From Pairwise to Multiple Alignment

1. From Pairwise to Multiple Alignment

2. WHATS TODAY? • Multiple Sequence Alignment- CLUSTAL • MOTIF search

3. Multiple Sequence Alignment MSA

4. VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGTSSNIGS--ITVNWYQQLPG LRLSCSSSGFIFSS--YAMYWVRQAPG LSLTCTVSGTSFDD--YYSTWVRQPPG PEVTCVVVDVSHEDPQVKFNWYVDG-- ATLVCLISDFYPGA--VTVAWKADS-- AALGCLVKDYFPEP--VTVSWNSG--- VSLTCLVKGFYPSD--IAVEWWSNG-- Like pairwise alignment BUT compare nsequences instead of 2 Rows represent individual sequences Columns represent ‘same’ position Gaps allowed in all sequences

5. How to find the best MSA GTCGTAGTCGGCTCGACGTCTAGCGAGCGTGATGCGAAGAGGCGAGCGCCGTCGCGTCGTAAC 1*1 2*0.75 11*0.5 Score=8 GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC 4*1 11*0.75 2*0.5 Score=13.25 Score : 4/4 =1 , 3/4 =0.75 , 2/4=0.5 , 1/4= 0

6. Alignment of 3 sequences: • Complexity: length A length B length C • Aligning 100 proteins, 1000 amino acids each • Complexity: 10300 table cells • Calculation time: beyond the big bang!

7. Feasible Approach Progressive alignment (Feng & Doolittle). • Based on pairwise alignment scores • Build n by n table of pairwise scores • Align similar sequences first • After alignment, consider as single sequence • Continue aligning with further sequences

8. For n sequences, there are n(n-1)/2 pairs GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC

9. 1 GTCGTA-GTCG-GC-TCGAC2 GTC-TA-G-CGAGCGT-GAT3 G-C-GAAGA-G-GCG-AG-C4 G-CCGTCGC-G-TCGTAA-C 1 GTCGTAGTCG-GC-TCGAC2 GTC-TAG-CGAGCGT-GAT3 GC-GAAGAGGCG-AGC4 GCCGTCGCGTCGTAAC

10. CLUSTAL method Applies Progressive Sequence Alignment • Higgins and Sharp 1988 • ref: CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene, 73, 237–244. [Medline] An approximation strategy (heuristic algorithm) yields a possible alignment, but not necessarily the best one

11. Treating Gaps in CLUSTAL • Penalty for opening gaps and additional penalty for extending the gap • Gaps found in initial alignment remain fixed • New gaps are introduced as more sequences are added (decreased penalty if gap exists)

12. Other MSA Approaches • Progressive approach CLUSTALW (CLUSTALX) PILEUP T-COFFEE • Iterative approach: Repeatedly realign subsets of sequences. MultAlin, DiAlign. • Statistical Methods: Hidden Markov Models (only for proteins) SAM2K, MUSCLE • Genetic algorithm SAGA

13. Links to commonly used MSA tools CLUSTALWhttp://www.ebi.ac.uk/Tools/clustalw2/T-COFFEEhttp://www.ebi.ac.uk/t-coffee/MUSCLEhttp://www.ebi.ac.uk/muscle/MAFFThttp://www.ebi.ac.uk/mafft/Kalignhttp://www.ebi.ac.uk/kalign/

14. CAUTION !!! Different tools may give different results

15. Example : 7 different alignment tools produced 6 different Estimated evolution trees Wong et al., Science 319, January 2008

16. Motifs • Motifs represent a short common sequence • Regulatory motifs (TF binding sites) • Functional site in proteins (DNA binding motif)

17. DNA Regulatory Motifs • Transcription Factors bind to regulatory motifs • TF binding motifs are usually 6 – 20 nucleotides long • Usually located near target gene, mostly upstream Transcription Start Site SBF MCM1 Gene X MCM1 motif SBF motif

18. E. Coli promoter sequences

19. Challenges • How to recognize a regulatory motif? • Can we identify new occurrences of known motifs in genome sequences? • Can we discover new motifs within upstream sequences of genes?

