Multiple Sequence Alignment A survey of the various programs available and application of MSA in addressing certain biol

1. Multiple Sequence AlignmentA survey of the various programs available and application of MSA in addressing certain biological problems Jeff Mower Kiran Annaiah

2. Sequence Comparison • Aligning two sequences is the cornerstone of Bioinformatics. • Sequence alignment is the basic step upon which everything else built. • Sequence alignments are employed in • predicting de novo secondary structure of proteins, • The initial functional assignment • knowledge-based tertiary structure predictions, • Interpretation of data from genome sequencing projects • Inference of phylogenetic trees and resolution of ancestral relationships between species.

3. Sequence Comparisons • Homology Searches • Look for homologous sequences in databases using FASTA or BLAST program • Pattern Searches • Used for searching short sequence patterns • Multiple Sequence Alignment • For aligning and comparing 3 or more related sequences • Profile Analysis • A profile is created from a multiple sequence alignment

4. MSA vs Pairwise • PSA – based on seq. similarity we can identify unknown biological relationship • MSA • Similar to PSA • But also possible to identify conserved sub-patterns based on known biological relationship • High seq similarity – functional & structural similarity (PSA) • Sequences with functional and structural similarity can differ in sequences • PSA cannot detect this case • Example : Haemoglobin

5. MSA vs Pairwise • Structurally and functionally conserved molecules can differ in sequence – PSA cannot reveal conserved patterns • Comparing of 2 sequences with high similarity – patterns detection lost due to high similarity • MSA – useful in revealing critical patterns from multiple related sequences

6. Multiple Sequence Alignment • Homology • Homologous sequences, derive from common ancestor • Inferred by sequence similarity • MSA useful to demonstrate homology • Weak similarity – non-significant in pairwise comparison could be highly significant if same residues are conserved in other distantly related sequences

7. Multiple Sequence Alignment • Global or Local Alignments • Substitution Matrices and weighting gaps • Best alignment is one that represents an evolutionary scenario • Mutational events in evolution considered in MSA • Substitutions • Insertions • Deletions • BLOSUM and PAM – based on evolutionary distances • Affine gap penalty model • p = a + bL • p = a + b blog(L) • Scoring MSA • Sum of Pairs score (SP) for columns

8. http://www.people.virginia.edu/~wrp/papers/ismb2000.pdf

9. Which MSA method…? • Global Methods • Optimal Algorithms (MSA, MWT, MUSEQAL) • Progressive (MULTALIN, PILEUP, CLUSTAL, MULTAL, AMULT, DFALIGN, T-Coffee, MAP, PRRP, AMPS) • Local methods • PIMA, DIALIGN, PRALINE, POA, MACAW, BlockMaker, Iteralign • Combined(GENALIGN, ASSEMBLE, DCA) • Statistical (HMMT, SAGA, SAM, Match Box) • Parsimony(MALIGN, TreeAlign)

10. Progressive algorithm • Alignment is only an approximate solution (heuristic based) • Simplest and effective ways of MSA • Less time and less memory • Sequences are added one by one to the multiple alignment based on a pre-computed dendogram • Sequence addition is by PSA algorithm • Disadvantage • Once a sequence is aligned, cant be changed even if it conflicts with later sequence additions • Examples • PileUp, ClustalW, MultAlign, T-Coffee, etc

11. Exact Algorithms • Useful in cases where sequences are extremely divergent • Simultaneously aligns all sequences • Disadvantage • Need to generalize Needleman-Wunsch algorithm • Only for a maximum of 3 sequences • Causes exponential increase in time and memory as number of sequences increase • Examples • MSA (up to 10 closely related sequences)

12. Iterative algorithms • Iterate over a existing sub-optimal solution, modifying it at each step, until a convergence point is met. • Examples • SAGA, AMPS, Praline, IterAlign

13. Consistency-based Algorithms • Given a set of sequences, an optimal MSA is one that agrees most with all possible optimal pairwise alignments • Do not depend on specific subs. Matrix • Score associated with alignment of 2 residues depends on their indexes (position within protein sequence) • Most consistent are often the ones close to truth • Examples • T-Coffee, DiAlign

14. Multiple Alignment by profile HMM training Given n sequences , consider the following cases: • If the profile HMM is known, the following procedure can be applied: • Align each sequence S(i) to the profile separately. • Accumulate the obtained alignments to a multiple alignment. • If the profile HMM is not known, one can use the following technique in order to obtain an HMM profile from the given sequences: • Choose a length L for the profile HMM and initialize the transition and emission probabilities. • Train the model using the Baum-Welch algorithm, on all the training sequences. • Obtain the multiple alignment from the resulting profile HMM, as in the previous case. http://www.math.tau.ac.il/~rshamir/algmb/00/scribe00/html/lec06/node11.html

