New functional genomics technologies and their analysis

Wolfgang HuberEuropean Molecular Biology Laboratory

European Bioinformatics Institute

OverviewWhole genome arrays – from levels of known

transcripts to the complete architecture of the transcriptome

ChIP arrays – protein-DNA interactions

Functional assays – from correlation to causality

Tiling microarrays• Oligonucleotide

microarrays• probes cover not only

known genes but whole genome

• length, distance and overlap between probes varies

• hybridize cDNA from complete poly-A-RNA in cell

Tiling arrays: possibilities

Unbiased view on transcription since no focus on or restriction to know genes

refine annotated genesconfirm predicted genesdiscover novel genes

Recent tiling array studies

Drosophila melanogaster

Poly-A-RNA from 6 developmental stages

Synthesized 36mersStolc et al.

Arabidopsis thalianaPoly-A-RNA from 4-6 samples

Synthesized 25mersYamada et al.

E. coliStationary and growth phase total RNA

Synthesized 25mers

Selinger & Church

H. sapiens (Chr. 22)Placental poly-A-RNASpotted PCR productsRinn et al.

H. sapiens (Chr. 21-22)

ds-cDNA from poly-A-RNA of 11 cell lines

Synthesized 25mers

Kapranov et al.

Species, chromosomesSamplesMicroarray typeStudy

Genechip S. cerevisiae Tiling Array

4 bp tiling path over complete genome(12 Mio basepairs, 16 chromosomes)

Sense and Antisense strands6.5·106 oligonucleotides5 mm feature size

Chips manufactured by AffymetrixApplication + analysis by L. Steinmetz (EMBL/Stanford Genome Center) and W. Huber (EMBL/EBI)

3,039,046 perfect match probes7,359 splice junction probes127,813 YJM789 polymorphism probes16,271 Tag3 barcode probes

The first complete genome on one array

Genomic DNA

Poly-A RNA (double enriched) from exponential growth in rich media

Total RNA from exponential growth in rich media

3 replicates each

Samples

RNA Hybridization

Probe specific affinity normalization

beforebefore

afterafter

Probe specific affinity normali-zation

2log ii

i

yqs

=

2( )glog i i

ii

y b sqs−

=

2log iy

2log is

Two obvious options:

Smoothing (e.g. running median) and thresholding: simple, but estimates of change points will be biased and depend on expression level!

Hidden Markov Model (HMM): but our “states” come from a continuum! Fiddly.

Our solution:

Fit a piecewise constant function

Segmentation

change point

Segmentation

( )1 2

11 1

( , , )s

s

i tS J

S ij sjs j i t

G t t y y+<

= = >=

= −∑∑∑…

t1,…, tS: change points

J: number of replicate arrays

Minimize

Segmentation

Naïve optimization has complexity ns, where n≈105

and s≈103.

Fortunately, there is a dynamic programmingalgorithm with complexity ≈n2:

Theory: F. Picard, S.Robin, M. Lavielle, C. Vaisse, G. Celeux, JJ Daudin, INRIA (2004)

Software: W. Huber, package tilingArray, www.bioconductor.org (2005)

Splicing

Mapping of UTRs:

BioconductorPackage tilingArray contains

Picard’s segmentation algorithm

the along-chromosome plots

To do: automated model choicediagnostics for model fitbetter user-interface and documentation of along-chromosome objects and their plots

Conclusionso Conventional microarrays: measure transcript levels

o High resolution tiling arrays: also measure transcript structure

introns, exons,partial degradationnovel transcriptsannotation errors

o DNA normalization: increase signal/noise by 1.5-2

o Accurate and simple segmentation algorithm

o Existing bioconductor data structures and packages (e.g. affy, vsn, geneplotter) were instrumental

o Current packages and data structures are mostly gene-centric, we still need more infrastructure for data that consists of features along genomic coordinates

Acknowledgements

Oleg Sklyar

EMBL-GE & Stanford:Lior DavidMarina Granovskaia

Jörn Tödling Raeka Aiyar

Lars Steinmetz

Robert GentlemanRafael IrizarryVince CareyBen Bolstad

ChIP-on-chipChromatin ImmunoPrecipitation

What? Systematic detection of protein-DNA interactionsin vivoin vitro

What for? Protein-DNA interactions play a role intranscriptionDNA replicationDNA recombinationDNA repair

Why? Efforts to computationally extrapolate the binding sites of a protein with DNA (“motifs”) that were observed with traditional methods, to obtain all sites active in vivo, have had rather limited success.