20. 1. Motif Representation CGGATATACCGG CGGTGATAGCGG CGGTACTAACGG CGGCGGTAACGG CGGCCCTAACGG ------------ CGGNNNTANCGG • Exact motif: CGGATATA • Consensus: represent only deterministic nucleotides. • Example: HAP1 binding sites in 5 sequences. • consensus motif: CGGNNNTANCGG • N stands for any nucleotide. • Representing only consensus loses information. How can this be avoided?

21. 2 3 4 5 6 1 2 3 4 5 6 1 A A 0.1 0.1 0.1 0.5 0.2 0.5 T 0.7 0.7 0.2 0.2 0.2 0.2 0.1 0.7 0.2 0.6 0.5 0.1 T 0.7 0.1 0.5 0.2 0.2 0.8 G 0.1 0.1 0.5 0.1 0.1 0.2 G 0.1 0.1 0.1 0.1 0.1 0.0 C 0.1 0.1 0.2 0.2 0.5 0.1 C 0.1 0.1 0.2 0.1 0.1 0.1 -35 -10 Based on ~450 known promoters Representing the motif as aprofile Transcription start site -35 -10 TTGACA TATAAT

22. PSPM – Position Specific Probability Matrix • Represents a motif of length k (5) • Count the number of occurrence of each nucleotide in each position

23. PSPM – Position Specific Probability Matrix • Defines Pi{A,C,G,T} for i={1,..,k}. • Pi (A) – frequency of nucleotide A in position i.

24. Identification of Known Motifs within Genomic Sequences • Motivation: • identification of new genes controlled by the same TF. • Infer the function of these genes. • enable better understanding of the regulation mechanism.

25. PSPM – Position Specific Probability Matrix • Each k-mer is assigned a probability. • Example: P(TCCAG)=0.5*0.25*0.8*0.7*0.2

26. Detecting a Known Motif within a Sequence using PSPM • The PSPM is moved along the query sequence. • At each position the sub-sequence is scored for a match to the PSPM. • Example: sequence = ATGCAAGTCT…

27. Detecting a Known Motif within a Sequence using PSPM • The PSPM is moved along the query sequence. • At each position the sub-sequence is scored for a match to the PSPM. • Example: sequence = ATGCAAGTCT… • Position 1: ATGCA 0.1*0.25*0.1*0.1*0.6=1.5*10-4

28. Detecting a Known Motif within a Sequence using PSPM • The PSPM is moved along the query sequence. • At each position the sub-sequence is scored for a match to the PSPM. • Example: sequence = ATGCAAGTCT… • Position 1: ATGCA 0.1*0.25*0.1*0.1*0.6=1.5*10-4 • Position 2: TGCAA0.5*0.25*0.8*0.7*0.6=0.042

29. Detecting a Known Motif within a Sequence using PSSM Is it a random match, or is it indeed an occurrence of the motif? PSPM -> PSSM (Probability Specific Scoring Matrix) • odds score : Oi(n) where n {A,C,G,T} for i={1,..,k} • defined as Pi(n)/P(n), where P(n) is background frequency. Oi(n) increases => higher odds that n at position i is part of a real motif.

30. PSSM as Odds Score Matrix • Assumption: the background frequency of each nucleotide is 0.25. • Original PSPM (Pi): • Odds Matrix (Oi): • Going to log scale we get an additive score,Log odds Matrix (log2Oi):

31. Calculating using Log Odds Matrix • Odds  0 implies random match; Odds> 0 implies real match (?). • Example: sequence = ATGCAAGTCT… • Position 1: ATGCA -1.32+0-1.32-1.32+1.26=-2.7odds= 2-2.7=0.15 • Position 2: TGCAA1+0+1.68+1.48+1.26 =5.42odds=25.42=42.8

32. Calculating the probability of a Match ATGCAAG • Position 1 ATGCA = 0.15

33. Calculating the probability of a Match ATGCAAG • Position 1 ATGCA = 0.15 • Position 2 TGCAA = 42.3

34. Calculating the probability of a Match ATGCAAG • Position 1 ATGCA = 0.15 • Position 2 TGCAA = 42.3 • Position 3 GCAAG =0.18

35. Calculating the probability of a match ATGCAAG • Position 1 ATGCA = 0.15 • Position 2 TGCAA = 42.3 • Position 3 GCAAG =0.18 P (1)= 0.003 P (2)= 0.993 P (3) =0.004 P (i) = S / (∑ S) Example 0.15 /(.15+42.8+.18)=0.003

36. Building a PSSM • Collect all known sequences that bind a certain TF. • Align all sequences (using multiple sequence alignment). • Compute the frequency of each nucleotide in each position (PSPM). • Incorporate background frequency for each nucleotide (PSSM).

