Biological definitions for r elated sequences

1. Biological definitions for related sequences • Homologues are similar sequences in two different organisms that have been derived from a common ancestor sequence. Homologues can be described as either orthologues or paralogues. • Orthologues are similar sequences in two different organisms that have arisen due to a speciation event. Orthologs typically retain identical or similar functionality throughout evolution. • Paralogues are similar sequences within a single organism that have arisen due to a gene duplication event. • Xenologues are similar sequences that do not share the same evolutionary origin, but rather have arisen out of horizontal transfer events through symbiosis, viruses, etc.

2. So this means … Source: http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/Orthology.html

3. Sequences can be conserved across species and perform similar or identical functions. • hold information about which regions have high mutation rates over evolutionary time and which are evolutionarily conserved • identification of regions or domains that are critical to functionality. Multiple sequence alignment Sequences can be mutated or rearranged to perform an altered function. • which changes in the sequence have caused a change in the functionality. Multiple sequence alignment: the idea is to take three or more sequences and align them so that the greatest number of similar characters are aligned in the same column of the alignment.

4. What to ask yourself • How do we get a multiple alignment? (three or more sequences) • What is our main aim? • Do we go for max accuracy, least computational time or the best compromise? • What do we want to achieve each time?

5. Sequence-sequence alignment sequence sequence

6. Multiple alignment methods • Multi-dimensional dynamic programming • Progressive alignment • Iterative alignment

7. Simultaneous multiple alignmentMulti-dimensional dynamic programming The combinatorial explosion: • 2 sequences of length n • n2 comparisons • Comparison number increases exponentially • i.e. nN where n is the length of the sequences, and N is the number of sequences • Impractical for even a small number of short sequences quite quickly

8. Multi-dimensional dynamic programming(Murata et al, 1985) Sequence 1 Sequence 3 Sequence 2

9. The MSA approach MSA (Lipman et al., 1989, PNAS 86, 4412) • Calculate all pair-wise alignment scores. • Use the scores to predict a tree. • Calculate pair weights based on the tree. • Produce a heuristic alignment based on the tree. • Calculate the maximum weight for each sequence pair. • Determine the spatial positions that must be calculated to obtain the optimal alignment. • Perform the optimal alignment. • Report the weight found compared to the maximum weight previously found. • Extremely slow and memory intensive • Max 8-9 sequences of ~250 residues

10. The DCA approach DCA (Stoye et al 1997) • Iteratively split at optimal cut points • Use MSA • Concatenate

11. So in effect … Sequence 1 Sequence 3 Sequence 2

12. Multiple alignment methods • Multi-dimensional dynamic programming • Progressive alignment • Iterative alignment

13. Multiple alignment profilesGribskov et al. 1987 Core region Gapped region Core region i A C D    W Y fA.. fC.. fD..    fW.. fY.. fA.. fC.. fD..    fW.. fY.. fA.. fC.. fD..    fW.. fY.. - Gapo, gapx Gapo, gapx Gapo, gapx Position dependent gap penalties

14. Profile building Example: Each aa is represented as a frequency, penalties as weights i A C D    W Y 0.5 0 0    0 0.5 0.3 0.1 0    0.3 0.3 0 0.5 0.2    0.1 0.2 Gap penalties 1.0 0.5 1.0 Position dependent gap penalties

15. Profile-sequence alignment sequence profile ACD……VWY

16. Profile-profile alignment profile A C D . . Y profile ACD……VWY

17. Scoring profiles • Think of sequence-sequence alignment • Same principles but more information for each position Reminder: • The sequence pair alignment score S comes from the sum of the positional scores M(aai,aaj) (i.e. the substitution matrix values at each alignment position minus penalties if applicable) • Profile alignment scores are exactly the same, but the positional scores are more complex

18. Scoring a profile position Profile 1 Profile 2 A C D . . Y A C D . . Y • At each position (column) we have different residue frequencies for each amino acid (rows) SO: • Instead of saying S=M(aa1, aa2) (one residue pair) • For frequency f>0 (amino acid is actually there)we take:

19. Log-average scoring • Remember the substitution matrix formula? • In log-average scoring (von Ohsen et al, 2003) • Why is this so important? Think about it…

20. Progressive alignment 1) Perform pair-wise alignments of all of the sequences 2) Use the alignment scores to produces a dendrogram using neighbour-joining methods 3) Align the sequences sequentially, guided by the relationships indicated by the tree • Biopat (first method ever) • MULTAL (Taylor 1987) • DIALIGN (1&2, Morgenstern 1996) • PRRP (Gotoh 1996) • ClustalW (Thompson et al 1994) • Praline (Heringa 1999) • T Coffee (Notredame 2000) • POA (Lee 2002) • MUSCLE (Edgar 2004)

21. Progressive multiple alignment 1 Score 1-2 2 1 Score 1-3 3 4 Score 4-5 5 Scores Similarity matrix 5×5 Scores to distances Iteration possibilities Guide tree Multiple alignment

22. General progressive multiple alignment technique(follow generated tree) d 1 3 1 3 2 5 1 3 2 5 1 root 3 2 5 4

23. PRALINE progressive strategy d 1 3 1 3 2 1 3 2 5 4 1 3 2 5 4

24. There are problems: Accuracy is very important !!!! • Errors are propagated into theprogressivesteps “ Once a gap, always a gap” Feng & Doolittle, 1987