Motif instance identification using comparative genomics

Pouya KheradpourJoint work with: Alexander Stark, Sushmita Roy and Manolis Kellis

Background and goal

TF1 microRNA1TF2

• Regulators bind to short (5 to 20bp) sequence specific patterns (motifs)

• Genes are largely controlled through the binding of regulators

– Transcription factors (TFs) are proteins that bind near the transcription start site (TSS) of genes and either activate or repress transcription

– miRNAs bind to the 3’ un-translated region (UTR) of mRNAs to repress translation

• The goal of our work is to identify these binding sites (motif instances)

Motivation

Network: Davidson and Erwin, Science (2006)Mouse: Pennacchio, et al., Nature (2006) Fly: Tomancak, et al., Genome Biology (2002)

• In all animals, genes are both temporally and spatially regulated to produce complex expression patterns

• Identifying the targets of regulators is vital to understanding this expression

• Conservation allows for identifying targets that are evolutionarily meaningful

Previous work

• Single genome approaches– Generally use positional clustering of motif matches to increase signal (e.g.

Berman, et al. 2002; Schroeder, et al. 2004; Philippakis, et al. 2006)• A single 5mer match occurs on average 3 million times in mammalian genome

– Requires set of specific factors that act together– Miss instances of motifs that may occur alone

• Multi-genome approaches (phylogentic footprinting)– Blanchette and Tompa 2002 use an alignment free phylogenetic approach

to find k-mers that are unusually well conserved– Moses, et al. 2004 use a strict phylogenetic model to find regions that

evolve according to the motif and not the background– Etwiller, et al. 2005 use both nearby species and distant species (fish) to

identify motif instances– Lewis, et al. 2005 finds putative microRNA binding sites requiring full

conservation in five species

Approach outline

1. Produce a raw conservation score for each motif match (branch length score or BLS)

2. For each motif and region, produce a mapping from BLS to confidence

Advantages• Now we have many, complete, closely related genomes

– Gives enough power to identify binding sites (Eddy, 2005)– Do not have to worry about dramatic divergence

• Account for non-motif conservation using globally derived statistics

• Robust against errors and evolutionary turnover• Computationally feasible to run genome wide for all available

motifs

Large phylogeny challenges in instance identification

• Sequencing / assembly / alignment artifacts– Low coverage sequencing, mis-alignments

• Evolutionary variation– Individual binding sites can move / mutate– Some instances found only in subset of species

Don’t require perfect conservation: Branch length score

Don’t require exact alignment: Search within a window

Motif instance

movement

missing sequence

Computing Branch Length Score (BLS)

CTCF

BLS = 2.23sps (78%)Does not over count redundant branch lengthAllows for:1. Mutations permitted by motif degeneracy2. Misalignment/movement of motifs within

window (up to hundreds of nucleotides)3. Missing motif matches in dense species tree

mutations

missing short branches

movement

Branch Length Score Confidence

1. Evaluate non-motif probability of a given score• Sequence could also be conserved due to overlap with

un-annotated element (e.g. non-coding RNA)

2. Account for differences in motif composition and length

• For example, short motif more likely to be conserved by chance

Control motifs

• Control motifs are the basis of our estimation of the background level of conservation and for evaluating enrichment

• Each motif has its own set of controls• They are chosen to:

– Have the same composition as the original motif– Match the target regions (e.g. promoters) with

approximately the same frequency (+/- 20%)– Not too similar to each other (to preserve diversity)– Not be similar to known motifs (including the one being

shuffled)• Background level is estimated separately in each region

type (e.g. Promoters or 3’ UTRs)

Branch Length Score Confidence

1. Use motif-specific shuffled control motifs determine the expected number of instances at each BLS by chance alone or due to non-motif conservation

2. Compute Confidence Score as fraction of instances over noise at a given BLS(=1 – false discovery rate)

3. Select movement window that leads to the most instances at each confidence

Confidence selects for functional instances

Transcription factor motifs

Promoter

5’UTR

CDS

Intron

3’UTR

MicroRNA motifs

Promoter

5’UTR

CDS

Intron

3’UTR

1. Confidence selects for transcription factor motif instances in promoters and miRNA motifs in 3’ UTRs

Confidence selects for functional instances

1. Confidence selects for transcription factor motif instances in promoters and miRNA motifs in 3’ UTRs

2. miRNA motifs are found preferentially on the plus strand, whereas no such preference is found for TF motifs

Strand Bias

Experimental identification of binding sites

• Chromatin immunoprecipitation (ChIP) combined with either sequencing (seq) or with microarrays (chip) are experimental procedures that are used to identify binding sites– Not all binding is functional, can have high false positive rate– Only binding that is active in the surveyed conditions is found

