Phylogenetic footprinting for motif identificationcs.ucf.edu/~xiaoman/fall/lecture 18...

Phylogenetic footprinting for motif identification

Phylogenetic Footprinting(Tagle et al. 1988)

Functional sequences evolve slower than nonfunctional ones

• Consider a set of orthologous sequences from different species

• Identify unusually well conserved regions

Slides from Martin Tompa

Evolutionary Conservation

CACGTGAACCACGTGACC CACGTGAAC

One of the earliest work

Discovery of regulatory elements by a computational method for phylogenetic footprinting. Blanchette M, Tompa M.Genome Res. 2002 May;12(5):739-48.

Substring Parsimony Problem

Given:• 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.


Small Example

AGTCGTACGTGAC... (Human)

AGTAGACGTGCCG... (Chimp)

ACGTGAGATACGT... (Rabbit)

GAACGGAGTACGT... (Mouse)

TCGTGACGGTGAT... (Rat)

Size of motif sought: k = 4

Solution

Parsimony score: 1 mutation

AGTCGTACGTGAC...AGTAGACGTGCCG...ACGTGAGATACGT...GAACGGAGTACGT...TCGTGACGGTGAT...ACGG

ACGT

ACGT

ACGT

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


An Exact Algorithm(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: 2ACGT: 1...



… ACGG: +ACGT: 0...

… ACGG: 1 ACGT: 0 ...

4k

entries

… ACGG: 0 ACGT: +...

… ACGG:ACGT :0 ...




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

of u

Recurrence

O(k 42k )time per

node


of u

Running Time

O(k 42k )time per

node

Number of species

Average sequence

length

Motif length

Total time O(n k (42k + l ))


of u

Running Time


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)


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)


Common carpACGGACTGTTACCACTTCACGCCGACTCAACTGCGCAGAGAAAAACTTCAAACGACAACATTGGCATGGCTTTTGTTATTTTTGGCGCTTGACTCAGGATCTAAAAACTGGAACGGCGAAGGTGACGGCAATGTTTTGGCAAATAAGCATCCCCGAAGTTCTACAATGCATCTGAGGACTCAATGTTTTTTTTTTTTTTTTTTCTTTAGTCATTCCAAATGTTTGTTAAATGCATTGTTCCGAAACTTATTTGCCTCTATGAAGGCTGCCCAGTAATTGGGAGCATACTTAACATTGTAGTATTGTATGTAAATTATGTAACAAAACAATGACTGGGTTTTTGTACTTTCAGCCTTAATCTTGGGTTTTTTTTTTTTTTTGGTTCCAAAAAACTAAGCTTTACCATTCAAGATGTAAA

GGTTTCATTCCCCCTGGCATATTGAAAAAGCTGTGTGGAACGTGGCGGTGCAGACATTTGGTGGGGCCAACCTGTACACTGACTAATTCAAATAAAAGTGCACATGTAAGACATCCTACTCTGTGTGATTTTTCTGTTTGTGCTGAGTGAACTTGCTATGAAGTCTTTTAGTGCACTCTTTAATAAAAGTAGTCTTCCCTTAAAGTGTCCCTTCCCTTATGGCCTTCACATTTCTCAACTAGCGCTTCAACTAGAAAGCACTTTAGGGACTGGGATGC

ChickenACCGGACTGTTACCAACACCCACACCCCTGTGATGAAACAAAACCCATAAATGCGCATAAAACAAGACGAGATTGGCATGGCTTTATTTGTTTTTTCTTTTGGC

GCTTGACTCAGGATTAAAAAACTGGAATGGTGAAGGTGTCAGCAGCAGTCTTAAAATGAAACATGTTGGAGCGAACGCCCCCAAAGTTCTACAATGCATCTGAGGACTTTGATTGTACATTTGTTTCTTTTTTAATAGTCATTCCAAATATTGTTATAATGCATTGTTACAGGAAGTTACTCGCCTCTGTGAAGGCAACAGCCCAGCTGGGAGGAGCCGGTACCAATTACTGGTGTTAGATGATAATTGCTTGTCTGTAAATTATGTAACCCAACAAGTGTCTTTTTGTATCTTCCGCCTTAAAAACAAAACACACTTGATCCTTTTTGGTTTGTCAAGCAAGCGGGCTGTGTTCCCCAGTGATAGATGTGAATGAAGGCTTTACAGTCCCCCACAGTCTAGGAGTAAAGTGCCAGTATGTGGG

