Network integration and function prediction: Putting it all together

Network integration and function prediction:Putting it all together

Curtis Huttenhower

04-13-11Harvard School of Public HealthDepartment of Biostatistics

2

Outline

• Functional network integration– Bayes nets and LR– The human genome, tissues, and disease

• Network meta-analysis– Pathogens and MTb– Quantifying progress in yeast

• Networks to pathways– Functional mapping: networks of networks– Hierarchical integration– Pathway prediction

• Regulatory network integration– Network motifs

3

A computational definition offunctional genomics

Genomic data Prior knowledge

Data↓

Function

Function↓

Function

Gene↓

Gene

Gene↓

Function

4

A framework for functional genomics

HighSimilarity

LowSimilarity

HighCorrelation

LowCorrelation

G1G2

+

G4G9

+…

G3G6

-

G7G8

-…

G2G5

?

0.9 0.7 … 0.1 0.2 … 0.8

+ - … - - … +

0.8 0.5 … 0.05 0.1 … 0.6

HighCorrelation

LowCorrelation

Freq

uenc

y

Coloc.Not coloc.

Freq

uenc

y

SimilarDissim.

Freq

uenc

y

P(G2-G5|Data) = 0.85

100Ms gene pairs →

← 1

Ks

data

sets

+ =

5

MEFIT: A Framework forFunctional Genomics

Golub 1999

Butte 2000

Whitfield 2002

Hansen 1998

Functional Relationship

Biological Context

Functional areaTissueDisease…

6

Functional networkprediction and analysis

Global interaction network

Metabolism network Signaling network Gut community network

Currently includes data from30,000 human experimental results,

15,000 expression conditions +15,000 diverse others, analyzed for

200 biological functions and150 diseases

HEFalMp

7

HEFalMp: Predicting human gene function

HEFalMp

8

HEFalMp: Predicting humangenetic interactions

HEFalMp

9

HEFalMp: Analyzing human genomic data

HEFalMp

10

HEFalMp: Understanding human disease

HEFalMp

11

Validating Human Predictions

Autophagy

Luciferase(Negative control)

ATG5(Positive control) LAMP2 RAB11A

NotStarved

Starved(Autophagic)

Predicted novel autophagy proteins

5½ of 7 predictions currently confirmed

With Erin Haley, Hilary Coller

12

Outline





13

Meta-analysis for unsupervisedfunctional data integration

Evangelou 2007

Huttenhower 2006Hibbs 2007

11log

21'

'

''

z

eiey ,

ieeeiey ,,

i

ieiee yw ,*,̂

22,

*, ˆ

1

eieie s

w

Simple regression:All datasets are equally accurate

Random effects:Variation within and

among datasets and interactions

14

Meta-analysis for unsupervisedfunctional data integration

Evangelou 2007

Huttenhower 2006Hibbs 2007

11log

21'

'

''

z

+ =

15

Unsupervised data integration:TB virulence and ESX-1 secretionWith Sarah Fortune

Graphle http://huttenhower.sph.harvard.edu/graphle/

16

Unsupervised data integration:TB virulence and ESX-1 secretionWith Sarah Fortune

Graphle http://huttenhower.sph.harvard.edu/graphle/

X?

17

Predicting gene function

Cell cycle genes

Predicted relationships between genes

HighConfidence

LowConfidence

18

Predicting gene functionPredicted relationships

between genes

HighConfidence

LowConfidence

Cell cycle genes

19

Cell cycle genes

Predicting gene functionPredicted relationships

between genes

HighConfidence

LowConfidence

These edges provide a measure of how likely a gene is to

specifically participate in the process of

interest.

20

Comprehensive validation of computational predictions

Genomic data

Computational Predictions of Gene FunctionMEFITSPELL

Hibbs et al 2007bioPIXIEMyers et al 2005

Genes predicted to function in mitochondrion organization

and biogenesis

Laboratory ExperimentsPetite

frequencyGrowthcurves

Confocal microscopy

New known functions for correctly predicted genes

Retraining

With David Hess, Amy Caudy

Prior knowledge

21

Evaluating the performance of computational predictions

106Original GO Annotations

Genes involved in mitochondrion organization and biogenesis

135Under-annotations

82Novel Confirmations,

First Iteration

17Novel Confirmations,

Second Iteration

340 total: >3x previously known genes in ~5 person-months

22

Evaluating the performance of computational predictions

106Original GO Annotations

Genes involved in mitochondrion organization and biogenesis

95Under-annotations

40Confirmed

Under-annotations

80Novel Confirmations

First Iteration

17Novel Confirmations

Second Iteration

340 total: >3x previously known genes in ~5 person-months

Computational predictions from large collections of genomic data can be

accurate despite incomplete or misleading gold standards, and they

continue to improve as additional data are incorporated.

23

Outline





24

Functional mapping: mining integrated networks


HighConfidence

LowConfidence

The strength of these relationships indicates how

cohesive a process is.

