GCTATGAGAT C_subterminale AAGG.TCGCA TGACTTTTAT ACCAAAGGAG..T............AATCC GCTATGAGAT C_tetanomorphum TTTT.CCGCA TGAAAAACTA ATCAAAGGAG..T............AAT.C GCTTTGAGAT C_pasteurianum AGTT.TCACA TGGAGCTTTA ATTAAAGGAG..T............AATCC GCTTTGAGAT C_collagenovorans TTGA.TCGCA TGGTCGAAAT ATTAAAGGAG..T............AATCC GCTTACAGAT C_histolyticum TTTA.ATGCA TGTTAGAAAG ATTAAAGGAG..............CAATCC GCTTTGAGAT C_tyrobutyricum AGTT.TCACA TGGAATTTGG ATGAAAGGAG..T............AATTC GCTTTGAGAT C_tetani GGTT.TCGCA TGAAACTTTA ACCAAAGGAG..T............AATCT GCTTTGAGAT C_barkeri GACA.TCGCA TGGTGTT....TTAATGAAA............ACTCCGGT GCCATGAGAT C_thermocellum GGCA.TCGTC CTGTTAT....CAAAGGAGA............AATCCGGT...ATGAGAT Pep_prevotii AGTC.TCGCA TGGNGTTATC ATCAAAGA................TTTATC GGTGTAAGAT C_innocuum ACGGAGCGCA TGCTCTGTAT ATTAAAGCGC CCTTCAAGGCGTGAAC........ATGGAT S_ruminantium AGTTTCCGCA TGGGAGCTTG ATTAAAGATG GCCTCTACTTGTAAGCTATC GCTTTGCGAT TCAAAGGAG

33. Statistics, multiple alignment and biological considerations 33 Alignment - considerations The programs simply try to maximize the number of matches –The “best” alignment may not be the correct biological one Multiple alignments are done progressively –Such alignments get progressively worse as you add sequences –Mistakes that occur during alignment process are frozen in. Unless the sequences are very similar you will almost certainly have to correct manually

34. Statistics, multiple alignment and biological considerations 34 Manual Alignment- software Geneious 2.0- java application: –http://www.geneious.com/ CINEMA- Java applet available from: –http://www.biochem.ucl.ac.uk Seqapp/Seqpup- Mac/PC/UNIX available from: –http://iubio.bio.indiana.edu Se-Al for Macintosh, available from: –http://evolve.zoo.ox.ac.uk/Se-Al/Se-Al.html BioEdit for PC, available from: –http://www.mbio.ncsu.edu/RNaseP/info/programs/BIOEDI T/bioedit.html

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36. Statistics, multiple alignment and biological considerations 36

37. Statistics, multiple alignment and biological considerations 37

38. Statistics, multiple alignment and biological considerations 38 MACCLADE 4

39. Statistics, multiple alignment and biological considerations 39 Extra T Missing G

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41. Statistics, multiple alignment and biological considerations 41

42. Statistics, multiple alignment and biological considerations 42 Hang on, what makes a good alignment?

43. Statistics, multiple alignment and biological considerations 43 What makes a good alignment

44. Statistics, multiple alignment and biological considerations 44 What makes a good alignment Structural Alignment Sequence Alignment

45. Statistics, multiple alignment and biological considerations 45 What makes a good alignment

46. Statistics, multiple alignment and biological considerations 46 I hate ad hoc algorithms and manual sequence alignment! Is there an alternative?

47. Statistics, multiple alignment and biological considerations 47 An evolutionary hypothesis AG AATAACACACCGACC Insert CC Insert T Delete G G->C G->A Observations Hypothesis/Model T->C Knowing the rates of different events (substitutions, insertions and deletions) provides a method of assessing the probability of these observations, given this hypothesis: Pr{D|T,Q} T: the evolutionary tree Q: parameters of the evolutionary process

48. Statistics, multiple alignment and biological considerations 48 Statistics: fitting versus modeling Statistical fitting of sequence variation –Count frequencies of changes in real data sets –Build empirical statistical descriptions of the data (Blosum62) –Compare observed frequencies to well defined null hypothesis for testing (log-odds ratio and scores) –Use scores in ad hoc algorithms for search and alignment (BLAST and ClustalX) Probabilistic models of sequence evolution –Describe a probabilistic model in terms of a process of evolution, rates of substitution, insertion and deletion –Estimate parameters of the models and compare models using model comparison (likelihood ratios, Bayes factors) –Use maximum likelihood and Bayesian inference to co-estimate (uncertainty in) alignment and evolutionary history.

49. Statistics, multiple alignment and biological considerations 49 Probabilistic models and biology 3D structure of myoglobin, showing six alpha-helices.

50. Statistics, multiple alignment and biological considerations 50 State of the art

51. Statistics, multiple alignment and biological considerations 51 What does the future hold? No single “true” alignment –In most situations there are a set of alignments that are consistent with the observations –Understanding this uncertainty is as important as understanding the “best” alignment Explicit evolutionary model-based methods –Methods that co-estimate alignment and phylogeny are beginning to appear –Co-estimation of protein structure and alignment using evolutionary models may be on horizon Death of manual sequence alignment?