1. Day 7 Carlow Bioinformatics Aligning sequences

2. What is an alignment? CENTRAL concept in bioinformatics Easy if straight-forward, similar seqs –THISTHESAME or THISTHESAME –| |||| ||| | ||| || –TOSSTHEGAME TREATHELPME Hard and CPU-intensive if seqs v. diff. –THISTHESAME vs THATGAMETHE –THISTHESAME--- or THIS----THESAME –|| ||| || ||| –THAT---GAMETHE THATGAMETHE

3. Why align? Trying to establish homology by similarity Homology – having a common ancestor –whale fin, bat wing, human hand (Cuvier) –human beta globin, dog beta globin –human beta globin, human alpha globin You can have % similarity, % identity Can’t have % homology (semantics but important concept)

4. Why homology? homologous structures/molecules have similar function. Related by evolution. –more similar seqs –more recent common ancestor –more likely similar function Human hand not for locomotion Bricolage – evolutionary tinkering

5. Define terms Indel –Insertion or deletion –May get a better alignment if you put a gap in one Implies a mutation in one of the seqs –Not clear if insert in one or delete in the other

6. Optimal alignment Best guess at evolutionary relationship –Which residues/bases are homologous Depends on model of evolution and parameters of alignment –Is a gap more likely than a substitution –Is one substitution more likely than another –Transition (Y-Y or R-R) vs transversion (R-Y) –Similar shape amino acid or different No “correct” answer.

7. Global alignment Needleman & Wunsch Tries to align two sequences from 5’ to 3’ or C terminus to N terminus Assumes (only works well if) seqs are similar over their entire length So less good if there are large indels (but can identify such features) Assesses overall (functional) similarity) LARGGHYFGKISTGREFDN L FGKI T E LNAHILSFGKISTSLEDA Identify (and count) every difference/mutation

8. Local alignment Smith & Waterman Ignores whole and focuses on region or domain Use to make high quality alignments …that has good similarity ----------FGKI---------- |||| ----------FGKI---------- BLAST & FASTA homology search progs (Basic local alignment search tool)

9. Algorithm Both local and global alignment programs use “dynamic programming” Trying to use this algorithm to make optimal alignment –The alignment that tells evolutionary story True story unknown without time-travel –The alignment that has the highest score Choose/change parameters to maximise score

10. Dynamic programming Protocol: 1.Decide on substitution matrix and score for all possible pairwise comparisons 2.At start position you have three choices Align two residues and increase score Put a gap in seq 1 and decrease overall score by gap penalty Put a gap in seq 2 and decrease overall score by gap penalty 3.Iterate thro next position to end of sequence Identify highest score (local) score at righthand end (global) This is the score for the alignment (higher is better) 4.Traceback from righthand end or highest score To establish the alignment

11. 2 sequence alignment aligning GARFIELDTHECAT & GARFIELDTHERAT is easy GARFIELDTHECAT ||||||||||| || GARFIELDTHERAT

12. Scoring systems DNA In an alignment add 1 if bases identical 0 if they are different Transition/transversion? –AG purines CT pyrimidines A T C G A 1 0 0 0 T 0 1 0 0 C 0 0 1 0 G 0 0 0 1 A T C G A 2 0 0 1 T 0 2 1 0 C 0 1 2 0 G 1 0 0 2

13. Scoring comparison DNA CTAGCGATGC CGAACGACAC 1010111001 1/0 Score = 6/10 2021222112 Ts/Tv score = 15/20 Transitions 5x more common that tranversions

14. Insert gaps Sometimes, you can get a better overall alignment if you insert gaps GARFIELDTHECAT |||||||| ||| GARFIELDA--CAT is better (scores higher) than GARFIELDTHECAT |||||||| GARFIELDACAT

15. No gap penalty But there must be some sort of a gap- penalty or you can align ANY two sequences: G-R--E------AT | | | || GARFIELDTHECAT

16. Gap penalty Could set a –ve score for each indel –Linear gap penalty But mutation could be point or deletion –latter is a single event Advise to use affine (open + extend) –Open –10, extend -0.05 How choose penalty? –Start with program defaults –Use good judgment - trial and error –Investigate statistical distribution of indels

17. Scoring for similarities: proteins Gap penalty? Traded vs positive scores for matches in aligned residues Could, as with DNA, use –match=1 mismatch=0 Or …

18. Scoring system proteins When doing a similarity search against a database you are trying to decide which of many sequences is the CLOSEST match to your search sequence. Which of the following alignment pairs is better?: FGDERTHHS FGD--DHRS FGDERTHHS FGDD--HRS FGDERTHHS FGD-D-HRS Where put the gap?

19. 3 Garfield relatives GARFIELDTHECAT |||| ||||||| GARFRIEDTHECAT GARFIELDTHECAT ||| ||| ||||| GARWIELESHECAT GARFIELDTHECAT || ||||||| || GAVGIELDTHEMAT

20. Substitution matrices Top left part of a BLOSUM 90 matrix A R N D C Q E G H I L A 5 -2 -2 -3 -1 -1 -1 0 -2 -2 -2 R -2 6 -1 -3 -5 1 -1 -3 0 -4 -3 N -2 -1 7 1 -4 0 -1 -1 0 -4 -4 D -3 -3 1 7 -5 -1 1 -2 -2 -5 -5 C -1 -5 -4 -5 9 -4 -6 -4 -5 -2 -2 Q -1 1 0 -1 -4 7 2 -3 1 -4 -3 E -1 -1 -1 1 -6 2 6 -3 -1 -4 -4 G 0 -3 -1 -2 -4 -3 -3 6 -3 -5 -5 H -2 0 0 -2 -5 1 -1 -3 8 -4 -4 I -2 -4 -4 -5 -2 -4 -4 -5 -4 5 1 L -2 -3 -4 -5 -2 -3 -4 -5 -4 1 5 Symmetrical!

