prediction of regulatory elements controlling gene expression

1

Prediction of Regulatory Elements Controlling

Gene Expression

Martin Tompa

Computer Science & EngineeringGenome Sciences

University of WashingtonSeattle, Washington, U.S.A.

2

Outline

• Regulation of genes

• Motif discovery by overrepresentation– MEME– Gibbs sampling

• Motif discovery by phylogenetic footprinting– FootPrinter– MicroFootPrinter

3

Outline




4

DNA, Genes, and Proteins

DNA: program for cell processes

Proteins: execute cell processes

TCCAA

CGGTGC

TGAGGT

GCAC

GeneProtein

DNA

5

Regulation of Genes

• What turns genes on (producing a protein) and off?

• When is a gene turned on or off?

• Where (in which cells) is a gene turned on?

• At what rate is the gene product produced?

6

Regulation of Genes

GeneRegulatory Element

Transcription Factor(Protein)

DNA

RNA polymerase

(Protein)

7

Regulation of Genes

DNA

Regulatory Element Gene


RNA polymerase

(Protein)

8

Regulation of Genes RNA

polymerase(Protein)

DNA

New protein

Regulatory Element Gene


9

GoalIdentify regulatory elements in DNA sequences. These are:

• Binding sites for proteins

• Short sequences (5-25 nucleotides)

• Up to 1000 nucleotides (or farther) from gene

• Inexactly repeating patterns (“motifs”)

10

Outline




11

2 Types of Motif Discovery

1. Motif discovery by overrepresentation• One species

• Multiple (co-regulated) genes

2. Motif discovery by phylogenetic footprinting

• Multiple species

• One gene

12

Overrepresentation: Daf-19 Binding Sites in C. elegans

GTTGTCATGGTGACGTTTCCATGGAAACGCTACCATGGCAACGTTACCATAGTAACGTTTCCATGGTAAC che-2 daf-19 osm-1 osm-6

F02D8.3-150 -1

13

Phylogenetic Footprinting:Regulatory Element of Growth Hormone Gene

-200 -1

Chicken

Rat

Human

Dog

Sheep

AGGGGATAAGGGTATAAGGGTATAAGGGTATAAGGGTATA

14

Outline




15

MEME

• (Multiple EM for Motif Elicitation)

Bailey & Elkan, 1995

• Very general iterative method based on Expectation Maximization

• Available at meme.sdsc.edu/meme/website/intro.html

16

Overrepresented Motifs

• Given sequences X = {X1, X2, …, Xn},

find statistically overrepresented motifs of length k

• For simplicity, assume– Exactly one motif instance per sequence

– Sequences over DNA alphabet

17

Hidden Information

• Z = {Zij}, where

1, if motif instance starts at Zij = position j of Xi

0, otherwise• Iterate over probabilistic models that

could generate X and Z, trying to converge on this solution

{

18

Model Parameters

• Motif profile: 4×k matrix θ = (θrp),

r {A,C,G,T}

1 p k

θrp = Pr(residue r in position p of motif)

• Background distribution:

θr0 = Pr(residue r in random nonmotif

position)

19

Profile Example

GTTGTC 0 0 0 .4 0 0GTTTCC 0 .2 0 0 .8 1GCTACC 1 0 0 .2 0 0GTTACC 0 .8 1 .4 .2 0GTTTCC

profile θ

20

Overview: Expectation Maximization

• Goal: Find profile θ and motif positions Z that have maximum likelihood

• At each iteration:

– E-step: From θ predict likely motif positions Z

– M-step: From sequences at positions Z compute new profile θ

21

Expectation Maximization

• Goal: Find θ, Z that maximize Pr (X, Z | θ)

• At iteration t:– E-step: Z(t) = E (Z | X, θ(t))

– M-step: Find θ(t+1) that maximizes

Pr (X, Z(t) | θ(t+1))

22

E-step Details

Zij(t) = Pr(Xi | Zij=1, θ(t))

Σj Pr(Xi | Zij=1, θ(t))

Xi

j

Use θ1(t), θ2

(t), …, θk(t) Use θ0

(t)

23

M-step Details

• If Zij(t) {0,1} it would be straightforward:

Calculate profile θ1, θ2, …, θk from motif instances and θr0 from frequency of r outside of motif instances.

