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Sequence Motifs
Motifs
• Motifs represent a short common sequence
– Regulatory motifs (TF binding sites)
– Functional site in proteins (DNA binding motif)
Regulatory Motifs
• Transcription Factors bind to regulatory
motifs
– Motifs are 6 – 20 nucleotides long
– Activators and repressors
– Usually located near target gene, mostly
upstream
MCM1
MCM1
motif
Transcription
Start Site
SBF
SBF
motif
Gene X
E. Coli promoter sequences
DNA binding Motif
Zn finger C2H2
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?
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
Representing the motif as a profile
Transcription
start site
-35
-10
TTGACA
3
4
5 6
0.1 0.1
0.7 0.7
0.1
0.2
0.5
0.2
0.2 0.5
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
1
A
T
G
C
2
TATAAT
-35
0.1
A
T
G
C
1
2
3
4
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
-10
5 6
Based on ~450
known promoters
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
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
• 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
0.25
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
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.
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.
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.
1
2
3
• Example:
A
0.1
0.25
0.05
sequence = ATGCAAGTCT…
4
5
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.
1
2
3
• Example:
A
0.1
0.25
0.05
sequence = ATGCAAGTCT…
C
0.3
0.25
0.8
• Position 1: ATGCA
0.1*0.25*0.1*0.1*0.6=1.5*10-4
4
5
0.7
0.6
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.
1
2
3
• Example:
A
0.1
0.25
0.05
sequence = ATGCAAGTCT…
C
0.3
0.25
0.8
• 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
4
5
0.7
0.6
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 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.
PSSM as Odds Score Matrix
•
Assumption: the background frequency of each
nucleotide is 0.25.
1
2
3
4
1. Original PSPM (Pi): A 0.1
0.25
0.05 0.7
2. Odds Matrix (Oi):
A
5
0.6
1
2
3
4
5
0.4
1
0.2
2.8
2.4
3. Going to log scale we get an additive score,
Log odds Matrix (log2Oi):
A
1
2
3
4
5
-1.322
0
-2.322
1.485
1.263
Calculating using Log Odds Matrix
• Odds  0 implies random match;
Odds > 0 implies real match (?).
• Example: sequence = ATGCAAGTCT…
1
2
• Position 1: ATGCA
-1.32+0-1.32-1.32+1.26=-2.7
odds= 2-2.7=0.15
• Position 2: TGCAA
1+0+1.68+1.48+1.26 =5.42
odds=25.42=42.8
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 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)= 0.003
P (2)= 0.993
P (3) =0.004
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).
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.
Example
Predicting the cAMP Receptor Protein (CRP)
binding site motif
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
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
Generate a PSSM
A
C
G
T
1
-0.43
0.1
-0.46
0.55
2
1.37
0.12
-1.59
-11.2
3
1.69
-1.28
-11.2
-1.43
4
-1.28
0.12
-11.2
1.32
5
0.91
-11.2
-0.46
0.47
6
1.53
-1.38
-1.48
-1.43
7
0.9
-0.48
-11.2
0.12
8
-1.37
-1.28
-11.2
1.68
9
-11.2
-11.2
1.73
-0.56
10
-11.2
-0.51
-11.2
1.72
11
-0.48
-11.2
1.72
-11.2
12
1.56
-1.59
-11.2
-0.46
13
-0.51
-0.38
-0.55
0.88
14
-11.2
0.5
0.57
0.13
15
0.17
-0.51
0.12
0.12
16
0.9
-11.2
0.5
-0.48
17
0.17
0.16
0.06
-0.48
18
-0.4
-0.38
0.82
-0.48
19
-1.38
-1.28
-11.2
1.68
20
-1.48
1.7
-11.2
-1.38
21
1.5
-1.38
-1.43
-1.28
XXXXXTGTGAXXXXAXTCACAXXXXXXX
XXXXXACACTXXXXTXGATGTXXXXXXX
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 in the protein
level.
http://motif.stanford.edu/emotif/