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I. Tissue dependent alternative exon usage II. Systematic mapping of genetic interactions by combinatorial RNAi and high-throughput microscopy. EMBL. Wolfgang Huber. Progress in science is driven by technology.

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I. Tissue dependent alternative exon usageII. Systematic mapping of genetic interactions by combinatorial RNAi and high-throughput microscopy

EMBL

Wolfgang Huber


Progress in science is driven by technology
Progress in science is driven by technology

We work on the methods in statistical computing, integrative bioinformatics and mathematical modelling to turn these data into biology.

Sequencing - DNA-Seq, RNA-Seq, ChiP-Seq, HiCMicroscopy & remote sensing- molecular interactions and life-cycles in single, live cells

Large scale perturbation libraries - RNAi, drugs


Research areas

  • Gene expression

  • Statistical method development: differential analysis

  • Alternative exon usage

  • 3D structure of DNA (HiC & Co.)

  • Single-cell transcriptomics and noise

  • collaborations with L. Steinmetz, P. Bertone, T. Hiiragi

  • Cancer Genomics & Precision Oncology

  • Somatic mutation detection (incl subclonal)

  • Phylogeny inference

  • collaborations with C.v.Kalle, H. Glimm, T. Zenz (NCT)

  • Genetic Interactions & phenotypes

  • Large-scale combinatorial RNAi & automated microscopy phenotyping

  • Cancer mutations & drugs

  • collaborations with M.Boutros (DKFZ) & T.Zenz and others

  • Basics of statistics

  • Tools & infrastructure for software ‘publication’

  • Teaching

  • collaborations M.Morgan (FHCRC), R.Gentleman (Genentech)


Count data in hts
Count data in HTS

  • Gene GliNS1 G144 G166 G179 CB541 CB660

  • 13CDNA73 4 0 6 1 0 5

  • A2BP1 19 18 20 7 1 8

  • A2M 2724 2209 13 49 193 548

  • A4GALT 0 0 48 0 0 0

  • AAAS 57 29 224 49 202 92

  • AACS 1904 1294 5073 5365 3737 3511

  • AADACL1 3 13 239 683 158 40

  • [...]

  • RNA-Seq

  • ChIP-Seq

  • HiC

  • Barcode-Seq

  • Peptides in mass spec

  • ...

Simon Anders


two biologicalreplicates

treatment vs control


Modelling variance overcoming the small n large p problem
Modelling Variance overcoming the small n, large p problem

  • To assess the variability in the data from one gene, we have

  • the observed standard deviation for that gene

  • that of all the other genes

  • ⇒Regularisation, Bayesian estimation


Alternative isoform regulation
Alternative isoform regulation

Alejandro

Reyes

Data: Brooks, ..., Graveley, Genome Res., 2010


Count table for a gene
Count table for a gene

  • number of reads mapped to each exon in a gene

  • treated_1 treated_2 control_1 control_2

  • E01 398 556 561 456

  • E02 112 180 153 137

  • E03 238 306 298 226

  • E04 162 171 183 146

  • E05 192 272 234 199

  • E06 314 464 419 331

  • E07 373 525 481 404

  • E08 323 427 475 373

  • E09 194 213 273 176

  • E10 90 90 530 398 <--- !

  • E11 172 207 283 227

  • E12 290 397 606 368 <--- ?

  • E13 33 48 33 33

  • E14 0 33 2 37

  • E15 248 314 468 287

  • E16 554 841 1024 680

  • [...]


Glm approach to detect changes in relative exon usage bioconductor package dexseq
GLM-approach to detect changes in relative exon usage: Bioconductor package DEXSeq

  • counts in gene i, sample j, exon l

size factor

dispersion

expression strength in control

change in expression due to treatment

fraction of reads falling onto exon l in control

change to fraction of reads for exon l due to treatment


Prkcz
PRKCZ Bioconductor package DEXSeq

PFC

CB

data:

D Brawand et al.

Nature 2011


Regulation of alternative exon usage
Regulation of (alternative) exon usage Bioconductor package DEXSeq

  • Data: multiple replicate samples each from:

  • 6 primate species (hsa, ppa, ptr, ggo, ppy, mml) X

  • 5 tissues (heart, kidney, liver, brain, cerebellum)

  • Brawand et al. Nature 2011 (Kaessmann Lab, Lausanne, CH)



Classification of exons
Classification of exons Bioconductor package DEXSeq





Tissue-dependent usage patterns are associated with splicing factor binding motifs and suggest a cis-regulatory code


Summary tissue dependent exon usage
Summary tissue-dependent exon usage factor binding motifs and suggest a cis-regulatory code

  • Detection of tissue-dependent regulation and its conservation across species at unprecedented scale and precision.

