1. Local Multiple Sequence Alignment Sequence Motifs

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

3. Regulatory Motifs DNA in every cell is identical
Different cells have different functions
Transcription is crucial aspect of regulation
Transcription factors (TFs) affect transcription rates
TFs bind to regulatory motifs
Motifs are 6 – 20 nucleotides long
Activators and repressors
Usually located near target gene, mostly upstream
Transcription
Start Site
MCM1
SBF
Gene X
MCM1
motif
SBF
motif

4. E. Coli promoter sequences

5. 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?

6. 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

7. Consensus considerations
Transcription start site
-35 hexamer
-10 hexamer
spacer
interval
TTGACA
bases
TATAAT
5 - 9 bases
A weight matrix contains more information 1 2 3 4 5 6 1 2 3 4 5 6 A A T T G G C C
-35
-10
Based on ~450
known promoters

8. PSPM – Position Specific Probability Matrix
Represents a motif of length k
Defines Pi{A,C,G,T} for i={1,..,k}.
Pi (A) – frequency of nucleotide A in position i. 1 2 3 4 5 A
0.1
0.25
0.05
0.7
0.6 C
0.3
0.8
0.15 T
0.5 G
0.2

9. PSPM – Position Specific Probability Matrix
Represents a motif of length k
Defines Pi{A,C,G,T} for i={1,..,k}.
Pi (A) – frequency of nucleotide A in position i.
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
0.8
0.15 T
0.5 G
0.2

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

11. 2. 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.

12. 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… 1 2 3 4 5 A
0.1
0.25
0.05
0.7
0.6 C
0.3
0.8
0.15 T
0.5 G
0.2

13. 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 1 2 3 4 5 A
0.1
0.25
0.05
0.7
0.6 C
0.3
0.8
0.15 T
0.5 G
0.2

14. 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
0.8
0.15 T
0.5 G
0.2

15. 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 matrix: 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.

16. 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): 1 2 3 4 5 A
0.1
0.25
0.05
0.7
0.6 1 2 3 4 5 A
0.4
0.2
2.8
2.4 1 2 3 4 5 A
-1.322
-2.322
1.485
1.263

17. Calculating using Log Odds Matrix
Odds  0 implies random match; Odds > 0 implies real match (?).
Example: sequence = ATGCAAGTCT…
Position 1: ATGCA =-2.7 odds= 2-2.7=0.15
Position 2: TGCAA =5.42 odds=25.42=42.8 1 2 3 4 5 A
-1.32
-2.32
1.48
1.26 C
0.26
1.68
-0.74 T G
-0.32

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

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

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

21. 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 /( )=0.003

22. 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).

23. 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!

24. 3. 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.

25. Difficulties in Computational Identification
Each motif can appear in any of m-k columns; there are (m-k)n possibilities.
Noise: Mismatches are allowed, the motif is not exact. Not all sequences contain the motif.
Statistical significance: k is short (6-20 nucleotides). m ranges from 10s (prokaryotes) to 1000s (eukaryotes) of nucleotides. => a random motif can appear by chance in sequences.

26. 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> …
Current status: Problem is still open.

27. Tools on the Web
MEME – Multiple EM for Motif Elicitation.
metaMEME- Uses HMM method
MAST-Motif Alignment and Search Tool
TRANSFAC - database of eukaryotic cis-acting regulatory DNA elements and trans-acting factors.
eMotif - allows to scan, make and search for motifs in the protein level.