Transcript Document 7361710

CS5263 Bioinformatics
Lecture 18
Motif finding
What is a (biological) motif?
• A motif is a recurring fragment, theme or pattern
• Sequence motif: a sequence pattern of nucleotides in a
DNA sequence or amino acids in a protein
• Structural motif: a pattern in a protein structure formed
by the spatial arrangement of amino acids.
• Network motif: patterns that occur in different parts of a
network at frequencies much higher than those found in
randomized network
• Commonality:
– higher frequency than would be expected by chance
– Has, or is conjectured to have, a biological significance
(Sequence) motif finding
• Given a set of sequences
• Goal: find sequence motifs that appear in
all or the majority of the sequences, and
are likely associated with some functions
– In DNA: regulatory sequences
– In protein: functional/structural domains
Roadmap
•
•
•
•
Biological background
Representation of motifs
Algorithms for finding motifs
Other issues
– Distinguish functional vs non-functional motifs
– Search for instances of given motifs
– Interpretation of motifs
• In motif finding, understanding the
motivations, significance of the problems,
difficulties, and ideas that have been
explored are more important than knowing
the details of the existing algorithms!
• Most algorithms often perform poorly in
real challenges!
– Not necessarily a fault of algorithm designers
• Algorithms will be improved
Biological background for motif finding
Cells respond to environment
Various external
messages
Heat
Responds to
environmental
conditions
Food
Supply
Genome is fixed – Cells are
dynamic
• A genome is static
– Every cell in our body has a copy of same genome
• A cell is dynamic
– Responds to external conditions
– Most cells follow a cell cycle of division
• Cells differentiate during development
Gene regulation
• … is responsible for the dynamic cell
• Gene expression (production of protein) varies
according to:
–
–
–
–
Cell type
Cell cycle
External conditions
Location
Where gene regulation takes place
• Opening of chromatin
• Transcription
• Translation
• Protein stability
• Protein modifications
Transcriptional Regulation
•
Strongest regulation happens during transcription
•
Best place to regulate:
No energy wasted making intermediate products
•
However, slowest response time
After a receptor notices a change:
1. Cascade message to nucleus
2. Open chromatin & bind transcription factors
3. Recruit RNA polymerase and transcribe
4. Splice mRNA and send to cytoplasm
5. Translate into protein
Transcription Factors Binding to DNA
Transcriptional regulation:
• Certain transcription
factors bind to DNA
Binding recognizes DNA
substrings:
• Regulatory motifs
Regulation of Genes
Transcription Factor (TF)
(Protein)
RNA polymerase
(Protein)
DNA
Promoter
Gene
Regulation of Genes
Transcription Factor (TF)
(Protein)
RNA polymerase
(Protein)
DNA
Regulatory Element, TF binding site, TF
binding motif, cis-regulatory motif (element)
Gene
Regulation of Genes
Transcription Factor
(Protein)
RNA polymerase
DNA
Regulatory Element
Gene
Regulation of Genes
New protein
RNA
polymerase
Transcription Factor
DNA
Regulatory Element
Gene
The Cell as a Regulatory Network
If C then D
gene D
A
B
C
Make D
If B then NOT D
If A and B then D
D
gene B
D
C
Make B
If D then B
Code for protein-DNA binding?
Some knowledge exists
However, overall code
still missing
Experimental methods
• DNase footprinting
Experimental methods
• To determine protein-DNA binding site is
tedious and time-consuming
• To determine the binding specificity is
even harder
– Involves mutating different combinations of
nucleic acids in promoter region and observe
the biological effects
• Computational methods can help
Finding Regulatory Motifs
.
.
.
Given a collection of genes that are believed to be
regulated by the same protein,
Find the common TF-binding motif from promoters
Essentially a Multiple Local
Alignment
.
.
.
• Find “best” multiple local alignment
• Then why don’t we just use multiple
sequence alignment algorithms like the
Multidimensional Dynamic Programming?
