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From Pairwise to Multiple Alignment
WHATS TODAY?
• Multiple Sequence Alignment- CLUSTAL
• MOTIF search
Multiple Sequence Alignment
MSA
VTISCTGSSSNIGAG-NHVKWYQQLPGVTISCTGTSSNIGS--ITVNWYQQLPGLRLSCSSSGFIFSS--YAMYWVRQAPGLSLTCTVSGTSFDD--YYSTWVRQPPGPEVTCVVVDVSHEDPQVKFNWYVDG--ATLVCLISDFYPGA--VTVAWKADS--AALGCLVKDYFPEP--VTVSWNSG---VSLTCLVKGFYPSD--IAVEWWSNG--
Like pairwise alignment BUT compare n sequences instead of 2
Rows represent individual sequences Columns represent ‘same’ position
Gaps allowed in all sequences
How to find the best MSA
GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC
GTCGTAGTCGGCTCGACGTCTAGCGAGCGTGATGCGAAGAGGCGAGCGCCGTCGCGTCGTAAC
1*12*0.7511*0.5
Score=8
4*111*0.752*0.5
Score=13.25
Score : 4/4 =1 , 3/4 =0.75 , 2/4=0.5 , 1/4= 0
Alignment of 3 sequences:
Complexity: length A length B length C
Aligning 100 proteins, 1000 amino acids eachComplexity: 10300 table cellsCalculation time: beyond the big bang!
Feasible Approach
• 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
Progressive alignment (Feng & Doolittle).
– For n sequences, there are n(n-1)/2 pairs
GTCGTAGTCG-GC-TCGACGTC-TAG-CGAGCGT-GATGC-GAAG-AG-GCG-AG-CGCCGTCG-CG-TCGTA-AC
1 GTCGTAGTCG-GC-TCGAC2 GTC-TAG-CGAGCGT-GAT3 GC-GAAGAGGCG-AGC4 GCCGTCGCGTCGTAAC
1 GTCGTA-GTCG-GC-TCGAC2 GTC-TA-G-CGAGCGT-GAT3 G-C-GAAGA-G-GCG-AG-C4 G-CCGTCGC-G-TCGTAA-C
CLUSTAL method
• 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
Applies Progressive Sequence Alignment
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)
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 algorithmSAGA
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/
CAUTION !!! Different tools may give different results
Example : 7 different alignment tools produced 6 differentEstimated evolution trees
Wong et al., Science 319, January 2008
Motifs
• Motifs represent a short common sequence– Regulatory motifs (TF binding sites) – Functional site in proteins (DNA binding motif)
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 the transcription start siteTranscription Start Site
SBFmotif
MCM1motif
Gene X
MCM1 SBF
Why are motifs interesting?
• A motif is evidence of binding• A motif can help in developing hypotheses
regarding which protein is regulating the expression of a specific genes
• Mutations at particular regulatory sites can lead to disease
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?
E. Coli promoter sequences
1. Motif Representation
• 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?
CGGATATACCGG
CGGTGATAGCGG
CGGTACTAACGG
CGGCGGTAACGG
CGGCCCTAACGG
------------
CGGNNNTANCGG
TTGACA
-35
TATAAT
-10
Transcription start site
Representing the motif as a profile
-35 -10
A
T
GC
1 2 3 4 5 6
A
T
GC
1 2 3 4 5 6
Based on ~450 known promoters
0.1 0.1 0.1 0.5 0.2 0.5
0.7 0.7 0.2 0.2 0.2 0.2
0.1 0.1 0.5 0.1 0.1 0.2
0.1 0.1 0.2 0.2 0.5 0.1
0.1 0.7 0.2 0.6 0.5 0.1
0.7 0.1 0.5 0.2 0.2 0.8
0.1 0.1 0.1 0.1 0.1 0.0
0.1 0.1 0.2 0.1 0.1 0.1
1 2 3 4 5
A 10 25 5 70 60
C 30 25 80 10 15
T 50 25 5 10 5
G 10 25 10 10 20
PSPM – Position Specific Probability Matrix
• Represents a motif of length k (5)• Count the number of occurrence of each nucleotide in
each position
1 2 3 4 5
A 0.1 0.25 0.05 0.7 0.6
C 0.3 0.25 0.8 0.1 0.15
T 0.5 0.25 0.05 0.1 0.05
G 0.1 0.25 0.1 0.1 0.2
PSPM – Position Specific Probability Matrix
• Defines Pi{A,C,G,T} for i={1,..,k}.
– Pi (A) – frequency of nucleotide A in position i.
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.