GGAGGGAGGGGCTACCTGTACACTGACTTAAGACCAGTTCAAATAAAAGTGCACACAATAGAGGCTTGACTGGTGTTGGTTTTTATTTCTGTGCTGCGCTGCTTGGCCGTTGGTAGCTGTTCTCATCTAGCCTTGCCAGCCTGTGTGGGTCAGCTATCTGCATGGGCTGCGTGCTGGTGCTGTCTGGTGCAGAGGTTGGATAAACCGTGATGATATTTCAGCAAGTGGGAGTTGGCTCTGATTCCATCCTGAGCTGCCATCAGTGTGTTCTGAAGGAAGCTGTTGGATGAGGGTGGGCTGAGTGCTGGGGGACAGCTGGGCTCAGTGGGACTGCAGCTGTGCT

HumanGCGGACTATGACTTAGTTGCGTTACACCCTTTCTTGACAAAACCTAACTTGCGCAGAAAACAAGATGAGATTGGCATGGCTTTATTTGTTTTTTTTGTTTTGTT

TTGGTTTTTTTTTTTTTTTTGGCTTGACTCAGGATTTAAAAACTGGAACGGTGAAGGTGACAGCAGTCGGTTGGAGCGAGCATCCCCCAAAGTTCACAATGTGGCCGAGGACTTTGATTGCATTGTTGTTTTTTTAATAGTCATTCCAAATATGAGATGCATTGTTACAGGAAGTCCCTTGCCATCCTAAAAGCCACCCCACTTCTCTCTAAGGAGAATGGCCCAGTCCTCTCCCAAGTCCACACAGGGGAGGTGATAGCATTGCTTTCGTGTAAATTATGTAATGCAAAATTTTTTTAATCTTCGCCTTAATACTTTTTTATTTTGTTTTATTTTGAATGATGAGCCTTCGTGCCCCCCCTTCCCCCTTTTTGTCCCCCAACTTGAGATGTATGAAGGCTTTTGGTCTCCCTGGGAGTGGGTGG

AGGCAGCCAGGGCTTACCTGTACACTGACTTGAGACCAGTTGAATAAAAGTGCACACCTTAAAAATGAGGCCAAGTGTGACTTTGTGGTGTGGCTGGGTTGGGGGCAGCAGAGGGTG

Parsimony score over 10 vertebrates: 0 1 2Slides from Martin Tompa

Current methods

One species Multiple species

•MEME (Bailey et al. 1994)

•AlignACE (Roth et al. 1998)

•BioProspector (Liu et al. 2001)

•MDscan (Liu et al. 2002)

•MITRA (Eskin et al. 2002)

•FootPrinter (Blanchette et al. 2003)

•PhyloCon (Wang et al. 2003)

•CompareProspector (Liu et al. 2004)

•OrthoMEME (Prakash et al. 2004)

•EMnEM (Moses et al. 2004 )

•PhyloGibbs (Siddharthan et al. 2004)

•PhyME (Sinha et al. 2004)

Limitations of current methods

Often find motifs in the pre-aligned sequences.



•CompareProspector (Liu et al. 2004)•OrthoMEME (Prakash et al. 2004)

•EMnEM (Moses et al. 2004)

•PhyloGibbs (Siddharthan et al. 2004)

•PhyME (Sinha et al. 2004)

Motif instances are not aligned

BCL2, GLi target, the transfac motif instance is GACCACCAA.

Alignment block 1 of 1 in window, 59138739 - 59138761, 23 bps Human gacttggtg-----gtcgctggggtccgMouse ggctggacgtgcctatagccagggcc--

Rat ggatgggtgcgcttattgccagggtcagRabbit ============================Dog NNNNNNNNNNNNNNNNNNNNNNNNNNNN

Elephant gggccgcgg-----gcgggcgagcggagOpossum ggctaggcg-------ggctaggttcgg

Chicken ============================tropicalis ============================

Tetraodon ============================

Limitations of current methodsPhylogenetic information is used ineffectively.