Chemotaxis

25



HighConfidence

LowConfidence

Chemotaxis

26


Flagellar assembly

The strength of these relationships indicates how

associated two processes are.


HighConfidence

LowConfidence

Chemotaxis

27

Functional mapping:Associations among processes

EdgesAssociations between processes

VeryStrong

ModeratelyStrong

Hydrogen Transport

Electron Transport

Cellular Respiration

Protein ProcessingPeptide

Metabolism

Cell Redox HomeostasisAldehyde

Metabolism

Energy Reserve

Metabolism

Vacuolar Protein

CatabolismNegative Regulation

of Protein Metabolism

Organelle Fusion

Protein Depolymerization

Organelle Inheritance

28



VeryStrong

ModeratelyStrong

BordersData coverage of processes

WellCovered

SparselyCovered

Hydrogen Transport

Electron Transport



Metabolism


Metabolism

Energy Reserve

Metabolism

Vacuolar Protein



Organelle Fusion



29



VeryStrong

ModeratelyStrong

NodesCohesiveness of processes

BelowBaseline

Baseline(genomic

background)

VeryCohesive


WellCovered

SparselyCovered

Hydrogen Transport

Electron Transport



Metabolism


Metabolism

Energy Reserve

Metabolism

Vacuolar Protein



Organelle Fusion



30



VeryStrong

ModeratelyStrong

NodesCohesiveness of processes

BelowBaseline

Baseline(genomic

background)

VeryCohesive


WellCovered

SparselyCovered

• Gene expression

• Physical PPIs

• Genetic interactions

• Colocalization

• Sequence

• Protein domains

• Regulatory binding

sites

…

?

How do functional interactionsbecome pathways?

31

+ =

Functional genomic data

32

With Chris Park, Olga Troyanskaya

Simultaneous inference of physical, genetic, regulatory, and functional networks

Functional interactions

Regulatory interactions

Post-transcriptional regulation

Metabolic interactions

Phosphorylation Protein complexes

33

Learning a compendium of interaction networks

Train one SVM per interaction type

Resolve consistency using hierarchical Bayes net

34

Learning a compendium of interaction networks

AUC

0.5 1.0

Both presence/absence and directionality of

interactions are accurately inferred

35

Using network compendia to predictcomplete pathways

Additional 20 novel synthetic lethality predictions tested,

14 confirmed(>100x better than random)

Confirmed

Unconfirmed

With David Hess

36

Interactive aligned network viewer –http://function.princeton.edu/bioweaver

Graphle

http://function.princeton.edu/bioweaver

37

Outline





38

• Of only five regulators found, four have

generic cell cycle/proliferation targets

• Just five basic regulators for ~7,000 genes?

• These motifs only appear upstream of ~half

of the genes

Human Regulatory Networks

G0

I

III

IV

V

VIVII

IX

VIII

II

X

6,829genes

Serum re-stimulated (hrs)Serum starved (hrs)1

5< <50

2 4 8 24 96 1 2 4 8 24 48

Dev

elop

men

t

Dev

elop

men

t

Cho

lest

erol

Pro

tein

loca

lizat

ion

Cel

l cyc

le

RN

A pr

oces

sing

Met

abol

ism

FIRE: Elemento et al. 2007

Elk-1

Sp1

NF-Y

YY1

Quiescence: reversible exit from the cell cycle

39

COALESCE: Combinatorial Algorithm forExpression and Sequence-based Cluster Extraction

Gene Expression DNA Sequence

5’ UTR 3’ UTR

Upstream flank Downstream flank

Evolutionary Conservation

Nucleosome Positions

Identify conditions where genes

coexpress

Identify motifs enriched in

genes’ sequences

Create a new module

Select genes based on conditions

and motifs

Subtract mean from all data

Regulatory modules• Coregulated genes• Conditions where they’re

coregulated• Putative regulating motifs

Feature selection:Tests for differential expression/frequency

Bayesian integration

40

COALESCE: SelectingCoexpressed Conditions

• For each gene expression condition…– Compare distributions of values for

• Genes in the module versus• Genes not in the module

– If significantly different, include the condition

Preserving data structure:• If multiple conditions derive from the

samedataset, can be included/excluded as a

unit• For example, time course vs. deletion

collection• Test using multivariate z-test• Precalculate covariance matrix; still very

efficient

41

COALESCE: SelectingSignificant Motifs

• Coalesce looks for three kinds of motifs:– K-mers– Reverse complement pairs– Probabilistic Suffix Trees (PSTs)

• For every possible motif…– Compare distributions of values for

• Genes in the module versus• Genes not in the module

– If significantly different, include the motif

ACGACGT

ACGACAT | ATGTCGT

A

TC

G

T

TG

CA

• This can distinguish flanks from UTRs• Fast!• Efficient enough to search coding sequence

(e.g. exons/introns)

42

COALESCE: SelectingProbable Genes

• For each gene in the genome…For each significant condition… For each significant motif…

What’s the probability the gene came from the module’s distribution?

