CSCE555 BioinformaticsCSCE555 Bioinformatics

Lecture 10 Motif Discovery

Meeting: MW 4:00PM-5:15PM SWGN2A21Instructor: Dr. Jianjun HuCourse page: http://www.scigen.org/csce555

University of South CarolinaDepartment of Computer Science and Engineering2008 www.cse.sc.edu.

HAPPY CHINESE NEW YEAR

OutlineOutline

Introduction to DNA MotifMotif Representations (Recap)Motif database searchAlgorithms for motif discovery

04/21/23 2

What is a DNA motif? What is a DNA motif? Motif A Recurring patternA short conserved sequence

pattern associated with distinct functions of a protein or DNA

DNA motifs asTranscription Factor biding sites

Transcription: binding sites Transcription: binding sites (DNA) and factors (proteins)(DNA) and factors (proteins)

Colored lines are binding sites: DNA sequence patterns.Blobs are factors (proteins) that recognize binding sites.

Example:Example:Transcription Factor Binding Transcription Factor Binding SitesSites

ERE

EstrogenReceptor Transcription start

DNA

Gene ERE Sequence

Efp … a g g g t c a t g g t g a c c c t …

TERT … t t g g t c a g g c t g a t c t c …

Oxytocin … g c g g t g a c c t t g a c c c c …

Lactoferrin … c a g g t c a a g g c g a t c t t …

Angiotensin … t a g g g c a t c g t g a c c c g …

VEGF … a t a a t c a g a c t g a c t g g …

(estrogen response element)

Why are sequence patterns Why are sequence patterns useful--revisiteduseful--revisitedIn the context of transcriptional

regulation, sequence patterns can be used to help answer several questions.

What transcription factors are involved in regulating my gene?

Does my gene contain a DNA binding domain?

What novel transcription factor binding sites does my set of co-regulated genes contain?

How do we represent How do we represent sequence patterns?sequence patterns?The three most common

pattern representation languages:

regular expressions (e.g.,leucine zipper)

profiles (PWMs, PSSMs etc.) hidden Markov models

(HMMs)

1) Regular expressions define 1) Regular expressions define sets of sequences that they sets of sequences that they matchmatch

Sp1 binds to DNA via 3 zinc-finger binding domains:

C-X(2,4)-C-X(3)-[LIVMFYWC]-X(8)-H-

X(3,5)-H

These particular domains recognize Sp1 binding sites:

GRGGCRGGW Transcription factor Sp1 bindingto DNA

2) Profiles are built from 2) Profiles are built from multiple alignments of multiple alignments of instances of a patterninstances of a pattern

Example: nuclear hormone receptor transcription factor binding site profile derived from experimentally determined sites.

Observed counts can be converted to frequencies by dividing by the number of observed instances.

So profiles are probabilistic models of sequence patterns.

Counts of number of times each letter is observed at each position in pattern.

Lecture 3.2 10

3) Making a Markov 3) Making a Markov ModelModel

A C A - - - A T GT C A A C T A T CA C A C - - A G CA G A - - - A T CA C C G - - A T C

[AT][CG][AC][ACGT-](3)A[TG][GC]

~3600 possible valid sequences

Lecture 3.2 11

Making a Markov Model of Making a Markov Model of MotifMotif

A:0.8T:0.2

C:0.8G:0.2

A:0.8C:0.2

A:1.0 T:0.8G:0.2

C:0.8G:0.2

C:0.4G:0.2T:0.2A:0.2

1.0 1.0 0.4

0.60.6

0.4

1.0 1.0

P(ACAC--ATC)=0.8x1.0x0.8x1.0x0.8x1.0x0.6x0.4 x0.6x1.0x1.0x0.8x1.0x0.8 = 0.0047

How to score the match of a How to score the match of a sequence against three motif sequence against three motif models?models?Regular express: exact match or

fuzzy matchProfile: sum of log-oddsHMM: probability score P(s|H)

OutlineOutline

Introduction to DNA MotifMotif Representations (Recap)Motif database searchAlgorithms for motif discovery

04/21/23 13

How do we search for How do we search for occurrences of known occurrences of known patterns?patterns?Tools exist that allow us to search for one or more

known sequence patterns in one or more sequences in different ways.

The patterns can come from a database of known patterns or be novel patterns we have discovered using pattern discovery software or other means.

Some tools treat each pattern independently; others look for groups of matches to patterns.

All tools compare each pattern to each position and compute a score which can be the number of mutations (regular expression patterns) or a probability or log-odds (profiles and HMMs).

Many useful databases of Many useful databases of patterns have been compiledpatterns have been compiledTRANSFAC – transcription factor

binding sites (profiles)PROSITE – protein sites and domains

(regular expressions and profiles)EPD – eukaryotic promoters (profiles)PFAM – protein families and domains

(HMMs)BLOCKS – protein families (profiles)

Searching for known Searching for known patterns in a given sequence patterns in a given sequence MOTIF – search protein sequence

against Prosite, PFAM etc.; search DNA sequence against TRANSFAC

PROFILESCAN – search protein sequence against Prosite database of profiles or regular expressions

MAST – search for occurrences of one or more patterns in a DNA sequence (or database of sequences)

OutlineOutline

Introduction to DNA MotifMotif Representations (Recap)Motif database searchAlgorithms for motif discovery

04/21/23 17

The Motif Discovery The Motif Discovery ProblemProblemWe are given a set of sequences, each

containing an instance of an unknown motif. Find the motif.