•CompareProspector (Liu et al. 2004)


•OrthoMEME (Prakash et al. 2004)•EMnEM (Moses et al. 2004)

•PhyloGibbs (Siddharthan et al. 2004)•PhyME (Sinha et al. 2004)

•How to find motif without alignment?

•How to explore phylogenetic tree properly?

Can we do better?

TGS: Tree Gibbs Sampler!!!

Outline

• Background• Current motif finding methods• Tree Gibbs Sampler (TGS)

– Overview– Results

• Motif verification– Overview– Results

How to find the ancestral motif

instances?

How did the motifs evolve?

Overview of TGS

Colored lines: regulatory regions of genesColored boxes: motif instances

How to find the ancestral motif instances?

CACGTGAACCACGTGACC CACGTGAAC

1 2 3 4 5 6 7 8 9A .036 .892 .036 .036 .036 .036 .892 .036 .036C .892 .036 .892 .036 .036 .036 .036 .75 .75 G .036 .036 .036 .892 .036 .892 .036 .036 .036T .036 .036 .036 .036 .892 .036 .036 .178 .178

Ancestral motif weight matrix:

A C G TA 0.8515 0.0278 0.0775 0.0432C 0.0464 0.8026 0.0344 0.1167G 0.1167 0.0350 0.8023 0.0460T 0.0429 0.0785 0.0264 0.8522

Background substitution matrix

A C G TA 0.9802 0.0066 0.0066 0.0066C 0.0120 0.9640 0.0120 0.0120G 0.0120 0.0120 0.9640 0.0120T 0.0066 0.0066 0.0066 0.9802

Motif substitution matrix

How did the motifs evolve?

Overview of Gibbs Sampler

Iteratively sample from conditional distribution when other parameters are fixed.

,i draw )|(~ ][ iii XXX

),,,( 21 nXXX )( X~In order to draw:

Implementation

ImplementationParameters

0

ip

Ancestral motif weight matrix at the root

Background distribution (multinomial)

Probability that a gene in the i-th species will contain the motif

w Motif width

iM 0 Background substitution matrix for the i-th branch

iM1 Motif substitution matrix for the i-th branch

ImplementationPrior distribution

0

ip

w

iM 0

iM1

Beta(1,1)

Poisson distribution

ImplementationInitialization

Parameters are sampled by using prior distributions;

Motif instances in current species are sampled from sequences directly for each current species;

Motif instances in ancestral species are randomly assigned with one of its immediate child motif instances.

ImplementationMotif instance updating

),,,,,,,,|Pr( 1211210)2(

1)1(

1)0(]1[

)0(1 MMwppAAAA

Updating motif instances in ancestral species

),,,,,|Pr( 1110)0(

1)1(

1 MwpSAA

Updating motif instances in current species

M11 M12

)0(1A

)1(1A )2(

1A

ImplementationUpdating motif instance in ancestral species

M11 M12

CCCGTGACC CACGTGAAC

Ancestral Motif Weight Matrix1 2 3 4 5 6 7 8 9

A .036 .892 .036 .036 .036 .036 .892 .036 .036C .892 .036 .892 .036 .036 .036 .036 .75 .75 G .036 .036 .036 .892 .036 .892 .036 .036 .036 T .036 .036 .036 .036 .892 .036 .036 .178 .178

M11 M12

C A

2th positionA: 0.932…C: 0.067G: 8.4e-6T: 2.5e-4

M11 M12

…CACACCACGTGAGCTT... …CACATCACGTGAACTT…

Updated ancestral motif instanceCACTTGAAC

Updating motif instances for current species

Implementation

Multiple Species??

CACGTGAAC CACGTGAAC CAGGTGATC

CACGTGAAC

CACGTGATC

CAGGTGATC

4

10

4

1

14

0 log})2|(|

)5.0(

)]5.0([)2(log{

)2()1||()1|(|log

!log

0

kkjk

kjkw

j

ww

nA

n

NANA

wew

Motif significance

Implementation

Outline




Data (I)• ChIP-chip for S. cerevisiae (Nature 431:99-104,2004).

