Multiple sequence alignment

1. Multiple sequence alignment Jarno Tuimala

2. Scoring matrices

3. Uses of matrices • Sequence alignment • Database searches • Phylogenetics • Distances between sequences • As evolutionary models • For amino acids: PAM, Blosum, JTT… • For DNA: IUB… (match 1.9, mismatch 0) • For evolutionary work, matrices are replaced by mathematical models, while working with DNA sequence data

4. Adeniini Guaniini Muunnettu kuvista: http://www.bigchalk.com/cgi-bin/WebObjects/WOPortal.woa/wa/HWCDA/file?fileid=18373&flt=ga Sytosiini Tymiini

5. An example of a DNA matrix • For local alignments with this matrix, gap opening -16 and extension of -4 are typically used.

6. Sequence alignment

7. How to align sequences • On paper / with computer • Description of alignment for computer: • scoring matrix • gap penalties • Aligning is not objective • Check the results computer gives you! • Alignments can be used for • searching conserved sequence areas • searching point mutations • studying evolution of genes and species

8. Gap penalties • Gap are evolutionarily expensive. • Opening is more costly than extension • Affine gap model • Mathematically • P = c + gd • P is the total gap penalty • c is gap opening penalty • d is extension penalty • g is the (lenght of the gap - 1)

10. How to calculate an alignment score? • match: +4 • mismatch: -5 • gap opening: -16 • gap extension: -4 • 4+4+(-4)+4+(-16)+4+4+4+4+4 = 12

11. Multiple sequence alignment(MSA)

12. What is MSA? • MSA is an alignment generated from three or more sequences. • MSA is usually a global alignment, i.e., the aim is to align homologous residues (nucleotides or amino acids) in columns across the length of the whole sequences. A--GT AC-GT ACGGT -CGGT

13. Alignability of sequences • If the similarity of sequences drops too low, sequences can’t be reliably aligned (accuracy drops below acceptable). • For proteins <20% similarity • For DNA <~75% similarity • This cut-off is called twilight zone. • In other words, twilight zone marks the sequence similarity below which the observed similarity is mainly due to random variation, and not due to evolution.

14. MSA and dynamic programming • There are methods that can produce the optimal alignment (in terms of gap penalties and scoring matrices), but they are computationally very heavy. • Program MSA uses dynamic programming • In practise, dynamic programming would be good for up to about 10 sequences, and is not usually used for MSA. • But for pairwise alignment it can be used.

15. MSA methods • There are two popular methods to perform a multiple sequence alignment: • Progressive alignment • Clustal (ClustalW and ClustalX), Pileup… • Clustal is the most commonly used alignment program • Iterative alignment • SAGA… • We will review the Pileup method first

16. Progressive alignment

17. Progressive alignment • Produce pairwise alignment between all the sequences you want to align with MSA. • Dynamic programming, ktup-methods, dot matrix method…(you choose it) • Produce a “guide tree” on the basis of the pairwise distances calculated from pairwise alignments. • UPGMA, neighbor joining (you choose it) • Produce an MSA using the guide tree. • Sequences are aligned in the same order as the guide tree instructs.

18. Pairwise alignments

19. Pairwise distances No. of nucl. diffs. Absolute distance, used in Pileup/ Clustal JC-distance

20. UPGMA • Unweighted Pair Group Method with Arithmetic mean • One of the fastest and tree construction methods • Used in Pileup (GCG package) • Clustal uses neighbor joining, but calculating NJ tree is much more demanding; thus, UPGMA is demonstrated here

21. UPGMA tree

22. Constructing MSA human ACGTACGTCC chimp ACCTACGTCC gorilla ACCACCGTCC orangutan ACCCCCCTCC human ACGTACGTCC chimp ACCTACGTCC gorilla ACCACCGTCC orangutan ACCCCCCTCC maqaque CCCCCCCCCC human ACGTACGTCC chimp ACCTACGTCC gorilla ACCACCGTCC orangutan ACCCCCCTCC

23. Score of alignment • 1234 • ACGT match=1 • ACGA mismatch=0 • AGGA • 1: A-A + A-A + A-A = 1+1+1 = 3 • 2: C-C + C-G + C-G =1+0+0 = 1 • 3: G-G + G-G + G-G = 1+1+1 = 3 • 4: T-A + T-A + A-A = 0+0+1 =1 • S(alignment) = S(1) + S(2) + S(3) + S(4) = 3+1+3+1 = 8 • The higher the score, the better the alignment

24. Progressive alignment - pros and cons • Pros • Fast • Quite accurate • Cons • Once gaps are opened they can never be closed • Errors in the alignment of the first few sequences can have catastrophic effects on the whole alignment

25. Muscle – both progressive and iterative

26. Muscle algorithm From http://nar.oxfordjournals.org/cgi/content/full/32/5/1792/GKH340F2

27. Muscle – comparison results • As fast as Clustal, but at the same time: • As accurate as T-COFFEE! • T-COFFEE was previously the most accurate alignment method (or software) available