Reference: Minireview by MJ Buck, JD Lieb, Genomics 83 (2004) 349-360

ApplicationsLee TI, Rinaldi NJ, Robert F, ...Young RA. Transcriptional regulatory networks in Saccharomyces cerevisiae.Science, 2002, 298(5594):799-804.In vivo binding sites of 106 yeast proteins

Harbison CT, Gordon DB, Lee TI,..., Young RA. Transcriptional regulatory code of a eukaryotic genome. Nature, 2004, 431(7004):99-104. ... some with multiple conditions

Mukherjee S, Berger MF, Jona G, ..., Bulyk ML. Rapid analysis of the DNA-binding specificities of transcription factors with DNA microarrays. Nat Genet. 2004, 36(12):1331-9. In vitro binding sites of 3 yeast TFs Abf1, Rap1, Mig1

Lee et al. (2002)

regulator := a transcription factor (TF) or a ligand of a TFtag: c-myc epitope

106 microarrayssamples: enriched (tagged-regulator + DNA-promoter)probes: cDNA of all promoter regionsspot intensity ~ affinity of a promotor to a certain regulator

Transcriptional regulatory networksbipartite graph

1

1

1

1

1

1

1

106 regulators (TFs)

6270

pro

mot

er re

gion

s

regulators

promoters

Experimental designIdeal control

Cells lacking the antibody epitope, but otherwise identical

Mock control

ChIP protocol repeated exactly, but the antibody is omitted (no DNA should be pulled down)

Reference (two colors)

"Input" = the input chromatin (before ChIP)

You can look at several quantities:

Sample / InputSample / MockSample / Input | (Mock small)

Pre-processingMostly similar to the corresponding transcription-microarray technology (e.g. spotted two-color)

Also need some kind of normalization assumption, e.g. known set of negative control DNA sequences, or "most sites are not binding".

Distribution of (g)log-ratios is usually skewed (a probe will give positive signal above background if its target is pulled down, but there is no mechanism for reducing its signal) -estimators in the normalization should take this into account, need to use robust methods.

Functional assays

(cell-based assays,phenotype screens,

RNAi screens)

Candidate gene sets from

microarraystudies:

dozens…hundreds

Capacity of detailed in-vivo functional

studies: one…few

How to close the gap?

sample

cros

s-va

lidat

ed p

roba

bilit

ies

0 20 40 60

0.0

0.2

0.4

0.6

0.8

1.0

ccRCC chRCC pRCC

Drowning by numbers

How to separate a flood of ‘significant’ secondary effects from causally relevant ones?

VHL: tumor suppressor with “gatekeeper” role in kidney cancers

Boer, Huber, et al. Genome Res. 2001: kidney tumor/normal profiling study

Drowning by numbers

Boer, Huber, et al. Genome Res. 2001

From association to intervention

mRNA profiling studies: association of genes with diseases

gene 1disease

gene 2

or or…

the dilemma

oror ?the next step: directed intervention

Is differential expression a good predictor for ’signaling’ function?

RNAiphenotypes

Differentially regulatedgenes

~ 70 280 genes

RIP/IMDpathways

RIP

Tak1

IKK

Rel

R

Targets

Michael Boutros

Most pathway targets are not required for pathway function

RNAiphenotypes

Differentially regulatedgenes

~ 70 280 genes

3

RIP/IMDpathways

RIP

Tak1

IKK

Rel

R

Targets

Michael Boutros

Signaling pathways

Drosophila antibacterial signalling The Drosophila Toll/antifungal

signaling pathway

Pictures from N. Silverman

UMass

Bufferingin yeast, ~73% of gene deletions are "non-essential"

(Glaever et al. Nature 418 (2002))

in Drosophila, ~95% (Boutros et al. Science 303 (2004))

association studies for most human genetic diseases have failed to produce single loci with high penetrance

evolutionary pressure for robustness

What are the implications for functional studies?Need to:

use combinatorial perturbationsobserve multiple phenotypes with high sensitivity

understand gene-gene and gene-phenotype interactions in terms of graph-like models ("networks")

RNAi+ genome wideo specificity- efficiency / monitoring?

Transfection (expression)+ 100% specific+ monitoring- library size, €€€

Small compounds…

Interference/Perturbation tools

Probe the role of a protein by increasing its abundance and measuring the effect in terms of changes in a cellular process or pathway activity.

Transfect cells with a vector that contains encoding DNA sequence and a short sequence for Green Fluorescent Protein (GFP)

The protein’s overabundance in each individual cell can be monitored through its fluorescence.

Avoid artifacts caused by non-specificor cross-reactive effects: work in small-perturbation regime

Transfection assays

He and Hannon, 2004

Initiation

Execution

RNAi

T7

Precursor dsRNA

siRNAs

Degradation of target message

C. elegans Drosophila Mammals

Injection and soaking

Feeding bacteria

Worms Cell-culture

Bathing Transfection

> 200bp> 200bp 21bp

Cell-culture

dsRNA dsRNA siRNAE. coli

RNAi experiments in different organisms

Dicer

Slide by M. Boutros

• Identification by Elbashir et al., 2001– Size is critical– siRNA (21-22nt) mediate mammalian RNAi– Introducing siRNA instead of dsRNA prevents non-

specific effects

• Application via transient transfection– No persistent or propagative effect as in C. elegans– Can be chemically synthesized or generated by in

vitro transcription

siRNAs

Slide by M. Boutros

Any cellular process (in principle) can be probed. E.g.