1 2 3 4 5
A 0.1 0.25 0.05 0.7 0.6
C 0.3 0.25 0.8 0.1 0.15
T 0.5 0.25 0.05 0.1 0.05
G 0.1 0.25 0.1 0.1 0.2
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
1 2 3 4 5
A 0.1 0.25 0.05 0.7 0.6
C 0.3 0.25 0.8 0.1 0.15
T 0.5 0.25 0.05 0.1 0.05
G 0.1 0.25 0.1 0.1 0.2
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…
• 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
1 2 3 4 5
A 0.1 0.25 0.05 0.7 0.6
C 0.3 0.25 0.8 0.1 0.15
T 0.5 0.25 0.05 0.1 0.05
G 0.1 0.25 0.1 0.1 0.2
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: TGCAA 0.5*0.25*0.8*0.7*0.6=0.042
1 2 3 4 5
A 0.1 0.25 0.05 0.7 0.6
C 0.3 0.25 0.8 0.1 0.15
T 0.5 0.25 0.05 0.1 0.05
G 0.1 0.25 0.1 0.1 0.2
Detecting a Known Motif within a Sequence using PSPM
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.
1 2 3 4 5
A 0.1 0.25 0.05 0.7 0.6
1 2 3 4 5
A 0.4 1 0.2 2.8 2.4
1 2 3 4 5
A -1.322 0 -2.322 1.485
1.263
PSSM as Odds Score Matrix• Assumption: the background frequency of each
nucleotide is 0.25.
1. Original PSPM (Pi):
2. Odds Matrix (Oi):
3. Going to log scale we get an additive score,Log odds Matrix (log2Oi):
1 2 3 4 5
A -1.32 0 -2.32 1.48 1.26
C 0.26 0 1.68 -1.32 -0.74
T 1 0 -2.32 -1.32 -2.32
G -1.32 0 -1.32 -1.32 -0.32
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
Calculating the probability of a Match
ATGCAAG
• Position 1 ATGCA = 0.15
Calculating the probability of a Match
ATGCAAG
• Position 1 ATGCA = 0.15
• Position 2 TGCAA = 42.3
Calculating the probability of a Match
ATGCAAG
• Position 1 ATGCA = 0.15
• Position 2 TGCAA = 42.3
• Position 3 GCAAG =0.18
Calculating the probability of a match
ATGCAAG
• Position 1 ATGCA = 0.15
• Position 2 TGCAA = 42.3
• Position 3 GCAAG =0.18
P (i) = S / (∑ S)Example 0.15 /(.15+42.8+.18)=0.003
P (1)= 0.003P (2)= 0.993P (3) =0.004
Building a PSSM for short motifs
• 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).
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.
Genes:WebLogo - Output
Proteins:
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.
Motif Discovery
Motif Discovery
Example
Predicting the cAMP Receptor Protein (CRP) binding site motif
Extract experimentally defined CRP Binding Sites GGATAACAATTTCACAAGTGTGTGAGCGGATAACAAAAGGTGTGAGTTAGCTCACTCCCCTGTGATCTCTGTTACATAGACGTGCGAGGATGAGAACACAATGTGTGTGCTCGGTTTAGTTCACCTGTGACACAGTGCAAACGCGCCTGACGGAGTTCACAAATTGTGAGTGTCTATAATCACGATCGATTTGGAATATCCATCACATGCAAAGGACGTCACGATTTGGGAGCTGGCGACCTGGGTCATGTGTGATGTGTATCGAACCGTGTATTTATTTGAACCACATCGCAGGTGAGAGCCATCACAGGAGTGTGTAAGCTGTGCCACGTTTATTCCATGTCACGAGTGTTGTTATACACATCACTAGTGAAACGTGCTCCCACTCGCATGTGATTCGATTCACA
Create a Multiple Sequence Alignment GGATAACAATTTCACATGTGAGCGGATAACAATGTGAGTTAGCTCACTTGTGATCTCTGTTACACGAGGATGAGAACACACTCGGTTTAGTTCACCTGTGACACAGTGCAAACCTGACGGAGTTCACAAGTGTCTATAATCACGTGGAATATCCATCACATGCAAAGGACGTCACGGGCGACCTGGGTCATGTGTGATGTGTATCGAATTTGAACCACATCGCAGGTGAGAGCCATCACATGTAAGCTGTGCCACGTTTATTCCATGTCACGTGTTATACACATCACTCGTGCTCCCACTCGCATGTGATTCGATTCACA
XXXXXTGTGAXXXXAXTCACAXXXXXXXXXXXXACACTXXXXTXAGTGTXXXXXXX
Generate a PSSM
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!
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.
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/