37. Graphical Representation – Sequence Logo • Horizontal axis: position of the base in the sequence. • Vertical axis: amount of information (bits). • Letter stack: order indicates importance. • Letter height: indicates frequency. • Consensus can be read across the top of the letter columns.

38. WebLogo - Input • http://weblogo.berkeley.edu

39. Genes: Proteins: WebLogo - Output

40. Finding new Motifs • We are given a group of genes, which presumably contain a common regulatory motif. • We know nothing of the TF that binds to the putative motif. • The problem: discover the motif.

41. Example Predicting the cAMP Receptor Protein (CRP) binding site motif

42. Extract experimentally defined CRP Binding Sites GGATAACAATTTCACA AGTGTGTGAGCGGATAACAA AAGGTGTGAGTTAGCTCACTCCCC TGTGATCTCTGTTACATAG ACGTGCGAGGATGAGAACACA ATGTGTGTGCTCGGTTTAGTTCACC TGTGACACAGTGCAAACGCG CCTGACGGAGTTCACA AATTGTGAGTGTCTATAATCACG ATCGATTTGGAATATCCATCACA TGCAAAGGACGTCACGATTTGGG AGCTGGCGACCTGGGTCATG TGTGATGTGTATCGAACCGTGT ATTTATTTGAACCACATCGCA GGTGAGAGCCATCACAG GAGTGTGTAAGCTGTGCCACG TTTATTCCATGTCACGAGTGT TGTTATACACATCACTAGTG AAACGTGCTCCCACTCGCA TGTGATTCGATTCACA

43. Create a Multiple Sequence Alignment GGATAACAATTTCACA TGTGAGCGGATAACAA TGTGAGTTAGCTCACT TGTGATCTCTGTTACA CGAGGATGAGAACACA CTCGGTTTAGTTCACC TGTGACACAGTGCAAA CCTGACGGAGTTCACA AGTGTCTATAATCACG TGGAATATCCATCACA TGCAAAGGACGTCACG GGCGACCTGGGTCATG TGTGATGTGTATCGAA TTTGAACCACATCGCA GGTGAGAGCCATCACA TGTAAGCTGTGCCACG TTTATTCCATGTCACG TGTTATACACATCACT CGTGCTCCCACTCGCA TGTGATTCGATTCACA

44. Generate a PSSM XXXXXTGTGAXXXXAXTCACAXXXXXXX XXXXXACACTXXXXTXGATGTXXXXXXX

45. PROBLEMS… • When searching for a motif in a genome using PSSM or other methods – the motif is usually found all over the place ->The motif is considered real if found in the vicinity of a gene. • Checking experimentally for the binding sites of a specific TF (location analysis) – the sites that bind the motif are in some cases similar to the PSSM and sometimes not!

46. Computational Methods • This problem has received a lot of attention from CS people. • Methods include: • Probabilistic methods – hidden Markov models (HMMs), expectation maximization (EM), Gibbs sampling, etc. • Enumeration methods – problematic for inexact motifs of length k>10. … • Current status: Problem is still open.

47. Tools on the Web • MEME – Multiple EM for Motif Elicitation. http://meme.sdsc.edu/meme/website/ • metaMEME- Uses HMM method http://meme.sdsc.edu/meme • MAST-Motif Alignment and Search Tool http://meme.sdsc.edu/meme • TRANSFAC - database of eukaryotic cis-acting regulatory DNA elements and trans-acting factors. http://transfac.gbf.de/TRANSFAC/ • eMotif - allows to scan, make and search for motifs at the protein level. http://motif.stanford.edu/emotif/