Motif Search

What are Motifs

• Motif (dictionary) A recurrent thematic element, a common theme

Find a common motif in the text

Find a short common motif in the text

Motifs in biological sequences

Sequence motifs represent a short common sequence (length 4-20) which is highly represented in the data

Challenges in biological sequencesMotifs are usually not exact words

How to present non exact motifs?

• Consensus string NTAHAWT

May allow “degenerate” symbols in string, e.g., N = A/C/G/T; W = A/T; H=not G; S = C/G; R = A/G; Y = T/C etc.

• Position Weight Matrix (PWM)

Probability for each base

in each position A

T

GC

1 2 3 4 5 6

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

Motifs in biological sequences

– Regulatory motifs in DNA (transcription factor binding sites)

– Functional site in proteins (Phosphorylation site)

What can we learn from these motifs?

DNA Regulatory Motifs

• Transcription Factors (TF) are regulatory protein that bind to regulatory motifs near the gene and act as a switch bottom (on/off)

– TF binding motifs are usually 6 – 20 nucleotides long

– located near target gene, mostly upstream the transcription start site

Transcription Start Site

TF2motif

TF1motif

Gene X

TF1 TF2

Can we find TF targets using a bioinformatics approach?

P53 is a transcription factorinvolved in most human cancers

We are interested to identify the genes regulated by p53

Finding TF targets using a bioinformatics approach?

Scenario 1 : Binding motif is known (easier case)

Scenario 2 : Binding motif is unknown (hard case)

Scenario 1 : Binding motif is known

• Given a motif (e.g., consensus string, or weight matrix), find the binding sites in an input sequence

Given a consensus :

For each position l in the input sequence, check if substring starting at position l matches the motif. Example: find the consensus motif NTAHAWT in the promoter of a gene

>promoter of gene AACGCGTATATTACGGGTACACCCTCCCAATTACTACTATAAATTCATACGGACTCAGACCTTAAAA…….

Given a Position Weight Matrix (PWM):

Seq 1 AAAGCCCSeq 2 CTATCCASeq 3 CTATCCCSeq 4 CTATCCCSeq 5 GTATCCCSeq 6 CTATCCCSeq 7 CTATCCCSeq 8 CTATCCCSeq 9 TTATCTG

Starting from a set of aligned motifs

Given a Position Weight Matrix (PWM):

1 1 9 9 0 0 0 1 A

6 0 0 0 0 9 8 7 C

1 0 0 0 1 0 0 1 G

1 8 0 0 8 0 1 0 T

W

.11 .11 1 1 0 0 0 .11 A

.67 0 0 0 0 1 .89 .78 C

.11 0 0 0 .11 0 0 .11 G

.11 .89 0 0 .89 0 .11 0 T

Counts of each baseIn each column

Probability of each baseIn each column

Wk = probability of base in column k

• Given a string s of length l = 7• s = s1s2…sl

• Pr(s | W) =

• Example: Pr(CTAATCCG) = 0.67 x 0.89 x 1 x 1 x 0.89x 1 x 0.89 x 0.11

k

Wsk k

Given a Position Weight Matrix (PWM)

• Given sequence S (e.g., 1000 base-pairs long)• For each substring s of S,

– Compute Pr(s|W)

– If Pr(s|W) > some threshold, call that a binding site

• In DNA sequences we need to search both strands AGTTACACCA

TGGTGTAACT (reverse complement)

Scenario 2 : Binding motif is unknown

“Ab initio motif finding”

Ab initio motif finding: Expectation Maximization

• Local search algorithm

- Start from a random PWM– Move from one PWM to another so as to

improve the score which fits the sequence to the motif

– Keep doing this until no more improvement is obtained : Convergence to local optima

Expectation Maximization

• Let W be a PWM . Let S be the input sequence . • Imagine a process that randomly searches,

picks different strings matching W and threads them together to a new PWM

Expectation Maximization

• Find W so as to maximize Pr(S|W)

• The “Expectation-Maximization” (EM) algorithm iteratively finds a new motif W that improves Pr(S|W)

Expectation Maximization

PWMStart from a random motif1.

Scan sequence for good matches to the current motif.

2.

3. Build a new PWM out of these matches, and make it the new motif

The final PWM represents the motif which is mostly enriched in the data

-A letter’s height indicates the information it contains -The top letter at each position can be read to obtain the consensus sequence (motif)

The PWM can be also represented as a sequence logo

Are common motifs the right thing to search for ?

?

Solutions:

-Searching for motifs which are enriched in one set but not in a random set

- Use experimental information to rank the sequences according to their binding affinity and search for enriched motifs at the top of the list

Searching for enriched motifs in a ranked list

1

234

Bin

ding

aff

init

y

k= number of motifs in the top of the listm= number of sequences in the top of the list

n= number of total motifs foundN= total number of sequences

The P reflects the surprise of seeing the observed density of motif occurrences at the top of the list compared to the rest of the list.

Hyper Geometric (HG) Distribution test

Searching for enriched motifs in ranked list

1

234

Bin

ding

aff

init

y

k= number of motifs in the top of the listm= number of sequences in the top of the list

n= number of total motifs foundN= total number of sequences

Choosing the best way to cut the list (minimal HG score)

Finding the p53 binding motif in a set of p53 target sequences which are ranked according

to binding affinity >affinity = 5.962ACAAAAGCGUGAACACUUCCACAUGAAAUUCGUUUUUUGUCCUUUUUUUUCUCUUCUUUUUCUCUCCUGUUUCU>affinity = 5.937AAUAAAAAUAGAUAUAAUAGAUGGCACCGCUCUUCACGCCCGAAAGUUGGACAUUUUAAAUUUUAAUUCUCAUGA> affinity = 5.763UCACACUUGAAUGUGCUGCACUUUACUAGAAGUUUCUUUUUCUUUUUUUAAAAAUAAAAAAAGAGGAGAAAAAUGC>affinity = 5.498GCUGGUGCAAGUUUCCGGUAAAAAUAAUGAUGUUCUAGUCAUUCAUAUAUACGAUACAAAAAUAACA...

http://drimust.technion.ac.il/

P[ED]XK[RW][RK]X[ED]

Protein Motifs

Protein motifs are usually 6-20 amino acids long andcan be represented as a consensus/profile:

or as PWM

Protein Domains• In additional to protein short motifs, proteins are

characterized by Domains. • Domains are long motifs (30-100 aa) and are

considered as the building blocks of proteins (evolutionary modules).

The zinc-finger domain

Some domains can be found in many proteins with different functions:

….while other domains are only found in proteins with a certain function…..

MBD= Methylated DNA Binding Domain

Varieties of protein domains

Page 228

Extending along the length of a protein

Occupying a subset of a protein sequence

Occurring one or more times

Pfam

> Database that contains a large collection of multiple sequence alignments of protein domains

Based on Profile hidden Markov Models (HMMs).

HMM in comparison to PWM is a modelwhich considers dependencies between thedifferent columns in the matrix (different residues) and is thus much more powerful!!!!

http://pfam.sanger.ac.uk/

Profile HMM (Hidden Markov Model)can accurately represent a MSA

D16 D17 D18 D19

M16 M17 M18 M19

I16 I19I18I17

100%

100% 100%

100%

D 0.8S 0.2

P 0.4R 0.6

T 1.0 R 0.4S 0.6

X XX X

50%

50%D R T RD R T SS - - SS P T RD R T RD P T SD - - SD - - SD - - SD - - R

16 17 18 19

Match

delete

insert