Multiple, local sequence alignment.A clean, computer-sciencey problem. A

bit too clean, we should be suspicious…

In Real LifeIn Real LifeA microarray experiment indicates that

50 genes share similar expression patterns.

Do they share a common type of transcription factor binding site?◦ Almost certainly some of the genes were

included erroneously: experimental noise.◦ Perhaps they share a common mRNA

degradation signal.Is the TFBS near the transcription start

site?◦ Yeast: probably. Human: who knows?

Approaches to Motif Approaches to Motif DiscoveryDiscoveryMatrix-based:

◦Gibbs Sampling - most popular.◦Expectation maximization.◦Stormo’s greedy algorithm.

Consensus sequence-based:◦Several algorithms by Pevzner.◦Box-finder of Kielbasa et al.

Three Ingredients of Almost Three Ingredients of Almost any Bioinformatics Methodany Bioinformatics Method

1. Search space (haystack)2. Scoring scheme3. Search algorithm (= optimization

technique)

Strictly speaking, Gibbs sampling and expectation-maximization are search algorithms. They are not specific to motif discovery; indeed they were first used in other contexts.

Mathematically precise formulation of the problem

Gibbs Sampling: Gibbs Sampling: Simplifying Simplifying AssumptionsAssumptionsThe width of the motif is known in

advance.

No indels (gaps).

Each sequence contains precisely one instance of the motif.

The sequences are single-stranded (e.g. mRNA).

Search SpaceSearch Space

N

Length = L

Motif width = W

Size of search space = (L – W + 1)N

L=100, W=15, N=10 size 1019

Scoring SchemeScoring SchemeAssign a numeric score to any

proposed answer.

What score should this get?

caga

ctga

cacc

cgca

Some DefinitionsSome Definitions

caga

ctga

cacc

cgca

1 2 3 4

a 0 2 0 3

c 4 0 2 1

g 0 1 2 0

t 0 1 0 0

count matrix: cki =

k

i

• pki = cki / N

• pi = background abundance of ith residue type

1. Based on frequentist statistics / information theory:

2. Based on Bayesian statistics:

Two Scoring SchemesTwo Scoring Schemes

W

k tgcai

ci

tgcai

cki

kiki pp1 ,,,,,,

lnscore

W

k tgcai

ci

tgcaiki

kipcN1 ,,,,,,

!!3

6lnscore

Worked ExampleWorked Example

W

k tgcai

ci

tgcaiki

kipcN1 ,,,,,,

!!3

6lnscore

1 2 3 4

a 0 2 0 3

c 4 0 2 1

g 0 1 2 0

t 0 1 0 0

2561

41 N

i

cikipcki =

N = 4pi = ¼

10532

!36

i

cikip

N

Score = 1.99 - 0.50 + 0.20 + 0.60 = 2.29

Search AlgorithmSearch AlgorithmWe want the global maximum score!

(Or as close as we can get.)

Exact algorithms (e.g. dynamic programming) would be too slow (e.g. lifetime of universe).

Therefore we resort to a heuristic algorithm: Gibbs sampling, which is a type of Monte Carlo Markov chain method.

Gibbs Sampling SearchGibbs Sampling Search

1

2

Suppose the search space is a 2D rectangle. (Typically, more than 2 dimensions!)

X

Start at a random point X.

Randomly pick a dimension.

Look at all points along this dimension.

Repeat.

Move to one of them randomly, proportional to its score π.

Gibbs Sampling for Motif Gibbs Sampling for Motif SearchSearch

Choose a random starting state.

Randomly pick a sequence.

Look at all motif positions in this sequence.

Pick one randomly proportional to exp(score).

Repeat.

Does it Work in Practice?Does it Work in Practice?Only successful cases get published!Seems more successful in microbes (bacteria &

yeast) than in animals.The search algorithm seems to work quite well,

the problem is the scoring scheme: real motifs often don’t have higher scores than you would find in random sequences by chance. I.e. the needle looks like hay.

Attempts to deal with this:◦ Assume the motif is an inverted palindrome (they often

are).◦ Only analyze sequence regions that are conserved in

another species (e.g. human vs. mouse).As usual, repetitive sequences cause problems.More powerful algorithm: MEME

1. Go to our MEME server:

http://molgen.biol.rug.nl/meme/website/meme.html

1. Fill in your emailadres, description of the sequences

2. Open the fasta formatted file you just saved with Genome2d (click “Browse”)

3. Select the number of motifs, number of sites and the optimum width of the motif

4. Click “Search given strand only”

5. Click “Start search”

Something like this will appear in your email. The results are quite self explanatory.

SummarySummaryMotif discovery and Motif search problem Motif representationGibbs sampling algorithm for motif discoveryUsing MEME (Expectation Maximization

algorithm) for motif discovery

AcknowledgementAcknowledgementZhiping Weng (Boston Uni.)Timothy L. Bailey