Multiple sequence alignment

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

2. Multiple alignment software Really need approximation methods. Four techniques • progressive global alignment of sequences starting with an alignment of the most similar sequences and then building a full alignment by adding more sequences • 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) • (alignments based on locally conserved patterns found in the same order in the sequences), and • use of probabilistic models of the indel and substitution process to do statistical inference of alignment. (“Statistical alignment”)

3. Scoring a multiple alignment i Usually 1 Gaps score Score for column N Column

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 i 1 k l N Column

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

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. 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 • Method of alignment, and scoring function

8. Guide tree A this ? B C D E A B or this ? C D E F

9. Feng & Doolittle (1987) Overview 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”. Construct guide tree from the distance matrix by using appropriate clustering algorithm. 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. Feng & Doolittle (1987) sequence-to-group Best pairwise alignment determines alignment to group X X X X X X X X X XX XX

11. Feng & Doolittle (1987) sequence-to-group Best pairwise alignment determines alignment to group X

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. Feng & Doolittle (1987) sequence-to-group Best pairwise alignment determines alignment to group – – – – – X X X X X X X X X XX XX

14. Feng & Doolittle (1987) sequence-to-group Best pairwise alignment determines alignment to group X X X X X X X X X X X X X X XX XX

15. Feng & Doolittle (1987) group-to-group X X XX XX Best pairwise alignment determines alignment of groups X X X X X X X X X XX XX

16. Feng & Doolittle (1987) group-to-group XX Best pairwise alignment determines alignment of groups X

17. Feng & Doolittle (1987) group-to-group XX – – – – – – Best pairwise alignment determines alignment of groups X –

18. Feng & Doolittle (1987) group-to-group – – – – – – X X – – – – – – – – – – – – XX XX – – – – – – Best pairwise alignment determines alignment of groups X X X ––––––– X X X X X X XX XX

19. Feng & Doolittle (1987) group-to-group – – – – – – X X – – – – – – XX XX – – – – – – – – – – – – Best pairwise alignment determines alignment of groups X X X ––––––– X X X X X X XX XX

20. Feng & Doolittle (1987) group-to-group X X X X X X X X X X X X X X X X X X X X X X X X X X XX XX Best pairwise alignment determines alignment of groups X X X XXXXXXX X X X X X X XX XX

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. Profile alignment group-to-group X X X A X X X B X X X Total alignment score = score (A) + score (B) + score (A*B)

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: • 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”. • Construct guide tree from distance matrix by using an appropriate neighbour-joining clustering algorithm. • Progressively align at nodes in order of decreasing similarity, using sequence-sequence, sequence-profile, and profile-profile alignment. • Plus many other heuristics.

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

27. Clustal X

28. Clustal X

29. CLUSTALX

30. CLUSTALX

31. C_aminophilum AGCT.YCGCATGRAGCAGTG TGAAAA.... ............ACTCCGGT GGTACAGGAT C_colinum AGTA..GGCATCTACAAGTT GGAAAA.... ............ACTGAGGT GGTATAGGAG C_lentocellum GGTATTCGCTTGATTATNATAGTAAA.... ............GATTTATC GCCATAGGAT C_botulinum_D TTTA.TGGCATCATACATAAAATAATCAAA ..........GGAGCAATCC GCTTTGAGAT C_novyi_A TTTA.CGGCAT....CGTAG AATAATCAAA ..........GGAGCAATCC GCTTTGAGAT C_gasigenes AGTT.TCGCATGAAACA... GC.AATTAAA ..........GGAGAAATCC GCTATAAGAT C_aurantibutyricum A.NT.TCGCATGGAGCA... AC.AATCAAA ..........GGAGCAAT.CACTATAAGAT C_sp_C_quinii AGTT.T.GCATGGGACA... GC.AATTAAA ..........GGAGCAATCC GCTATGAGAT C_perfringens AAGA.TGGCAT.CATCA... TTCAACCAAA ..........GGAGCAATCC GCTATGAGAT C_cadaveris TTTT.CTGCATGGGAAA... GTC.ATGAAA ..........GGAGCAATCC GCTGTAAGAT C_cellulovorans ATTC.TCGCATGAGAGA... .TGTATCAAA ..........GGAGCAATCC GCTATAAGAT C_K21 TTGR.TCGCATGATCKAAACATCAAAGGAT ..TTTTCTTTGGAAAATTCCACTTTGAGAT C_estertheticum TTGA.TCGCATGATCTTAACATCAAAGGAA ..TTT..TTCGG..AATTTCACTTTGAGAT C_botulinum_A AGAA.TCGCATGATTTTCTTATCAAAGATT ..T............ATT.. GCTTTGAGAT C_sporogenes AGAA.TCGCATGATTTTCTTATCAAAGATT ..T............ATT.. GCTTTGAGAT C_argentinense AAGG.TCGCATGACTTTTATACCAAAGGAG ..T............AATCC GCTATGAGAT C_subterminale AAGG.TCGCATGACTTTTATACCAAAGGAG ..T............AATCC GCTATGAGAT C_tetanomorphum TTTT.CCGCATGAAAAACTAATCAAAGGAG ..T............AAT.C GCTTTGAGAT C_pasteurianum AGTT.TCACATGGAGCTTTAATTAAAGGAG ..T............AATCC GCTTTGAGAT C_collagenovorans TTGA.TCGCATGGTCGAAATATTAAAGGAG ..T............AATCC GCTTACAGAT C_histolyticum TTTA.ATGCATGTTAGAAAG ATTAAAGGAG ..............CAATCC GCTTTGAGAT C_tyrobutyricum AGTT.TCACATGGAATTTGG ATGAAAGGAG ..T............AATTC GCTTTGAGAT C_tetani GGTT.TCGCATGAAACTTTAACCAAAGGAG ..T............AATCT GCTTTGAGAT C_barkeri GACA.TCGCATGGTGTT... .TTAATGAAA ............ACTCCGGT GCCATGAGAT C_thermocellum GGCA.TCGTCCTGTTAT... .CAAAGGAGA ............AATCCGGT ...ATGAGAT Pep_prevotii AGTC.TCGCATGGNGTTATCATCAAAGA.. ..............TTTATC GGTGTAAGAT C_innocuum ACGGAGCGCATGCTCTGTATATTAAAGCGCCCTTCAAGGCGTGAAC.... ....ATGGAT S_ruminantium AGTTTCCGCATGGGAGCTTG ATTAAAGATG GCCTCTACTTGTAAGCTATC GCTTTGCGAT