15. Schematic of T-Coffee

16. Testing the methods • BAliBASE benchmark • “Correct” Alignments • Core Blocks of Conserved Motifs • Typical “Hard Problem” Sets

17. BAliBASE reference sets, showing the number of alignments in each set

18. BAliBASE - Reference set 1

19. Scores based on core blocks(V1) Scores based on full-length alignment(V1)

20. Median score for Reference set 2

21. Median score for aligning subgroups of sequences in Reference set 3

22. Median score for N/C terminal extensions – Reference set 4

23. Median score for internal insertions – Reference set 5

24. Results: • Core blocks aligned well over long sequences – all programs • Due to different patterns of conservation • Alignment unreliable in the twilight zone • Iterative method did well under distinct alignment conditions, but not in the presence of an orphan sequence • Global algorithms – accurate and reliable for • equidistant sequences • divergent families of sequences and • alignment of orphan sequence with a family • Local algorithm – DiAlign • Best for N/C terminal extensions • Internal insertions

25. ClustalW vs DiAlign vs T-Coffee vs POA • ClustalW – global progressive method • Guide tree created • Successive pairwise alignment • Poa – progressive using partially ordered graphs • No tree to guide alignment of sequences • 2 most similar seqs are aligned and others are added to this one profile in a stepwise fashion • DiAlign – local algorithm • Aligns whole segments • PSA performed and ungapped alignments used • T-Coffee – progressive global & local • Performs PSA – twice, once global (ClustalW) and local (LAlign) • Results combined into primary library, then extension step • Progressive alignment using info from library

27. Results of BAliBASE testing

28. CPU time consumed by each program to align sets of increasingly long sequences

29. Results: • Poor performance by all programs with increase in evolutionary distance • Increase in seq length – better alignment • T-Coffee – best for low – moderate evolutionary distances • DiAlign good for high evolutionary distances

30. What Can We Do With MSAs? • Motif / pattern identification • Structural modeling • Phylogenetic analysis • Molecular evolutionary analyses • Identification of conserved genomic regions across species

31. Phylogenetic Analysis • Visual representation of a MSA as a tree of relationships • Many methods: • Distance: builds tree by clustering sequences according to their similarity • Parsimony: finds tree that minimizes the number of changes required • Maximum Likelihood: finds tree that maximizes likelihood given parameters • Bayesian: Markov chain Monte Carlo simulation to calculate posterior probabilities of trees

32. Determining Relationships Phylogram Cladogram (Baldauf 2003)

33. Rooted vs Unrooted Trees Rooted Unrooted (Baldauf 2003)

34. Many taxa (567), Few genes (3) (Soltis et al. 1999)

35. Few taxa (13), Many genes (61) (Goremykin et al. 2003)

36. Many taxa, Many genes Coming Soon…

37. Detecting Gene Families (Baldauf 2003)

38. Human, Gorilla, & Chimp Glycophorin gene family (Wang et al. 2003)

39. Bootstrapping (Baldauf 2003)

40. Molecular Evolution Background • Types of changes in protein-coding DNA • Silent (synonymous) • Replacement (nonsynonymous) • Based on degeneracy of the genetic code • K – frequency of change between two sequences • Ka: # of replacement changes per replacement site • Driven by natural selection • Reflect level of protein conservation • Ks: # of silent changes per silent site • Neutral, not affected by selective forces • Used to estimate the neutral mutation rate

41. Estimation of Substitution Rates • Absolute Rate • R = ½ K / T, where T = time since last common ancestor • Rates are directly comparable • Relative Rate • Two species of interest and one outgroup • Compare K from species 1 and outgroup against K from species 2 and outgroup

42. Relative Rate Rat K Rat-Kan = K Hum-Kan = + + Human K Rat-Kan - K Hum-Kan = - Kangaroo

43. Evaluation of Selective Pressures, The Ka / Ks Ratio • Ka / Ks > 1 indicates positive selection • Ka / Ks ≈ 1 indicates no selection • Ka / Ks < 1 indicates purifying selection

44. 280 homologs, Macaque-Human (Wang et al. 2003)

45. Conserved Genomic Regions • Need complete genomes or homologous genomic regions • Identify exons from distantly related species • Identify regulatory elements from more closely related species

46. (Thomas et al. 2003)

47. References • Biological Sequence Analysis – R. Durbin, S. Eddy, A. Krogh & G. Mitchison • Bioinformatics: Sequence, structure and databanks – D. Higgins & W. Taylor • Recent progress in multiple sequence alignment: a survey. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11966409&dopt=Abstract • Quality assessment of multiple alignment programs.http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12354624&dopt=Abstract • Multiple sequence alignment with the Clustal series of programs.http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12824352&dopt=Abstract • MAVID multiple alignment serverhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12824358&dopt=Abstract • Multiple alignment of sequences and structures. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12510583&dopt=Abstract • Fast algorithms for large-scale genome alignment and comparison.http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12034836&dopt=Abstract