Characteristics of Regulatory Motifs
• Tiny (6-12bp)
• Intergenic regions are
very long
• Highly Variable
• ~Constant Size
– Because a constant-size
transcription factor binds
• Often repeated
• Often conserved
Motif Representation
Motif representation
• Collection of exact words
– {ACGTTAC, ACGCTAC, AGGTGAC, …}
• Consensus sequence (with wild cards)
– {AcGTgTtAC}
– {ASGTKTKAC} S=C/G, K=G/T (IUPAC code)
• Position specific weight matrices
Position Specific Weight Matrix
1
2
3
4
5
6
7
8
9
A
.97
.10
.02
.03
.10
.01
.05
.85
.03
C
.01
.40
.01
.04
.05
.01
.05
.05
.03
G
.01
.40
.95
.03
.40
.01
.3
.05
.03
T
.01
.10
.02
.90
.45
.97
.6
.05
.91
A
S
G
T
K
T
K
A
C
frequency
Sequence Logo
A
C
1
2
3
4
5
6
7
8
9
.97 .10 .02 .03 .10 .01 .05 .85 .03
.01 .40 .01 .04 .05 .01 .05 .05 .03
G
T
.01 .40 .95 .03 .40 .01
.01 .10 .02 .90 .45 .97
.3
.6
.05 .03
.05 .91
Sequence Logo
A
C
G
T
1
.97
.01
.01
.01
2
.10
.40
.40
.10
3
.02
.01
.95
.02
4
.03
.04
.03
.90
5
.10
.05
.40
.45
6
7
8
9
.01 .05 .85 .03
.01 .05 .05 .03
.01 .3 .05 .03
.97 .6 .05 .91
Entropy and information content
• Entropy: a measure of uncertainty
• The entropy of a random variable X that
can assume the n different values x1, x2, . .
. , xn with the respective probabilities p1,
p2, . . . , pn is defined as
Entropy and information content
• Example: A,C,G,T with equal probability
 H = 4 * (-0.25 log2 0.25) = log2 4 = 2 bits
 Need 2 bits to encode (e.g. 00 = A, 01 = C, 10 = G, 11 = T)
 Maximum uncertainty
• 50% A and 50% C:
 H = 2 * (-0. 5 log2 0.5) = log2 2 = 1 bit
• 100% A
 H = 1 * (-1 log2 1) = 0 bit
 Minimum uncertainty
• Information: the opposite of uncertainty
 I = maximum uncertainty – entropy
 The above examples provide 0, 1, and 2 bits of information,
respectively
Entropy and information content
A
C
G
T
1
2
3
4
5
6
7
8
9
.97
.01
.01
.01
.10
.40
.40
.10
.02
.01
.95
.02
.03
.04
.03
.90
.10
.05
.40
.45
.01
.01
.01
.97
.05
.05
.3
.6
.85
.05
.05
.05
.03
.03
.03
.91
H
I
.24 1.72 .36 .63 1.60 0.24 1.40 0.85 0.58
1.76 0.28 1.64 1.37 0.40 1.76 0.60 1.15 1.42
Mean
1.15
Total
10.4
Expected occurrence in random DNA: 1 / 210.4 = 1 / 1340
Expected occurrence of an exact 5-mer: 1 / 210 = 1 /
Sequence Logo
1
2
3
4
5
6
7
8
9
A
C
.97
.10
.02
.03
.10
.01
.05
.85
.03
.01
.40
.01
.04
.05
.01
.05
.05
.03
G
T
I
.01
.40
.95
.03
.40
.01
.3
.05
.03
.01
.10
.02
.90
.45
.97
.6
.05
.91
1.76 0.28 1.64 1.37 0.40 1.76 0.60 1.15 1.42
Background-normalized Seq Logo
• Many genomes have skewed base
distribution
• In a thermophilic bacteria (i.e. living in a
hot spring), GC content can be as high as
70%.