204 transcription factors. 53 have:(1) experimentally verified sites (2) at least 5 gene sequences

• Testing species:S. cerevisiae, S.mikatae, S. Kudriavzevii, S.bayanus.

• Validation Less than two mismatches

Result (1): True Positive

CompareProspector 30/53

(4130)/53=

20.75%

5.66% improvement over MEME

PhyloCon 27/53

Tree Gibbs Sampler 41/53!

Result (1): False Positive

CompareProspector 18/48=37.5%

PhyloCon 6/32=18.75%

Tree Gibbs Sampler 8/49=16.33%

Result (1): Extreme Cases

Weak motifs are found in 2 out of 6 cases. MSN4, NRG1.

Motifs in small gene sets are found in 9 out of 10.

Data (II)

•63 Ribosomal protein genes for D. melanogaster.

•Test Species:

D. melanogaster, Anopheles gambiae.

•Literature.

Result (II)Distant species

250 million years

Summary

•Unaligned sequences •Weak motifs•Small gene sets•Distant species•Different motifs from the same origin

Li, X., Wong, WH. (2005)Sampling motifs on phylogenetic trees Proc Natl Acad Sci U S A. 102(27): 9481-6.

Outline




Motif Verification

Which are the real ones in the output of any software?

Independent evidence

MEME on each species, then hypothesis testing by conservation information

Find overrepresented motifs in each species separately. These motifs are called Marginally Significant Motifs (MSMs).

Using stringent criteria to find really co-regulated genes. Either with ChIP-chip data, or very confident microarray data, or microarray data plus other data (such as protein complex, pathway annotation).Find the orthologous genes in related species

Using low-stringency criteria to include the bona fide motifs.

The MSMs in the anchor species can be re-ranked according to the CSC motif conservation p-value.

Identify co-regulated genes in a species (the anchor species).

Report the significant groupings. The MSMs in the anchor species that show up in the significant groupings are the motifs to output.

Enumerate all MSM “groupings”. Each grouping is a collection of similar MSMs, where each MSM is from a different species. Use the CSC method proposed in this paper to test whether this grouping contains motifs that are more conserved than expected under the neutral evolution model.

Verifier strategy diagram.

Figure 2. The construction of the test statistic for testing whether a group of MSMs evolved from the same ancestral motif.

For every orthologous group of genes, g, do the following

Set x=0

Construct the ancestral motif weight matrix from all the ancestral instances on all the genes, except g.

Scan the orthologous sequences for gene g

x = x +1 if there are two or more genes, including the anchor gene, containing real motif instances of the ancestral motif.

Finished for all orthologous gene groups?No

Yes

Output x

)1()]0))((,0))(([Pr(

)]0))(([Pr()]0))(([Pr(1

)0))((,0))((,Pr(

)0))((,Pr()0))((,Pr(1

})1))((,{}1))((,Pr({)2,1(

1211

1211

21

21

21

n

nnii

ji

jii

XgfXgf

XgfXgf

XgfXgfi

XgfjXgfi

XgfjXgfiP

k

i m j

wwmj

k

i

Twwwwwwww

k

i

T

Av m

wmv

j

wjvi

imij

iiiiiiii

i

im

i

ij

i

tta

ttttAtttt

tMtMvttg

1

4

1

4

121

122222222

1

4

12

)2(4

11

)1(21

),,,(),,,(

]][)[(),(

43214321

(2).)()()Pr( xxn

xxnnxX

Overall Performance

• CompareProspector 24+1 correct prediction, 21 incorrect prediction

• PhyloCon 24+1 correct prediction, 10 incorrect prediction

• Verifier 29+1 correct prediction, 5 incorrect prediction

Reliable prediction of transcription factor binding sites by phylogenetic verification

Li, Zhong & WongPNAS vol 102, 16945-16950

Summary and outlook

• Blanchette et al’s method is an early work based on one group of orthologous sequences.

• We have developed two efficient methods for identifying transcription factor binding sites in the promoter regions.

• Future motif identification should target at motif modules. Hu J, Hu H, Li X. Nucleic Acids Res. 2008 Aug;36(13):4488-97. Epub 2008 Jul 7. and the entire non-coding regions.

Phylogenetic footprinting for motif identificationcs.ucf.edu/~xiaoman/fall/lecture 18...

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