• But Zij(t) [0,1], so weight these

frequencies by the appropriate values of Zij

(t) .

24

Outline




25

Gibbs Sampler

• Lawrence et al., 1993• Very general iterative method, related

to Markov Chain Monte Carlo (MCMC)• Available at bayesweb.wadsworth.org/gibbs/gibbs.html

26

One Iteration of Gibbs Sampler• n motif instances each of

length kGGGTCACGGGGTGGGAGCTGAGAAGGGGTGGAG

CACGGGGGAGCCTGGAGGGGATCCGGAGGGGTG

GGCCGTGGGGAACCTGGGGGGAGCTGGGCTCAG

GGAGCGTGGAGGTGGGGTGGGAGCTGAGGGTGG

GGCTGGGGTGGCGGTGGGAGCCCAGGACGTTG

27


length k

• Remove one at random

• Form profile of remaining n-1

• Let pi be the probability with

which g[i .. i+k-1] fits profile

GGGTCACGGGGTGGGAGCTGAGAAGGGGTGGAG





i

28


length k

• Remove one at random

• Form profile of remaining n-1

• Let pi be the probability with

which g[i .. i+k-1] fits profile

• Choose to start replacement at i with probability proportional to pi

GGGTCACGGGGTGGGAGCTGAGAAGGGGTGGAG





i

29

Outline




30

FootPrinter

• Blanchette & Tompa, 2002

• First algorithm explicitly designed for phylogenetic footprinting

• Available at bio.cs.washington.edu/software.html

31

Phylogenetic Footprinting(Tagle et al. 1988)

Functional regions of DNA evolve slower than nonfunctional ones.

32

Phylogenetic Footprinting(Tagle et al. 1988)

Functional regions of DNA evolve slower than nonfunctional ones.

• Consider a set of orthologous (i.e., corresponding) sequences from different species

• Identify unusually well conserved substrings (i.e., ones that have not changed much over the course of evolution)

33

CLUSTALW multiple sequence alignment (rbcS gene)Cotton ACGGTT-TCCATTGGATGA---AATGAGATAAGAT---CACTGTGC---TTCTTCCACGTG--GCAGGTTGCCAAAGATA-------AGGCTTTACCATTPea GTTTTT-TCAGTTAGCTTA---GTGGGCATCTTA----CACGTGGC---ATTATTATCCTA--TT-GGTGGCTAATGATA-------AGG--TTAGCACATobacco TAGGAT-GAGATAAGATTA---CTGAGGTGCTTTA---CACGTGGC---ACCTCCATTGTG--GT-GACTTAAATGAAGA-------ATGGCTTAGCACCIce-plant TCCCAT-ACATTGACATAT---ATGGCCCGCCTGCGGCAACAAAAA---AACTAAAGGATA--GCTAGTTGCTACTACAATTC--CCATAACTCACCACCTurnip ATTCAT-ATAAATAGAAGG---TCCGCGAACATTG--AAATGTAGATCATGCGTCAGAATT--GTCCTCTCTTAATAGGA-------A-------GGAGCWheat TATGAT-AAAATGAAATAT---TTTGCCCAGCCA-----ACTCAGTCGCATCCTCGGACAA--TTTGTTATCAAGGAACTCAC--CCAAAAACAAGCAAADuckweed TCGGAT-GGGGGGGCATGAACACTTGCAATCATT-----TCATGACTCATTTCTGAACATGT-GCCCTTGGCAACGTGTAGACTGCCAACATTAATTAAALarch TAACAT-ATGATATAACAC---CGGGCACACATTCCTAAACAAAGAGTGATTTCAAATATATCGTTAATTACGACTAACAAAA--TGAAAGTACAAGACC