• Thus a motif ATAT in the genome of a
thermophilic bacteria would contain more
information than a motif GCGC
Relative Entropy
• Definition 6.1. Let P and Q be two probability
measures on the same alphabet X. Then the
relative entropy (information divergence,
Kullback-Leibler distance, discrimination) from P
to Q is defined as
• Easy to prove that if Q is a uniform distribution,
D(P || Q) is equal to the Information content of P
Relative Entropy
• Background: pA = pT = 0.2, pC = pG = 0.3
• Distribution on some column of a PWM:
Case 1: pA = 0.85, pC = pG = pT = 0.05
Case 2: pG = 0.85 pC = pA = pT = 0.05
• Assuming uniform background distribution:
I1 = I2 = 1.15
• With the non-uniform background distribution:
– D1 = 1.42
– D2 = 0.95
Background-normalized Seq Logo
1
2
3
4
5
6
7
8
9
A
C
G
.97
.10
.02
.03
.10
.01
.05
.85
.03
.01
.40
.01
.04
.05
.01
.05
.05
.03
.01
.40
.95
.03
.40
.01
.3
.05
.03
T
I
I’
.01
.10
.02
.90
.45
.97
.6
.05
.91
1.76 0.28 1.64 1.37 0.40 1.76 0.60 1.15 1.42
2
.13
1.35
1.6
0.45
2
.70
1.37 1.65
Physical interpretation
• Information content is reversely
proportional to the binding energy
– High information content => lower energy =>
high affinity of binding
• Relative entropy represents the specificity
of the binding sites compared to random
DNA sequences
Real example
• E. coli. Promoter
• “TATA-Box” ~ 10bp upstream of transcription
start
• TACGAT
• TAAAAT
• TATACT
Consensus: TATAAT
• GATAAT
• TATGAT
Note: none of the instances
• TATGTT
matches the consensus perfectly
Finding Motifs
Definitions of terms
• Motif: a consensus sequence or a PWM
• Pattern: alias for motif (used in
combinatorial motif finding)
• Instance of a motif: a substring of a
sequence that “matches” to the motif
– How to define “match” will be shown later
Motif finding schemes
Conservation
Yes
No
Whole Yes Genome 1 & 2 & 3
Genome
1
Dictionary
building
genome
Phylogenetic footprinting
No Gene 1A & 1B & 1C or
“Motif finding”
Gene
Set 1
Gene Set 1 & 2 & 3
1A
1B
1C
Gene set 1
Gene set 2
Gene set 3
Genome 2
Genome 3
Genome 1
Ideally, all information should be used, at some stage.
i.e., inside algorithm vs pre- or post-processing.
Classification of approaches
• Combinatorial search
– Based on enumeration of words and
computing word similarities
– Analogy to DP for sequence alignment
• Probabilistic modeling
– Construct models to distinguish motifs vs nonmotifs
– Analogy to HMM for sequence alignment
Combinatorial motif finding
• Idea 1: find all k-mers that appeared at least m times
• Idea 2: find all k-mers that are statistically significant
• Problem: most motifs allow divergence. Each variation
may only appear once.
• Idea 3: find all k-mers, considering IUPAC code
– e.g. ASGTKTKAC, S = C/G, K = G/T
– Still inflexible
• Idea 4: find k-mers that approximately appeared at least
m times
– i.e. allow some mismatches
Combinatorial motif finding
Given a set of sequences S = {x1, …, xn}
• A motif W is a consensus string w1…wK
• Find motif W* with “best” match to x1, …, xn
Definition of “best”:
d(W, xi) = min hamming dist. between W and a word in xi
d(W, S) = i d(W, xi)
W* = argmin( d(W, S) )
Exhaustive searches
1. Pattern-driven algorithm:
For W = AA…A to TT…T
(4K possibilities)
Find d( W, S )
Report W* = argmin( d(W, S) )
Running time: O( K N 4K )
(where N = i |xi|)
Guaranteed to find the optimal solution.
Exhaustive searches
2. Sample-driven algorithm:
For W = a K-long word in some xi
Find d( W, S )
Report W* = argmin( d( W, S ) )
OR Report a local improvement of W*
Running time: O( K N2 )
Exhaustive searches
• Problem with sample-driven approach:
• If:
– True motif does not occur in data, and
– True motif is “weak”
• Then,
– random strings may score better than any
instance of true motif
Example
• E. coli. Promoter
• “TATA-Box” ~ 10bp upstream of transcription
start
• TACGAT
• TAAAAT
• TATACT
Consensus: TATAAT
• GATAAT
Each instance differs at most 2
• TATGAT
bases from the consensus
• TATGTT
None of the instances matches the
consensus perfectly
Heuristic methods
• Cannot afford exhaustive search on all
patterns
• Sample-driven approaches may miss real
patterns
• However, a real pattern should not differ
too much from its instances in S
• Start from the space of all words in S,
extend to the space with real patterns
Some of the popular tools
• Consensus (Hertz & Stormo, 1999)
• WINNOWER (Pevzner & Sze, 2000)
• MULTIPROFILER (Keich & Pevzner,
2002)
• PROJECTION (Buhler & Tompa, 2001)
• WEEDER (Pavesi et. al. 2001)
• And a dozen of others
Consensus
Algorithm:
Cycle 1:
For each word W in S
For each word W’ in S
Create alignment (gap free) of W, W’
Keep the C1 best alignments, A1, …, AC1
ACGGTTG ,
ACGCCTG ,
CGAACTT ,
AGAACTA ,
GGGCTCT …
GGGGTGT …
Algorithm (cont’d):
Cycle i:
For each word W in S
For each alignment Aj from cycle i-1
Create alignment (gap free) of W, Aj
Keep the Ci best alignments A1, …, ACi
• C1, …, Cn are user-defined heuristic constants
Running time:
O(kN2) + O(kN C1) + O(kN C2) + … + O(kN Cn)
= O(kN2 + NCtotal)
Where Ctotal = i Ci, typically O(nC), where C is a
big constant
Extended sample-driven (ESD)
approaches
• Hybrid between pattern-driven and sample-driven
• Assume each instance does not differ by more than α
bases to the motif ( usually depends on k)
motif
instance

α-neighborhood
The real motif will reside in the neighborhood of some words in S.