21. Willie Taylor’s AA Venn Diagram

22. Substitution matrices Plenty of choice –Identical = 1.0; similar (K/R, F/Y) = 0.5; rest 0.0 –PAM series, BLOSUM series, others Based on observations and counting in real seqs Blosum 90 made from aligned seqs 90% identical Main diagonal elements positive –Some more positive than others –More highly conserved (C, F etc.) Off-diagonal elements mostly negative –Some more negative than others (less likely) –Some positive score (K-R, D-E etc.)

23. Dotplot theory A T G A T A T T C T T A........... T........... G........... T........... C........... Task: align ATGATATTCTT and ATTGTTC Another way of comparing 2 sequences

24. A T G A T A T T C T T A........... T. +.. +. +.. +. T........... G........... T........... C........... Go along the first seq inserting a + wherever 2/3 bases in a moving window match. The first seq is compared to ATT (the first 3 bases in the vertical sequence) Windowsize = 3 Threshold = 2

25. A T G A T A T T C T T A........... T. +.. +. +.. +. T. +..... +... G........... T........... C........... Then go along the first seq inserting a + wherever 2/3 bases in a moving window match. The first seq is compared to TTG (the next 3 in the vertical sequence).

26. A T G A T A T T C T T A........... T. +.. +. +.. +. T. +..... +... G.. +..... +.. T... +..... +. T....... +... C........... Iterate until

27. A T G A T A T T C T T A T + + + + T + + G + + T + + T + C The human eye is particularly good at picking up structure from the pattern of dots. You might see a hint of a duplicated region in the horizontal sequence that is not so clear from the sequence itself

30. Jurassic Dotplot Mark Boguski 1 st smartass

31. Dinosaur DNA 2 (GAT1_CHICK sw:P17678 Erythroid Transcription Factor) scoring matrix: BLOSUM50, gap penalties: -12/-2 95.6% identity; Global alignment score: 2144 New seq published in Jurassic Park II Search database with “dinosaur” DNA Top hit But alignment not perfect – gaps inserted

32. TPQWVPPATQMEPPHYLELLQPPRGSPPHPSSGPLLPLSSGPPPCEARECVMARKNCGAT ::::::::::::::::::::::::::::::::::::::::::::::::::: ::::: TPQWVPPATQMEPPHYLELLQPPRGSPPHPSSGPLLPLSSGPPPCEARECV----NCGAT ATPLWRRDGTGHYLCNWASACGLYHRLNGQNRPLIRPKKRLLVSKRAGTVCSHERENCQT :::::::::::::::: ::::::::::::::::::::::::::::::::: :::: ATPLWRRDGTGHYLCN---ACGLYHRLNGQNRPLIRPKKRLLVSKRAGTVCS----NCQT STTTLWRRSPMGDPVCNNIHACGLYYKLHQVNRPLTMRKDGIQTRNRKVSSKGKKRRPPG ::::::::::::::::: :::::::::::::::::::::::::::::::::::::::: STTTLWRRSPMGDPVCN---ACGLYYKLHQVNRPLTMRKDGIQTRNRKVSSKGKKRRPPG Dinosaur Boguski Alignment Aligning the “dinosaur” DNA (upper) with the chicken (lower)

33. When global fails F1 E EF2 EKCatalyticK K F12 PLAT Two blood clotting genes Factor 12 and Plasminogen Activator have F, E, K and Catalytic domains typical of pathway By aligning PLAT’s F1 domain with F12’s F2 domain, you miss a better alignment (in grey) between the two F1 domains The alignment doesn’t recognise the second E domain in F12 but just puts a gap in the other sequence The alignment doesn’t recognise the second K domain in PLAT but forces an alignment to the other sequence

34. Alignment protocol What should real biologists do? 1.Dotplot against self to identify internal repeats 2.Dotplot against other sequence Alter windowsize and stringency 3.If similarity along whole seq do global alignment Take default parameters Then change parameters to check effect 4.If local/domain similarity only then do local alignment 5.If in doubt do local alignment 6.LOOK at the alignment and see if you can improve it: by hand – use good judgment

35. LALIGN Internal repeats really confuse global alignment Local alignment reports only BEST alignment What about sub-optimal, second best hits? If you do a dotplot repeats will be clear Use LALIGN to report not only the best alignment but also any other repeated elements –And show you the aligned sequences there

36. 2 sequence alignment Finally, some sequences are similar even if they have no recent common ancestor. Huntington's disease is caused by repeated CAG tracks in the DNA which results in polyGlutamine (Gln, Q) tracks in the protein. If you do a homology search with QQQQQQQQQQ you get hits to other proteins that have a lot of glutamines but have totally different function.

37. 2 sequence alignment Huntingtin: MATLEKLMKA FESLKSFQQQ QQQQQQQQQQ QQQQQQQQQQ PPPPPPPPPP PQLPQPPPQA Search against database hits: >MM16_MOUSE MATRIX METALLOPROTEINASE-16 Score = 34.4 bits (78), Expect = 0.18 Identities = 21/65 (32%), Positives = 25/65 (38%), Gaps = 2/65 (3%): FQQQQQQQQQQQQQQQQQQQQQQQPPPPPPPPPPPQLPQPPPQ--AQPLLPQPQPPPPPP F Q + + Q Q+ PP PPP LP PP P P+ P PP FYQYMETDNFKLPNDDLQGIQKIYGPPDKIPPPTRPLPTVPPHRSVPPADPRRHDRPKPP But not because it is involved in microtubule mediated transport! PRPs (proline-rich protein) have same problem