Download - Motif Search
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
Motifs in biological sequences
– Regulatory motifs on DNA or RNA – Functional sites in proteins
What can we learn from these motifs?
Regulatory Motifs on DNA
• 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
What can we learn from these motifs?
About half of all cancer patients have a mutation in a gene called p53 which codes for a key Transcription factors.The mutations are in the DNA binding region and allowstumors to survive and continue growing even after chemotherapy severely damages their DNA
P53 Transcription Factor
Target Gene
Binding sites (moifs)
Why is P53 involved in so many cancer types?
We are interested to identify the genes regulated by p53
p53 regulated over 100 different genes
(hub)
Can we find TF targets using a bioinformatics approach?
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 find the binding sites in an input sequence
Challenges in biological sequencesMotifs are usually not exact words
……
.
How to present non exact motifs?
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 Specific Scoring Matrix (PSSM)
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
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 PSSM:
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 PSSM:
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 PSSM:• 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)
Seq1 :AAAACGTGCGTAGCAGTTACACCAACTCTA TTTTGCACGCATCGTCAATGTGGTTGAGAT
Seq2 :ACTTACTACTGGTGTAACTATATATTTTCG TGAATGATGACCACATTGATATATAAAAGC
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 PSSM represents the motif which is mostly enriched in the data
-A letter’s height indicates the information it contains
The PSSM can be also represented as a sequence logo
Presenting a sequence motif as a logo
TTCACGTACATGTACAGGTACAAG
PSSM
Letter Height
Log2S
1 2 3 4 5 6
A 0 3 0 1 1 0
G 0 0 0 0 1 4
C 0 0 4 0 1 0
T 4 1 0 0 1 0
1 2 3 4 5 6
A 0 0.75 0 1 0.25 0
G 0 0 0 0 0.25 1
C 0 0 1 0 0.25 0
T 1 0.25 0 0 0.25 0
PWM
T position 1=Log24=2T position 5=Log21=0
Divide each score by backgroundprobability 0.25
חידה
מהו המקסימום גובה שנוכל לקבל בלוגו שמתאר •מוטיב שהתקבל מרצפי חלבונים??
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
Sequencing the regions in the genome to which a protein (e.g. transcription factor) binds to.
ChIP-Seq
ChIP –SEQ
BestBinders
WeakBinders
Finding the p53 binding motif in a set of p53 target sequences which are ranked according to binding affinity
Ranked sequences list
Candidate k-mers
CTACGC
ACTTGA
ACGTGA
ACGTGC
CTGTGC
CTGTGA
CTGTAC
ATGTGC
ATGTGA
CTATGC
CTGTGC
CTGTGA
CTGTGACTGTGA
CTGTGA
CTGTGA
CTGTGA
- a word search approach to search for enriched motif in a ranked list
CTGTGA
CTGTGA
The total number of input sequences
The number of sequences containing the motif
The number of sequences at
the top of the list
The number of sequences containing the motif among the top sequences
Ranked sequences list
CTGTGA
CTGTGA
CTGTGA
CTGTGA
CTGTGA
CTGTGA
CTGTGA
CTGTGA
uses the minimal hyper geometric statistics (mHG) to find enriched
motifs
The enriched motifs are combined to get a PSSM which represents the binding
motif
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