Instead of searching all 4K patterns,
we can search the -neighborhood
of every word in S.
WEEDER
• Naïve: N Kα 3α NK
# of patterns to test
# of words in sequences
Better idea
• Using a joint suffix tree, find all patterns
that:
– Have length K
– Appeared in at least m sequences with at
most α mismatches
• Post-processing
WEEDER: algorithm sketch
Current pattern P, |P| < K
# mismatches
(e, B)
Seq occ
A
C
G
T
T
• A list containing all eligible
nodes: with at most α
mismatches to P
• For each node, remember
#mismatches accumulated (e),
and bit vector (B) for seq occ,
e.g. [011100010]
• Bit OR all B’s to get seq
occurrence for P
• Suppose #occ >= m
– Pattern still valid
• Now add a letter
WEEDER: algorithm sketch
Current pattern P
(e, B)
A
C
G
T
T
A
• Simple extension: no branches.
– No change to B
– e may increase by 1 or no
change
– Drop node if e > α
• Branches: replace a node with
its child nodes
– Drop if e > α
– B may change
• Re-do Bit OR using all B’s
• Try a different char if #occ < m
• Report P when |P| = K
WEEDER: complexity
• Can get all D(P, S) in time
O(nN (K choose α) 3α) ~ O(nN Kα 3α).
n: # sequences. Needed for Bit OR.
• Better than O(KN 4K) since usually α << K
• Kα 3α may still be expensive for large K
– E.g. K = 20, α = 6
WEEDER: More tricks
Current pattern P
A
C
G
T
T
A
• Eligible nodes: with at most α
mismatches to P
• Eligible nodes: with at most
min(L, α) mismatches to P
– L: current pattern length
– : error ratio
– Require that mismatches to be
somewhat evenly distributed
among positions
• Prune tree at length K
MULTIPROFILER
W*
W*: ACGTACG
W:
ATGTAAG
W
W differs from W* at 
positions.
The consensus sequence
for the words in the
-neighborhood of W is
similar to W.
If we ignore all the chars
that are similar to W,
the rest may suggest
the difference between
W and W*
MULTIPROFILER: alg sketch
• For each word P in S
– Find its α-neighborhood in S
– List of words: C
W*
W
• For each position j from
1..K of the words in C
– Find the most popular char
that differ from P[j]
• Replace α positions in P
with the chars found above
W*: ACGTACG
W:
ATGTAAG
– Call the new word P’
• W* = argmin D(P’, S)
MULTIPROFILER
W*
W*: ACGTACG
W:
ATGTAAG
W
• No complexity provided in
the paper
• More efficient than
WEEDER for longer
patterns: N < Kα 3α
• How to choose α is an
issue:
– Large α: too many noises
in neighborhood
– Small α: few true instances
in neighborhood
Probabilistic modeling approaches
for motif finding
Probabilistic modeling approaches
• A motif model
– Usually a PWM
– M = (Pij), i = 1..4, j = 1..k, k: motif length
• A background model
– Usually the distribution of base frequencies in
the genome (or other selected subsets of
sequences)
– B = (bi), i = 1..4
• A word can be generated by M or B
Expectation-Maximization
• For any word W,
 P(W | M) = PW[1] 1 PW[2] 2…PW[K] K
 P(W | B) = bW[1] bW[2] …bW[K]
• Let  = P(M), i.e., the probability for any word to
be generated by M.