Cotton CAAGAAAAGTTTCCACCCTC------TTTGTGGTCATAATG-GTT-GTAATGTC-ATCTGATTT----AGGATCCAACGTCACCCTTTCTCCCA-----APea C---AAAACTTTTCAATCT-------TGTGTGGTTAATATG-ACT-GCAAAGTTTATCATTTTC----ACAATCCAACAA-ACTGGTTCT---------ATobacco AAAAATAATTTTCCAACCTTT---CATGTGTGGATATTAAG-ATTTGTATAATGTATCAAGAACC-ACATAATCCAATGGTTAGCTTTATTCCAAGATGAIce-plant ATCACACATTCTTCCATTTCATCCCCTTTTTCTTGGATGAG-ATAAGATATGGGTTCCTGCCAC----GTGGCACCATACCATGGTTTGTTA-ACGATAATurnip CAAAAGCATTGGCTCAAGTTG-----AGACGAGTAACCATACACATTCATACGTTTTCTTACAAG-ATAAGATAAGATAATGTTATTTCT---------AWheat GCTAGAAAAAGGTTGTGTGGCAGCCACCTAATGACATGAAGGACT-GAAATTTCCAGCACACACA-A-TGTATCCGACGGCAATGCTTCTTC--------Duckweed ATATAATATTAGAAAAAAATC-----TCCCATAGTATTTAGTATTTACCAAAAGTCACACGACCA-CTAGACTCCAATTTACCCAAATCACTAACCAATTLarch TTCTCGTATAAGGCCACCA-------TTGGTAGACACGTAGTATGCTAAATATGCACCACACACA-CTATCAGATATGGTAGTGGGATCTG--ACGGTCA

Cotton ACCAATCTCT---AAATGTT----GTGAGCT---TAG-GCCAAATTT-TATGACTATA--TAT----AGGGGATTGCACC----AAGGCAGTG-ACACTAPea GGCAGTGGCC---AACTAC--------------------CACAATTT-TAAGACCATAA-TAT----TGGAAATAGAA------AAATCAAT--ACATTATobacco GGGGGTTGTT---GATTTTT----GTCCGTTAGATAT-GCGAAATATGTAAAACCTTAT-CAT----TATATATAGAG------TGGTGGGCA-ACGATGIce-plant GGCTCTTAATCAAAAGTTTTAGGTGTGAATTTAGTTT-GATGAGTTTTAAGGTCCTTAT-TATA---TATAGGAAGGGGG----TGCTATGGA-GCAAGGTurnip CACCTTTCTTTAATCCTGTGGCAGTTAACGACGATATCATGAAATCTTGATCCTTCGAT-CATTAGGGCTTCATACCTCT----TGCGCTTCTCACTATAWheat CACTGATCCGGAGAAGATAAGGAAACGAGGCAACCAGCGAACGTGAGCCATCCCAACCA-CATCTGTACCAAAGAAACGG----GGCTATATATACCGTGDuckweed TTAGGTTGAATGGAAAATAG---AACGCAATAATGTCCGACATATTTCCTATATTTCCG-TTTTTCGAGAGAAGGCCTGTGTACCGATAAGGATGTAATCLarch CGCTTCTCCTCTGGAGTTATCCGATTGTAATCCTTGCAGTCCAATTTCTCTGGTCTGGC-CCA----ACCTTAGAGATTG----GGGCTTATA-TCTATA

Cotton T-TAAGGGATCAGTGAGAC-TCTTTTGTATAACTGTAGCAT--ATAGTACPea TATAAAGCAAGTTTTAGTA-CAAGCTTTGCAATTCAACCAC--A-AGAACTobacco CATAGACCATCTTGGAAGT-TTAAAGGGAAAAAAGGAAAAG--GGAGAAAIce-plant TCCTCATCAAAAGGGAAGTGTTTTTTCTCTAACTATATTACTAAGAGTACLarch TCTTCTTCACAC---AATCCATTTGTGTAGAGCCGCTGGAAGGTAAATCATurnip TATAGATAACCA---AAGCAATAGACAGACAAGTAAGTTAAG-AGAAAAGWheat GTGACCCGGCAATGGGGTCCTCAACTGTAGCCGGCATCCTCCTCTCCTCCDuckweed CATGGGGCGACG---CAGTGTGTGGAGGAGCAGGCTCAGTCTCCTTCTCG

34

FootPrinter• Inputs:

– evolutionary tree T– corresponding regulatory regions at leaves

• Output: motifs well conserved w.r.t. T.