  • Most of tissue-dependent alternative exon usage in primates is

  • low amplitude

  • noise

  • little evidence for conservation

  • However, a significant fraction is

  • high amplitude

  • conserved

  • associated with function in mRNA life-cycle & localisation, translation regulation, protein interaction & function


Good scientific software is like a scientific publication
Good scientific software is like a scientific publication factor binding motifs and suggest a cis-regulatory code

  • Reproducible

  • Peer-reviewed

  • Easy to access by other researchers & society

  • Builds on the work of others

  • Others will build their work on top of it

  • In bioinformatics, the software is the ‘real thing’, the paper is the advertisement


  • An international factor binding motifs and suggest a cis-regulatory codeopen sourceand open development software project

  • Statisticalmethods for the analysis of genomic data

  • Publication-quality graphics

  • Integration of biological metadata in the analysis of experimental data

  • Software: accessible, extensible, interoperable, transparent, well-documented

  • Approach: rapid development, code re-use, self-documenting datasets

  • Reproducible research

  • Training

  • Six-monthly release cycle; release 1.0 in March 2003 (15 packages), …, release 2.12 in April 2013 (>600 packages)‏

  • The world’s largest bioinformatics project.


Bioconductor factor binding motifs and suggest a cis-regulatory codeShort Course:

Brixen, South Tyrol, 23-28 June 2013

Bioinformatics of HT Sequencing Data

RNA-Seq, ChIP-Seq

Statistical testing, regression

Machine Learning, Gene Set enrichment analysis

Bioconductor Conference: Seattle, WA, 17-19 July 2013

Developer MeetingCambridge Dec 2013

Many further short courses & developer meetings: see www.bioconductor.org


How do we know which genes do what? factor binding motifs and suggest a cis-regulatory code

  • Forward genetics

  • from phenotypes to genes

  • → genome-wide association studies

  • → cancer genome sequencing

Reverse genetics

from genes to phenotypes

→ deletion libraries

→ high-throughput RNAi


Genetic interactions
Genetic interactions factor binding motifs and suggest a cis-regulatory code

RNAi x RNAi

DNA-mutation x RNAi

DNA-mutation x drug


Many chromatin modifiers

Clonal heterogeneity

Many recurrent mutations with low frequency

Questions:

How to assign function to the rare somatic mutations (“de-stratification”)?

Need systems overview over genetic interactions

Understand tumor phylogenies and mutual exclusivity


High throughput rnai and automated cellular phenotyping
High-throughput RNAi and automated cellular phenotyping tumour genomes

RNAi or drug library

Feature

extraction

Segmentation

g.x g.y g.s g.p g.pdm

[1,] 123.1391 3.288660 194 67 9.241719

[2,] 206.7460 9.442248 961 153 20.513190

[3,] 502.9589 7.616438 219 60 8.286918

[4,] 20.1919 22.358418 1568 157 22.219461

[5,] 344.7959 45.501992 2259 233 35.158966

[6,] 188.2611 50.451863 2711 249 28.732680

[7,] 269.7996 46.404036 2131 180 26.419631

[8,] 106.6127 58.364243 1348 143 21.662879

[9,] 218.5582 77.299007 1913 215 25.724580

[10,] 19.1766 81.840147 1908 209 26.303760

[11,] 6.3558 62.017647 340 68 10.314127

[12,] 58.9873 86.034128 2139 214 27.463158

[13,] 245.1087 94.387405 1048 123 18.280901

[14,] 411.2741 109.198678 2572 225 28.660816

[15,] 167.8151 107.966014 1942 160 24.671533

[16,] 281.7084 121.609892 2871 209 31.577270

Quantitative cell and organelle features

multivariate phenotypic landscape

Michael Boutros

Boutros, Bras, Huber, Genome Biol. 2006

Fuchs, Pau et al. Mol. Sys. Biol. 2010

Pau, Fuchs et al. Bioinf. 2010

Neumann et al. Nature 2010

Kuttenkeuler et al. J. Innate Imm. 2010

Axelsson et al. BMC Bioinf. 2011

Horn et al. Nature Methods 2011

Gregoire Pau


Genetic interactions for multiple phenotypes
Genetic interactions for multiple phenotypes tumour genomes

384-well plates, microscopy readout with 3 channels (DAPI, phospho-His3, aTubulin)