• Then P(B) = 1 - 
• Can compute the posterior probability P(M|W)
and P(B|W)
 P(M|W) ~ P(W|M) * 
 P(B|W) ~ P(W|B) * (1-)
Expectation-Maximization
Initialize:
Randomly assign each word to M or B
• Let Zxy = 1 if position y in sequence x is a motif, and 0
otherwise
• Estimate parameters M, , B
Iterate until converge:
• E-step: Zxy = P(M | X[y..y+k-1]) for all x and y
• M-step: re-estimate M,  given Z (B usually fixed)
Expectation-Maximization
position
5
1
Initialize
E-step
probability
1
5
9
9
M-step
• E-step: Zxy = P(M | X[y..y+k-1]) for all x and y
• M-step: re-estimate M,  given Z
MEME
•
•
•
•
•
Multiple EM for Motif Elicitation
Bailey and Elkan, UCSD
http://meme.sdsc.edu/
Multiple starting points
Multiple modes: ZOOPS, OOPS, TCM
Gibbs Sampling
• Another very useful technique for
estimating missing parameters
• EM is deterministic
– Often trapped by local optima
• Gibbs sampling: stochastic behavior to
avoid local optima
Gibbs sampling
Initialize:
Randomly assign each word to M or B
• Let Zxy = 1 if position y in sequence x is a motif, and 0
otherwise
• Estimate parameters M, B, 
Iterate:
•
•
•
•
•
Randomly remove a sequence X* from S
Recalculate model parameters using S \ X*
Compute Zx*y for X*
Sample a y* from Zx*y.
Let Zx*y = 1 for y = y* and 0 otherwise
Gibbs Sampling
probability
position
0.2
probability
0.15
0.1
0.05
0
0
2
4
6
8
10
position
12
14
16
18
Sampling
• Gibbs sampling: sample one position according to probability
•
•
– Update prediction of one training sequence at a time
Viterbi: always take the highest
Simultaneously update
EM: take weighted average
predictions of all sequences
20
Gibbs sampling motif finders
• Gibbs Sampler, based on C. Larence et.al.
Science, 1993
• AlignACE, Nat Biotech 1998, developed in
Church lab, Harvard Univ
• BioProspector, X. Liu et. al. PSB 2001 , an
improvement of AlignACE
Better background model
• Repeat DNA can be confused as motif
– Especially low-complexity CACACA… AAAAA, etc.
• Solution: more elaborate background model
– Higher-order Markov model
0th order: B = { pA, pC, pG, pT }
1st order: B = { P(A|A), P(A|C), …, P(T|T) }
…
Kth order: B = { P(X | b1…bK); X, bi{A,C,G,T} }
Has been applied to EM and Gibbs (up to 3rd order)
Limits of Motif Finders
0
???
gene
• Given upstream regions of coregulated genes:
– Increasing length makes motif finding harder –
random motifs clutter the true ones
– Decreasing length makes motif finding harder – true
motif missing in some sequences
Challenging problem
d mutations
n = 20
k
L = 1000
• (k, d)-motif challenge problem
• Many algorithms fail at (15, 4)-motif for n = 20 and L = 1000
• Combinatorial algorithms usually work better on challenge problem
– However, they are usually designed to find (k, d)-motifs
– Performance in real data varies
Motif finding in practice
• Now we’ve found some good looking
motifs
– Easiest step?
• What to do next?
– Are they real?
– How do we find more instances in the rest of
the genome?
– What are their functional meaning?
• Motifs => regulatory networks
To make sense about the motifs
• Each program usually reports a number of motifs
(tens to hundreds)
– Many motifs are variations of each other
– Each program also report some different ones
• Each program has its own way of scoring motifs
–
–
–
–
Best scored motifs often not interesting
AAAAAAAA
ACACACAC
TATATATAT
Strategies to improve results
• Combine results from different algorithms
usually helpful
– Ones that appeared multiple times are
probably more interesting
• Except simple repeats like AAAAA or ATATATATA
• Will talk about this later.
– Cluster motifs into groups. Issues:
• Measure similarities between two motifs (PWMs)
• # of clusters
Strategies to improve results
• Compare with known motifs in database
– TRANSFAC
– JASPAR
• Issues:
– Compute similarities among motifs
– How similar is similar?