35

Finding Short Motifs

AGTCGTACGTGAC... (Human)

AGTAGACGTGCCG... (Chimp)

ACGTGAGATACGT... (Rabbit)

GAACGGAGTACGT... (Mouse)

TCGTGACGGTGAT... (Rat)

Size of motif sought: k = 4

36

Most Parsimonious Solution

“Parsimony score”: 1 mutation

AGTCGTACGTGAC...

AGTAGACGTGCCG...

ACGTGAGATACGT...

GAACGGAGTACGT...

TCGTGACGGTGAT...ACGGACGT

ACGT

ACGT

37

Substring Parsimony ProblemGiven:

• phylogenetic tree T,• set of orthologous sequences at leaves of T,• length k of motif• threshold d

Problem:

• Find each set S of k-mers, one k-mer from each leaf, such that the parsimony score of S in T is at most d.

This problem is NP-hard.

38

FootPrinter’s Exact Algorithm(with Mathieu Blanchette, generalizing Sankoff and Rousseau

1975)

Wu [s] = best parsimony score for subtree rooted at node u,

if u is labeled with string s.

AGTCGTACGTG

ACGGGACGTGC

ACGTGAGATAC

GAACGGAGTAC

TCGTGACGGTG

… ACGG: 2 ACGT: 1 ...

… ACGG: 0 ACGT: 2...

… ACGG: 1 ACGT: 1 ...

…

ACGG: + ACGT: 0

...

… ACGG: 1 ACGT: 0 ...

4k entries

… ACGG: 0 ACGT: + ...

… ACGG: ACGT :0 ...



39

Wu [s] = min ( Wv [t] + d(s, t) ) v: child t of u

Running Time

Number of species

Average sequence

length

Motif length

Total time O(n k (4k + l ))

40

Improvements• Better algorithm reduces time from

O(n k (42k + l )) to O(n k (4k + l ))

• By restricting to motifs with parsimony score at most d, greatly reduce the number of table entries computed (exponential in d, polynomial in k)

• Amenable to many useful extensions (e.g., allow insertions and deletions)

41

Application to -actin Gene

Gilthead sea bream (678 bp)

Medaka fish (1016 bp)

Common carp (696 bp)

Grass carp (917 bp)

Chicken (871 bp)

Human (646 bp)

Rabbit (636 bp)

Rat (966 bp)

Mouse (684 bp)

Hamster (1107 bp)

42

Common carpACGGACTGTTACCACTTCACGCCGACTCAACTGCGCAGAGAAAAACTTCAAACGACAACATTGGCATGGCTTTTGTTATTTTTGGCGCTTGACTCAGGATCTAAAAACTGGAACGGCGAAGGTGACGGCAATGTTTTGGCAAATAAGCATCCCCGAAGTTCTACAATGCATCTG

AGGACTCAATGTTTTTTTTTTTTTTTTTTCTTTAGTCATTCCAAATGTTTGTTAAATGCATTGTTCCGAAACTTATTTGCCTCTATGAAGGCTGCCCAGTAATTGGGAGCATACTTAACATTGTAGTATTGTATGTAAATTATGTAACAAAACAATGACTGGGTTTTTGTACTTTCAGCCTTAATCTTGGGTTTTTTTTTTTTTTTGGTTCCAAAAAACTAAGCTTTACCATTCAAGATGTAAAGGTTTCATTCCCCCTGGCATATTGAAAAAGCTGTGTGGAACGTGGCGGTGCA