Fly: 1367 x 72 genes (Nat. Methods 2011 & unpublished)

Human: 323 x 20 (Nat. Methods 2013)

neg. ctrl

Rho1 dsRNA

Dynein light chain dsRNA

number of cells

area

mitotic index

shape

variances

01/23/11

Bernd Fischer

Horn*, Sandmann*, Fischer*, ..., Huber, Boutros. Nature Methods 2011

16


Multiple phenotypes are observed tumour genomes

z-score

z-score

z-score

z-score



1293 target genes x 2 dsrna x 72 query genes x 2 dsrna x 21 features

3D data cube tumour genomes

1293 target genes x 2 dsRNA x 72 query genes x 2 dsRNA x 21 features

Thomas Horn

Thomas Sandmann


Genetic interaction map tumour genomes

clustering of genetic interaction profilesred: ribosome biogenesisgreen: kinetochoreblue: centrosome


Members of same protein complexes are enriched for high correlation of interaction profiles

Co-complex score tumour genomes

members of same protein complexes are enriched for high correlation of interaction profiles


Co-complexity score matrix tumour genomes

Chaperonin-containing

T-complex

anaphase-promoting

complex

γ-tubulin

ring complex

DNA−directed

RNA polymerase II

core complex

26S proteasome



Cancer mutations complex

GI-map

Glio-

blastoma

Medullo-

blastoma

Breast

cancer

Prostate

carcinoma

Colon

carcinoma



Genetic interactions in human cells
Genetic interactions in human cells complex

~300 genes

>200 features

colon cancer cells



Genotype dependence of drug sensitivity cll et al
Genotype-dependence of drug sensitivity (CLL et al.) complex

etc.

Small molecule library

Automated

seeding of cells

Measurement of ATP-levels

Thorsten Zenz,

Leo Sellner, NCT


drugs complex

primary tumour samples

with Thorsten Zenz, Leo Sellner, NCT

L. Sellner


Quality control of dsRNA designs complex

possible off-target effects 2 independent dsRNA designs per genequality criterion:cor. of multi-phenotype interaction profile between designs1293 genes passed QC


The transient and quantitative nature of rnai induced phenotypes
The transient and quantitative nature of RNAi induced phenotypes

log2(rel.

cell number)

Ras85D (ng)

drk (ng)

  • reagent concentration

  • (relative) timing of reagent applications and readout

Interaction score

positive (alleviating)

none

negative (aggravating)

7/11

01/23/11

Thomas Sandmann

Horn*, Sandmann*, Fischer*, ..., Huber, Boutros. Nature Methods 2011

16



Interaction map phenotypes

cell number phenotype

1 AKT1-/-&AKT2-/-

2 MEK2-/-

3 AKT1-/-

4 CTNNB1 mt-/wt+

5 PARENTAL007

6 P53 -/+

7 P53-/-

8 PTEN-/-

9 PI3KCA mt-/wt+

10 KRAS mt-/wt+

11 BAX-/-

12 MEK1-/-

13 PARENTAL001

drugs

cell lines


Interaction map phenotypes

cell number phenotype

FDR=0.2

1 AKT1-/-&AKT2-/-

2 MEK2-/-

3 AKT1-/-

4 CTNNB1 mt-/wt+

5 PARENTAL007

6 P53 -/+

7 P53-/-

8 PTEN-/-

9 PI3KCA mt-/wt+

10 KRAS mt-/wt+

11 BAX-/-

12 MEK1-/-

13 PARENTAL001

drugs

cell lines


Interaction map phenotypes

cell eccentricity phenotype

FDR=0.1

1 AKT1-/-&AKT2-/-

2 MEK2-/-

3 AKT1-/-

4 CTNNB1 mt-/wt+

5 PARENTAL007

6 P53 -/+

7 P53-/-

8 PTEN-/-

9 PI3KCA mt-/wt+

10 KRAS mt-/wt+

11 BAX-/-

12 MEK1-/-

13 PARENTAL001

drugs

cell lines



drugs phenotypes

Clustering of interaction profiles: “guilt by association”

drugs


Clustering of drug interaction profiles phenotypes

  • Cluster 1:

  • inhibitor of microtubule function

  • Cluster 2:

  • Topoisomerase II

  • DNA Metabolism

  • CDK2

  • Cluster 3:

  • folic acid metabolism

  • Caspase 3 activator


Summary phenotypes

  • we established a method to detect synthetic genetic interactions using isogenic cell lines

  • we are able to detect specific interactions

  • similar drug interaction profiles can be linked to similar biological processes


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