- (de-)activation of a certain pathway- differentiation,- changes in the cell cycle dynamics- morphological changes- activation of apoptosis

Similarly, for organisms (e.g. fly embryos, worms)

Effects are sometimes also called phenotypes

Phenotypes can be registered at various levels of detail

- yes/no alternative- single quantitative variable- image- time series

The effect - it all depends on the assay

Plate reader96 or 384 well, 1…4 measurements per well

FACSca. 2000 x 4…8 measurements per well

Automated Microscopypractically unlimited.Here: 30 x 1280 x 1024 x 3

Monitoring tools

Plate reader96 or 384 well, 1…4 measurements per well

FACS4…8 measurements per cell, thousands of cellsper well

Automated Microscopyunlimited

Monitoring tools

Normalization

Boutros, Kiger, et al. Science 2004

Viability screen each dot corresponds to one gene, shown is cell viability after knock-down.

Normalization

Boutros, Kiger, et al. Science 2004

Normalization

Boutros, Kiger, et al. Science 2004

(linear) regression models for normalization

kcdi p w k c d kd kc kciy P W G C D F X ε= + + + + + + +

y: observed signalP: plate effectW: well effect (e.g. spatial)G: probe effectC: condition effectD: overall dye effectF: probe-specific dye effectε: noise

X: gene-specific condition effect

k: probe / genec: treatment, conditiond: dyei: replicatep=p(kci): plate indexw=w(kc): plate index

Similar problems/solutions as for microarrays: Small no. of replicates - moderation of the variancesLarge dynamic range / heteroskedasticity - scale transformations

Example:

ANOVA to study synthetic phenotypes

cr c r cr kciy C R S ε= + + +

y: observed signalC: drug treatment (0/1, or dose) R: RNAi (0/1, … efficiency)S: synergistic / antagonistic effectε: noise

Open questionsOn what scale to measure y, C, R (normalization)?

e.g. log(a+b) ≠ log(a)+log(b)

assays to challenge the cell-cycle and beyond

Other assays: - Protein

localisation- Protein

interaction

HT functional assays

cDNA library(>100 clones)

expression clonesBrdU

incorporation

GFP-ORF- protein

DAPI: identification

CFP: expression

BrdU: proliferation

Data analysis

effect on proliferation

automatedmicroscope

Liquid handlingTransfection & antibody incubation

Transfectionreagents

Expressionplasmids

Chamberslides

Transfectionmixing plate

Buffers &antibodies

Sterilehousing

Christian Schmitt

High Content Screening Microscope

- Cube -

Urban Liebel EMBL

Automated image analysis

DAPI YFP Cy5 Anti-BrdU

Urban Liebel EMBL

...621 258

2101 1441732 4011183 120493 21966 297

232 421182 120286 332

...

YFPchannel

Cy5channel

raw data

expression clones

plates

propertiesx 19

cells2,263,287

Images (ca. 350 GB)3 x 42,294

non-empty wells2,437

289

27

Local Regression analysis

Signal intensity (cyclin A)

… focus on small perturbations and weak phenotypes!

local slope

σ= 0

0'

ˆ '( )ˆ ( )

m

m xzx

Arlt, Huber, et al. submitted (2005)

Signal intensity (PP2A)

Signal intensity (CFP)

Act

ivat

edC

aspa

se3

mock

Expression YFP-CDK 2

Expression YFP-CIDE

Apoptosis Assay

M. Sauermann, F. Hahne, D. Arlt, DKFZ

pERK1/2

Expression YFP-ERK1

Expression YFP-DSPP

Expression YFP-MEK

MAPK Assay

M. Majety, F. Hahne, D. Arlt, DKFZ

YFP

PP2-YFPCyclinA-YFPYFP

Within Well Analysis - Plate Plot

CFP

Between Well AnalysisCFP/YFP PP2Cyclin A

ORF 65 ORF 12

Prada packageImport of flow cytometry data (FCS3.0 files)

"cytoSet" class - analogon of exprSet for flow cytometry

-> use generic R plotting and statistical modeling for flow cytometry data

Specialized functions (e.g. platePlot, fitNorm2)

AcknowledgementsDKFZ Heidelberg -Molecular Genome AnalysisDorit ArltAnnemarie PoustkaStefan WiemannHolger SültmannAndreas BunessMarkus RuschhauptFlorian Hahne… & many others

DKFZ - SignalingMichael Boutros

DFCI / HarvardRobert Gentleman

The R project

Leiden UMCJudith Boer

EMBLRainer PepperkokPhilippe BastiaensUrban LiebelHolger Erfle

MPI Mol. Genetics, BerlinAnja von HeydebreckMartin Vingron

Uni HeidelbergGünther Sawitzki

Significance, effect size, and separation

not significant

0.0

0.5

1.0

1.5

2.0

significant, but small effect and weak separation

010

2030

40

significant, good separation but small effect

020

4060

8010

0

significant, large effect but weak separation

05

1015

2025

significant, large effect and good separation

020

4060

8010

0