GACATTTGGTGGGGCCAACCTGTACACTGACTAATTCAAATAAAAGTGCACATGTAAGACATCCTACTCTGTGTGATTTTTCTGTTTGTGCTGAGTGAACTTGCTATGAAGTCTTTTAGTGCACTCTTTAATAAAAGTAGTCTTCCCTTAAAGTGTCCCTTCCCTTATGGCCTTCACATTTCTCAACTAGCGCTTCAACTAGAAAGCACTTTAGGGACTGGGATGC

ChickenACCGGACTGTTACCAACACCCACACCCCTGTGATGAAACAAAACCCATAAATGCGCATAAAACAAGACGAGATTGGCATGGCTTTATTTG

TTTTTTCTTTTGGCGCTTGACTCAGGATTAAAAAACTGGAATGGTGAAGGTGTCAGCAGCAGTCTTAAAATGAAACATGTTGGA

GCGAACGCCCCCAAAGTTCTACAATGCATCTGAGGACTTTGATTGTACATTTGTTTCTTTTTTAATAGTCATTCCAAATATTGTTATAATGCATTGTTACAGGAAGTTACTCGCCTCTGTGAAGGCAACAGCCCAGCTGGGAGGAGCCGGTACCAATTACTGGTGTTAGATGATAATTGCTTGTCTGTAAATTATGTAACCCAACAAGTGTCTTTTTGTATCTTCCGCCTTAAAAACAAAACACACTTGATCCTTTTTGGTTTGTCAAGCAAGCGGGCTGTGTTCCCCAGTGA

TAGATGTGAATGAAGGCTTTACAGTCCCCCACAGTCTAGGAGTAAAGTGCCAGTATGTGGGGGAGGGAGGGGCTACCTGTACACTGACTTAAGACCAGTTCAAATAAAAGTGCACACAATAGAGGCTTGACTGGTGTTGGTTTTTATTTCTGTGCTGCGCTGCTTGGCCGTTGGTAGCTGTTCTCATCTAGCCTTGCCAGCCTGTGTGGGTCAGCTATCTGCATGGGCTGCGTGCTGGTGCTGTCTGGTGCAGAGGTTGGATAAACCGTGATGATATTTCAGCAAGTGGGAGTTGGCTCTGATTCCATCCTGAGCTGCCATCAGTGTGTTCTGAAGGAAGCTGTTGGATGAGGGTGGGCTGAGTGCTGGGGGACAGCTGGGCTCAGTGGGACTGCAGCTGTGCT

HumanGCGGACTATGACTTAGTTGCGTTACACCCTTTCTTGACAAAACCTAACTTGCGCAGAAAACAAGATGAGATTGGCATGGCTTTATTTGTTT

TTTTTGTTTTGTTTTGGTTTTTTTTTTTTTTTTGGCTTGACTCAGGATTTAAAAACTGGAACGGTGAAGGTGACAGCAGTCGGTT

GGAGCGAGCATCCCCCAAAGTTCACAATGTGGCCGAGGACTTTGATTGCATTGTTGTTTTTTTAATAGTCATTCCAAATATGAGATGCATTGTTACAGGAAGTCCCTTGCCATCCTAAAAGCCACCCCACTTCTCTCTAAGGAGAATGGCCCAGTCCTCTCCCAAGTCCACACAGGGGAGGTGATAGCATTGCTTTCGTGTAAATTATGTAATGCAAAATTTTTTTAATCTTCGCCTTAATACTTTTTTATTTTGTTTTATTTTGAATGATGAGCCTTCGTGCCCCCCCTTC

CCCCTTTTTGTCCCCCAACTTGAGATGTATGAAGGCTTTTGGTCTCCCTGGGAGTGGGTGGAGGCAGCCAGGGCTTACCTGTACACTGACTTGAGACCAGTTGAATAAAAGTGCACACCTTAAAAATGAGGCCAAGTGTGACTTTGTGGTGTGGCTGGGTTGGGGGCAGCAGAGGGTG