ChIP-seq

Maridis 2007

Intersection with CTCF ChIP-Seq regions

• Conserved CTCF motif instances highly enriched in ChIP-Seq sites

• High enrichment does not require low sensitivity

• Many motif instances are verified

ChIP data from Barski, et al., Cell (2007)

≥ 50% of regions with a motif50% motifs verified

50% confidence

CTCF

Enrichment found for other factors in mammals and flies B

arsk

i, et

al.,

Cel

l (20

07)

Odo

m, e

t al.,

Nat

ure

Gene

tics (

2007

)

Lim

, et a

l., M

olec

ular

Cel

l (20

07)

Wei

, et a

l., C

ell (

2006

)

Zelle

r, et

al.,

PN

AS (2

006)

Lin,

et a

l., P

LoS

Gene

tics (

2007

)

Robe

rtso

n, e

t al.,

Nat

ure

Met

hods

(200

6)

Mammals

Abra

ms a

nd A

ndre

w, D

evel

(200

5) (N

ot C

hIP)

Sand

man

n, e

t al.,

Dev

el C

ell (

2006

)Ze

itlin

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l., G

enes

& D

evel

(200

7)Sa

ndm

ann,

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l., G

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(200

7)

Flies

Enrichment increases in conserved bound regions

Human: Barski, et al., Cell (2007)Mouse: Bernstein, unpublished

1. ChIP bound regions may not be conserved (Odom, et al. 2007)2. For CTCF we also have binding data in mouse 3. Enrichment in intersection is dramatically higher

Enrichment increases in conserved bound regionsHu

man

: Bar

ski,

et a

l., C

ell (

2007

)M

ouse

: Ber

nste

in, u

npub

lishe

d

Odo

m, e

t al.,

Nat

ure

Gene

tics (

2007

)

1. ChIP bound regions may not be conserved (Odom, et al. 2007)2. For CTCF we also have binding data in mouse 3. Enrichment in intersection is dramatically higher4. Trend persists for other factors where we have multi-species

ChIP data

1. Motifs at 60% confidence and ChIP have similar enrichments (depletion for the repressor Snail) in the functional promoters

2. Enrichments persist even when you look at non-overlapping subsets3. Intersection of two has strongest signal4. Evolutionary and experimental evidence is complementary

• ChIP includes species specific regions and differentiates tissues• Conserved instances include binding sites not seen in tissues surveyed

ChIP data from: Zeitlinger, et al., G&D (2007); Sandmann, et al,. G&D (2007); Sandmann, et al., Dev Cell (2006)

Enrichment of instances in fly muscle genes

Fly regulatory network at 60% confidence

TFs: 67 of 83 (81%) 46k instances

miRNAs: 49 of 67 (86%) 4k instances

• Several connections confirmed by literature (either directly or indirectly)Global view of instances allows us to make network level observations:• TFs were more targeted by TFs (P < 10-20) and by miRNAs (P < 5 x 10-5)• TF in-degree associated with miRNA in-degree (high-high: P < 10-4; low-low P < 10-6)

Contributions

• A general methodology for regulatory motif instance identification using many, closely related genomes

– Robust against errors from sequencing, assembly and alignment

– Allows limited functional turnover and motif movement– Provides statistical measurement of confidence for each

instance, correcting for length, composition and overlap with other functional elements

• Validation and comparison to experimental data– High enrichment of binding sites in ChIP regions for a

variety of factors– Functional enrichments suggest comparable ability to

identify functional instances as ChIP

Future directions

• Our predicted network was static, but real regulatory networks are dynamic– They change throughout development and in different

conditions– They can vary greatly in different species

• We want to expand this work to learn about this network dynamics– ChIP data is becoming increasingly available in a variety

of conditions – we can use this to learn what causes changes in binding

– Multi-species data is also becoming more available• Can match motif binding to cross-species expression changes

– We can train on this data to find motifs that act together or compensate for each other

Acknowledgments• Alexander Stark• Sushmita Roy• Manolis Kellis

Mouse CTCF ChIP-Seq• Tarjei Mikkelsen• Brad Bernstein

Funding• William C.H. Chao Fellowship• NSF Graduate Research Fellowship

MIT CSAIL• Matt Rasmussen• Mike Lin• Issao Fujiwara• Rogerio Candeias

Broad Institute• Or Zuk• Michele Clamp• Manuel Garber• Mitch Guttman• Eric Lander