What’s the probability that it came from outside the module?

)()|()()|()()|()|(

MgPMgDPMgPMgDPMgPMgDPDMgP

Distributions of each feature in and out of the developing module are observed from the data.

Prior is used to stabilize module convergence; genes already in the module are more likely to stay there next iteration.

The probability of a gene being in the module given some data…

43

COALESCE: IntegratingAdditional Data Types

Nucleosome placement Evolutionary conservation

• Can be included as additional datasets and feature

selected just like expression conditions/motifs.

• Or can be used as a prior or weight on the values of

individual motifs.

N CG1 2.5 0.0

G2 0.6 0.5

G3 1.2 0.9

… … …

TCCGGTAGAACTACTGGTATTGTTTTGGATTCCGGTGATG

44

COALESCE Results:S. cerevisiae Modules

~2,200 conditions

~6,000 genes

The haystack

A needle100 genes

80 conditions

45


54 genes, 144 conditionsConjugation

33 genes, 434 conditionsBudding

112 genes, 82 conditionsMitosis and DNA replication

Swi5

Stb1/Swi6Ste12

46


50 genes, 775 conditionsIron transport

11 genes, 844 conditionsPhosphate transport

126 genes, 660 conditionsGlycolysis, iron and phosphate transport, amino acid metabolism…

Pho4

Helix-Loop-HelixTye7/Cbf1/Pho4

Aft1/2

47


72 genes, 319 conditionsMitochondrial translation Puf3

…plus more ribosome clusters

than you can shake a stick at!

48

COALESCE Results:Yeast TF/Target Accuracy

Bas1p Hap4p Met32p Cup2p Met31p Zap1p Upc2p Mbp1p Hsf1p Gln3p Hap3p Gcn4p Uga3p Gis1p Hap5p

-0.3

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

1.3

COALESCEcMonkeyFIREWeeder

Z-Sc

ore

49

COALESCE Results:TF/Targets Influenced by Supporting Data

Sfl1p Gcr1p Uga3p Mot3p Sum1p Cst6p Mig3p

-0.5

0

0.5

1

1.5

2

2.5

3

COALESCECOALESCE, conservationCOALESCE, nucleosomesCOALESCE, cons. + nuc.

Z-Sc

ore

Improved by any addl. data, mainly conservation

Decreased by addl. data Improved by conservation

Improved only by both

50

COALESCE Results:Yeast Clustering Accuracy

• ~2,200 yeast conditions– Recapitulation of known biology from Gene Ontology

51

COALESCE Results:Yeast Clustering Accuracy

• ~2,200 yeast conditions– Recapitulation of known biology from Gene Ontology

ASCL1 in 5’ flank, unch. sequences underenriched in 3’ UTR

M. musculus: Up in callosal and motor neurons

C. elegans: Up in larvae, down in adults

GATA in 5’ flank, miR-788 seed in 3’ UTR

AAGGGGC (zf?) and enriched in 5’ flank

H. sapiens: Up in normal muscle, down in diabetic

52

COALESCE: Coregulated Quiescence Modules

• Predicts regulatory modules from genomic data:– Coregulated genes– Conditions under which coregulation occurs– Putative regulatory motifs

• 5 quiescence-related microarray datasets,60 conditions– Quiescence program (Coller et al. 2006)– Adenoviral infection (Miller et

al. 2007)– let-7 response

(Legesse-Miller et al. unpub.)– Contact inhibition

(Scarino et al. unpub.)– Serum withdrawal (Legesse-

Miller et al. unpub.)

53

COALESCE: Coregulated Quiescence Modules

Down during quiescence entry, up during quiescence exit,down with adenoviral infection

Specific predicted uncharacterized reverse complement motif

Up during quiescence entry, down during quiescence exit

Many known related (proliferation) motifs:Pax4, Staf, NFKB1, Gfi, ESR1, Runx1, Su(H)

Down during quiescence entry,enriched for transport/trafficking

miR-297 motif predicted in 3’ UTR (CACATAC)

Down with let-7 exposure

let-7 motifs predicted in 3’ UTR (UACCUC)

Network Motifs

54

Coherent feed-forward

filter

Incoherent feed-forward

pulse

Bi-fan

Positiveauto-regulation

delay

WGD and evolvability

Negativeauto-regulation

speed + stability

Feedback

memory

March 1, 2010 55

From Milo, et al., Science, 2002

56

Outline





1:1 Lewis Carroll Map“… And then came the grandest idea of

all! We actually made a map of the country, on the scale of a mile to the mile!"

"Have you used it much?" I enquired.

"It has never been spread out, yet," said Mein Herr: "the farmers objected: they said it would cover the whole country, and shut out the sunlight! So we now use the country itself, as its own map, and I assure you it does nearly as well.

Sylvie and Bruno Concluded by Lewis Carroll, 1893.March 1, 2010 57

Documents

Network integration and function prediction: Putting it all together