Strategies to improve results
• Statistical test of significance
– Enrichment in target sequences vs
background sequences
Target set
T
Assumed to contain a
common motif, P
Background set
B
Assumed to not contain P,
or with very low frequency
Ideal case: every sequence in T has P, no sequence in B has P
Statistical test for significance
P
Target set
T
Background set + target set
B+T
N
Appeared in
n sequences
Appeared in m
sequences
• If n / N >> m / M
– P is enriched (over-represented) in T
– Statistical significance?
• If we randomly draw N sequences from (B+T), how
likely we will see at least n sequences with P?
M
Hypergeometric distribution
• A box with M balls, of which N are
red, and the rest are blue.
• We randomly draw m balls from
the box
• What’s the probability we’ll see n
red balls?
• Red ball: target sequences
• Blue ball: background sequences
 N  M  N 
 

n mn 
Hypegeom ( M , N , m, n)   
M 
 
m
• Total # of choices: (M choose m)
• # of choices to have n red balls:
(N choose n) x (M-N choose m-n)
Cumulative hypergeometric test for
motif significance
• We are interested in: if we
randomly pick m balls, how likely
that we’ll see at least n red balls?
 N  M  N 
 

min( m , N ) 
i  m  i 

cHypegeom ( M , N , m, n)  
M 
i n
 
m
This can be interpreted as the p-value for the null
hypothesis that we are randomly picking.
Alternative hypothesis: our selection favors red balls.
Equivalent: the target set T is enriched with motif P.
Or: P is over-represented in T.
 N  M  N 
 

n 1 
i  m  i 

 1 
M 
i 0
 
m
Examples
•
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Yeast genome has 6000 genes
Select 50 genes believed to be co-regulated by a common TF
Found a motif for these 50 genes
It appeared in 20 out of these 50 genes
In the whole genome, 100 genes have this motif
M = 6000, N = 50, m = 100+20 = 120, n = 20
Intuition:
– m/M = 120/6000. In Genome, 1 out 50 genes have the motif
– N = 50, would expect only 1 gene in the target set to have the motif
– 20-fold enrichment
• P-value = 6 x 10-22
• n = 5. 5-fold enrichment. P-value = 0.003
• Normally a very low p-value is needed, e.g. 10-10
ROC curve for motif significance
• Motif is usually a PWM
• Any word will have a score
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Typical scoring function: Log P(W | M) / P(W | B)
W: a word.
M: a PWM.
B: background model
• To determine whether a sequence contains a
motif, a cutoff has to be decided
– With different cutoffs, you get different number of
genes with the motif
– Hyper-geometric test first assumes a cutoff
– It may be better to look at a range of cutoffs
ROC curve for motif significance
P
Target set
T
N
Given a score cutoff
Appeared in
n sequences
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Background set + target set
B+T
Appeared in m
sequences
With different score cutoff, will have different m and n
Assume you want to use P to classify T and B
Sensitivity: n / N
Specificity: (M-N-m+n) / (M-N)
False Positive Rate = 1 – specificity: (m – n) / (M-N)
With decreasing cutoff, sensitivity , FPR 
M
ROC curve for motif significance
A good cutoff
Lowest cutoff. Every sequence
has the motif. Sensitivity = 1.
specificity = 0.
sensitivity
1
ROC-AUC: area under curve.
1: the best. 0.5: random.
Motif 1
Motif 2
Random
0
0
1-specificity
Motif 1 is more enriched than motif
2.
1
Highest cutoff. No motif can pass the cutoff. Sensitivity = 0. specificity = 1.
Other strategies
• Cross-validation
– Randomly divide sequences into 10 sets, hold 1 set
for test.
– Do motif finding on 9 sets. Does the motif also appear
in the testing set?
• Phylogenetic conservation information
– Does a motif also appears in the homologous genes
of another species?
– Strongest evidence
– However, will not be able to find species-specific ones
Other strategies
• Finding motif modules
– Will two motifs always appear in the same gene?
• Location preference
– Some motifs appear to be in certain location
• E.g., within 50-150bp upstream to transcription start
– If a detected motif has strong positional bias, may be a sign of its
function
• Evidence from other types of data sources
– Do the genes having the motif always have similar activities
(gene expression levels) across different conditions?
– Interact with the same set of proteins?
– Similar functions?
– etc.