The End

Implementation details

• Table lookup on the next 8 bases of the genome are used to find potential matches to the target genome – Results in an order-of-magnitude increase in speed over

scanning through all motifs• In a first run, 100 shuffles of each motif are evaluated

and up to 10 that fulfill the requirements are selected• All motifs and their selected shuffles are matched to

the target genome and their BLS scores are computed• The matches are evaluated at each branch length

cutoff and a mapping is produced for each motif from branch length score to confidence

• All code is designed to run on BROAD cluster (often with parallelization) and is written in C

Performance on mammalian TRANSFAC motifs

• Most motifs have confident instances into 90% confidence with 18 mammals• Substantial increase in the number of instances compared to only human, mouse rat and

dog.

2.5x increase

3.5x

6.5x

The promise of many genomes

• Eddy showed that with many genomes, resolving binding sites using conservation is possible

• The goal of our work is to make this practical– Integrate evidence from multiple informant species– Determine which of the thousands of motif matches are

functional using conservation

Slides on motif discovery

Related problem: computational motif discovery

• Discovery of the regulatory motifs (as opposed to their binding sites) has also been an active area of research for several years

• Single species work has generally required sequences thought to have similar regulation (for comparison, see Tompa, et al. 2005; Elemento, et al. 2007)

– Looked for patterns that were enriched in target sequences• Use of conservation has been generally successful in re-

identifying known binding affinities for TFs and miRNAs (e.g. Kellis, et al. 2003; Xie, et al. 2005; Etwiller, et al. 2005)

– Requires fewer species (i.e. less branch length) than instance identification because signal can be integrated over thousands of instances found genome-wide

Motif discovery pipeline

1.Enumerate motif seeds

• Six non-degenerate characters with variable size gap in the middle

2.Score seed motifs• Use a conservation ratio corrected for composition

and small counts to rank seed motifs3.Expand seed motifs

• Use expanded nucleotide IUPAC alphabet to fill unspecified bases around seed using hill climbing

4.Cluster to remove redundancy• Using sequence similarity

GT C A GTgap

GT C A GTR RY gapS W

Consensus MCS Matches to known Expression enrichment Promoters Enhancers

1 CTAATTAAA 65.6 engrailed (en) 25.4 2

2 TTKCAATTAA 57.3 reversed-polarity (repo) 5.8 4.2

3 WATTRATTK 54.9 araucan (ara) 11.7 2.6

4 AAATTTATGCK 54.4 paired (prd) 4.5 16.5

5 GCAATAAA 51 ventral veins lacking (vvl) 13.2 0.3

6 DTAATTTRYNR 46.7 Ultrabithorax (Ubx) 16 3.3

7 TGATTAAT 45.7 apterous (ap) 7.1 1.7

8 YMATTAAAA 43.1 abdominal A (abd-A) 7 2.2

9 AAACNNGTT 41.2 20.1 4.3

10 RATTKAATT 40 3.9 0.7

11 GCACGTGT 39.5 fushi tarazu (ftz) 17.9

12 AACASCTG 38.8 broad-Z3 (br-Z3) 10.7

13 AATTRMATTA 38.2 19.5 1.2

14 TATGCWAAT 37.8 5.8 2

15 TAATTATG 37.5 Antennapedia (Antp) 14.1 5.4

16 CATNAATCA 36.9 1.8 1.7

17 TTACATAA 36.9 5.4

18 RTAAATCAA 36.3 3.2 2.8

19 AATKNMATTT 36 3.6 0

20 ATGTCAAHT 35.6 2.4 4.6

21 ATAAAYAAA 35.5 57.2 -0.5

22 YYAATCAAA 33.9 5.3 0.6

23 WTTTTATG 33.8 Abdominal B (Abd-B) 6.3 6

24 TTTYMATTA 33.6 extradenticle (exd) 6.7 1.7

25 TGTMAATA 33.2 8.9 1.6

26 TAAYGAG 33.1 4.7 2.7

27 AAAKTGA 32.9 7.6 0.3

28 AAANNAAA 32.9 449.7 0.8

29 RTAAWTTAT 32.9 gooseberry-neuro (gsb-n) 11 0.8

30 TTATTTAYR 32.9 Deformed (Dfd) 30.7

Top 30 discovered fly motifs

1. Many of the top discovered motifs match known motifs2. Motifs are associated with genes that are preferentially expressed in tissues

Discovered motifs have functional enrichments

1. Most motifs avoided in ubiquitously expressed genes 2. Functional clusters emerge

Tissues

Mot

ifs Enrichment or depletion of a motif in the promoters

of genes expressed in a tissue