Local%20Multiple%20Sequence%20Alignment%20Sequence%20Motifs

1
Local Multiple Sequence AlignmentSequence 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
SBF
MCM1
Gene X
SBF motif
MCM1 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 CGGCGGTAACG
G CGGCCCTAACGG ------------ CGGNNNTANCGG
7
Transcription start site
Consensus considerations
-35 hexamer
-10 hexamer
spacer
interval
TTGACA
TATAAT
15 - 19 bases
5 - 9 bases
A weight matrix contains more information
2
3
4
5
6
1
2
3
4
5
6
1
A
A
0.1 0.1 0.1 0.5 0.2 0.5
T
0.7 0.7 0.2 0.2 0.2 0.2
T
G
0.1 0.1 0.5 0.1 0.1 0.2
G
C
0.1 0.1 0.2 0.2 0.5 0.1
C
-35
-10
Based on 450 known promoters
8
PSPM Position Specific Probability Matrix

• Represents a motif of length k
• Defines PiA,C,G,T for i1,..,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
9
PSPM Position Specific Probability Matrix

• Represents a motif of length k
• Defines PiA,C,G,T for i1,..,k.
• Pi (A) frequency of nucleotide A in position i.
  
• Each k-mer is assigned a probability.
• Example P(TCCAG)0.50.250.80.70.2

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
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 0.25 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
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 0.10.250.10.10.61.510-4
  

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
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 0.10.250.10.10.61.510-4
  
• Position 2 TGCAA 0.50.250.80.70.60.042

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
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 -gt PSSM (Probability Specific Scoring
  Matrix)
• odds score matrix Oi(n) where n? A,C,G,T for
  i1,..,k
• defined as Pi(n)/P(n), where P(n) is background
  frequency.
• Oi(n) increases gt 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 1 0.2 2.8 2.4
1 2 3 4 5
A -1.322 0 -2.322 1.485 1.263
17
Calculating using Log Odds Matrix

• Odds ? 0 implies random match Odds gt 0 implies
  real match (?).
• Example sequence ATGCAAGTCT
• Position 1 ATGCA -1.320-1.32-1.321.26-2.7odd
  s 2-2.70.15
• Position 2 TGCAA101.681.481.26
  5.42odds25.4242.8

1 2 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
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 /(.1542.8.18)0.0
03
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
• -gtThe 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
  columnsthere are (m-k)n possibilities.
• NoiseMismatches are allowed, the motif is not
  exact.Not all sequences contain the motif.
• Statistical significancek is short (6-20
  nucleotides).m ranges from 10s (prokaryotes) to
  1000s (eukaryotes) of nucleotides.gt 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 kgt10.
• Current status Problem is still open.

27
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/