32. TCAAAGGAG C_aminophilum AGCT.YCGCATGRAGCAGTG TGAAAA.... ............ACTCCGGT GGTACAGGAT C_colinum AGTA..GGCATCTACAAGTT GGAAAA.... ............ACTGAGGT GGTATAGGAG C_lentocellum GGTATTCGCTTGATTATNATAGTAAA.... ............GATTTATC GCCATAGGAT C_botulinum_D TTTA.TGGCATCATACATAAAATAATCAAA ..........GGAGCAATCC GCTTTGAGAT C_novyi_A TTTA.CGGCAT....CGTAG AATAATCAAA ..........GGAGCAATCC GCTTTGAGAT C_gasigenes AGTT.TCGCATGAAACA... GC.AATTAAA ..........GGAGAAATCC GCTATAAGAT C_aurantibutyricum A.NT.TCGCATGGAGCA... AC.AATCAAA ..........GGAGCAAT.CACTATAAGAT C_sp_C_quinii AGTT.T.GCATGGGACA... GC.AATTAAA ..........GGAGCAATCC GCTATGAGAT C_perfringens AAGA.TGGCAT.CATCA... TTCAACCAAA ..........GGAGCAATCC GCTATGAGAT C_cadaveris TTTT.CTGCATGGGAAA... GTC.ATGAAA ..........GGAGCAATCC GCTGTAAGAT C_cellulovorans ATTC.TCGCATGAGAGA... .TGTATCAAA ..........GGAGCAATCC GCTATAAGAT C_K21 TTGR.TCGCATGATCKAAACATCAAAGGAT ..TTTTCTTTGGAAAATTCCACTTTGAGAT C_estertheticum TTGA.TCGCATGATCTTAACATCAAAGGAA ..TTT..TTCGG..AATTTCACTTTGAGAT C_botulinum_A AGAA.TCGCATGATTTTCTTATCAAAGATT ..T............ATT.. GCTTTGAGAT C_sporogenes AGAA.TCGCATGATTTTCTTATCAAAGATT ..T............ATT.. GCTTTGAGAT C_argentinense AAGG.TCGCATGACTTTTATACCAAAGGAG ..T............AATCC GCTATGAGAT C_subterminale AAGG.TCGCATGACTTTTATACCAAAGGAG ..T............AATCC GCTATGAGAT C_tetanomorphum TTTT.CCGCATGAAAAACTAATCAAAGGAG ..T............AAT.C GCTTTGAGAT C_pasteurianum AGTT.TCACATGGAGCTTTAATTAAAGGAG ..T............AATCC GCTTTGAGAT C_collagenovorans TTGA.TCGCATGGTCGAAATATTAAAGGAG ..T............AATCC GCTTACAGAT C_histolyticum TTTA.ATGCATGTTAGAAAG ATTAAAGGAG ..............CAATCC GCTTTGAGAT C_tyrobutyricum AGTT.TCACATGGAATTTGG ATGAAAGGAG ..T............AATTC GCTTTGAGAT C_tetani GGTT.TCGCATGAAACTTTAACCAAAGGAG ..T............AATCT GCTTTGAGAT C_barkeri GACA.TCGCATGGTGTT... .TTAATGAAA ............ACTCCGGT GCCATGAGAT C_thermocellum GGCA.TCGTCCTGTTAT... .CAAAGGAGA ............AATCCGGT ...ATGAGAT Pep_prevotii AGTC.TCGCATGGNGTTATCATCAAAGA.. ..............TTTATC GGTGTAAGAT C_innocuum ACGGAGCGCATGCTCTGTATATTAAAGCGCCCTTCAAGGCGTGAAC.... ....ATGGAT S_ruminantium AGTTTCCGCATGGGAGCTTG ATTAAAGATG GCCTCTACTTGTAAGCTATC GCTTTGCGAT TCAAAGGAG

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. 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/BIOEDIT/bioedit.html

38. MACCLADE 4

39. Missing G Extra T

42. Hang on, what makes a good alignment?

43. What makes a good alignment

44. What makes a good alignment Sequence Alignment Structural Alignment

45. What makes a good alignment

46. I hate ad hoc algorithms and manual sequence alignment!Is there an alternative?

47. An evolutionary hypothesis Hypothesis/Model AG 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 G->A Insert CC Insert T G->C T->C Delete G AAT AAC AC ACCG ACC Observations

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. Probabilistic models and biology 3D structure of myoglobin, showing six alpha-helices.

50. State of the art