Parsimony score over 10 vertebrates: 0 1 2

43

Motifs Absent from Some Species

• Find motifs – with small parsimony score

– that span a large part of the tree

• Example: in tree of 10 species spanning 760 Myrs, find all motifs with– score 0 spanning at least 250 Myrs– score 1 spanning at least 350 Myrs– score 2 spanning at least 450 Myrs– score 3 spanning at least 550 Myrs

44

Application to c-fos Gene

Asked for motifs of length 10, with 0 mutations over tree of

size 6 1 mutation over tree of size 11 2 mutations over tree of size 16 3 mutations over tree of size 21 4 mutations over tree of size 26

Puffer fish

Chicken

Pig

Mouse

Hamster

Human

10

2

7

2

2

21

0

1

1

Found: 0 mutations over tree of size 81 mutation over tree of size 163 mutations over tree of size 214 mutations over tree of size 28

45

Application to c-fos GeneMotif Score Conserved in Known?

CAGGTGCGAATGTTC 0 4 mammals

TTCCCGCCTCCCCTCCCC 0 4 mammals yes

GAGTTGGCTGcagcc 3 puffer + 4 mammals

GTTCCCGTCAATCcct 1 chicken + 4 mammals yes

CACAGGATGTcc 4 all 6 yes

AGGACATCTG 1 chicken + 4 mammals yes

GTCAGCAGGTTTCCACG 0 4 mammals yes

TACTCCAACCGC 0 4 mammals

metK in B. subtilis

http://wingless.cs.washington.edu/MicroFootPrinter/NC_000964_16080107_10_2_1141252736208002/links.html



46

Outline




47

MicroFootPrinter

• Neph & Tompa, 2006

• Designed specifically for phylogenetic footprinting in prokaryotic genomes

• Front end to FootPrinter• Available at bio.cs.washington.edu/software.html

48

Microbial Footprinting• 1454 prokaryotes with genomes completely

sequenced (as of 2/17/2011)– For any prokaryotic gene of interest, plenty of close genes

in other species available– Relatively simple genomes

• MicroFootPrinter– undergraduate Computational Biology Capstone project– Goal: simple interface for microbiologists– User specifies species and gene of interest– Automates collection of orthologous genes, cis-regulatory

sequences, gene tree, parameters

49

Demo

• MicroFootPrinter home• Examples: Agrobacterium tumefaciens

genes regulated by ChvI (with Eugene Nester)

– chvI (two component response regulator)– ropB (outer membrane protein )

http://bio.cs.washington.edu/MicroFootPrinter.html

http://bio.cs.washington.edu/MicroFootPrinter.html



50

Sample chvI motifParsimony score: 2Span: 41.10Significance score: 4.22

B. henselae -151 GCTACAATTTR. etli -90 GCCACAATTTR. leguminosarum -106 GCCACAATTTS. meliloti -119 GCCACAATTTS. medicae -118 GCCACAATTTA. tumefaciens -105 GCCACAATTTM. loti -80 GCCACATTTTM. sp. -87 GCCACATTTTO. anthropi -158 GCCACATTTTB. suis -38 GCCACATTTTB. melitensis -156 GCCACATTTTB. abortus -156 GCCACATTTTB. ovis -156 GCCACATTTTB. canis -38 GCCACATTTT

51

Sample ropB motifParsimony score: 1Span: 20.70Significance score: 1.34

Jannaschia sp. -151 CACATTTTGGR. etli -134 CACAATTTGGR. leguminosarum -135 CACAATTTGGA. tumefaciens -131 CACATTTTGGS. meliloti -128 CACATTTTGGS. medicae -128 CACATTTTGG

52

Combined ChvI MotifropB: CACATTTTGGchvI: GCCACAATTTAtu1221: TTGTCACAAT

ultimate: GYCACAWTTTGGY={C,T}

W={A,T}

prediction of regulatory elements controlling gene expression

Documents

motif instance

likely motif positions

dna sequences

gene product

motif elicitationbailey

prresidue r

proteinsshort sequences

iteration t