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in-silico workflows for biomarker and target identification. emergentec biodevelopment GmbH Rathausstrasse 5/3 A-1010 Vienna, Austria www.emergentec.com. Bernd Mayer [email protected] differential abundance. candidates. explorative. hypothesis driven. functional

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in-silico workflows for

biomarker and target identification

emergentec biodevelopment GmbH

Rathausstrasse 5/3

A-1010 Vienna, Austria

www.emergentec.com

Bernd Mayer

[email protected]


differential

abundance

candidates

explorative

hypothesis

driven

functional

interpretation

from Liu et al.

2006Cancer Cell 9(4):245-7

background

emergentec biodevelopment is

developing and applying computational tools for biomarker and target identification.


definitions

WORKFLOW

Framework for a progression of computational steps, sequentially or in parallel,

for identifying biomarkers and targets.

BIOMARKER

A compound (gene, RNA, protein, metabolite, etc.) with

high sensitivity and specificity for a given cellular status.

TARGET

A compound with biomarker characteristics which can be addressed

by therapy means for altering a given cellular status.


ELISA

epitope

VACCINE

THER. AB

epitope

antibody

antibody

AB in IHC

biomarkers & application


to candidates derived from a functional context

from Bornholdt et al.

2005Science 310(5747):449-51

the trend

...from descriptive lists

of candidates


agenda

I. Sequential (iterative) workflows

II. Parallel (integrated) workflows


omics

selection

genomics

transcriptomics

proteomics

metabolomics

...

expression

co-regulation

interaction networks

functional pathways

...

verification

PCR

ELISA, IHC,

siRNA

...

characterization

structure

location

determinants

...

sequential procedure


focus on SE/SP

41,121 features

  • 1. Filter genes

    • Quality Filter (80%)

1.

  • 2. Replace missing values

    • KNN – Algorithm

2.

16,100 features

  • 3. Filter genes

    • Variation Filter (SD > 0.9)

3.

1,081 features

  • 4. Statistical testing

    • t-test with maxT correction

4.

n candidates


co-regulation analysis

network analysis

refinement of SE/SP

statistical analysis


ID1

ID2

ID3

Detection of coregulation in differential gene expression profiles.

P Perco, A Kainz, G Mayer, A Lukas, R Oberbauer, B Mayer, Biosystems 82, 235-247 (2005).

co-regulation

correct / expand Omics results from statistics


differentially expressed genes

Stress Responses and Conditioning Effects in Mesothelial Cells Exposed to Peritoneal Dialysis Fluid.

K Kratochwill, M Lechner, Ch Siehs, HC Lederhuber, P Rehulka, M Endemann, DC Kasper, KR Herkner, B Mayer, A Rizzi,

Ch Aufricht, Journal of Proteome Research, 8, 1731-1747 (2009).

Biomarkers for cardiovascular disease and bone metabolism disorders in chronic kidney disease:

A systems biology perspective.

P Perco, J Wilflingseder, A Bernthaler, M Wiesinger, M Rudnicki, B Wimmer, A Kainz, A Lukas, G Mayer, B Mayer,

R Oberbauer, Journal of Cellular and Molecular Medicine, 12, 1177-1187 (2008).

differentially expressed genes

genes of a particular functional category

protein networks

and interpret in the context of PPIs


Machine learning approaches for prediction of linear B-cell epitopes on proteins.

J Sollner, B Mayer, Journal of Molecular Recognition 19, 200-208 (2006).

Identifying discontinuous antigenic determinants on proteins based on shape complementarities and

binding energies.

R Rapberger, A Lukas, B Mayer, Journal of Molecular Recognition 20, 113-121 (2007).

addressable

Workflows for computing subcellular location, PTMs,

accessibility, antigenicity, etc.

http://taverna.sourceforge.net/


subcellular location epitopes on proteins.

co-regulation

CCP data set;

Schaner et al. 2003;

Welsh et al. 2001;

conjoint pathways

interaction networks

candidate protein selection

in silico immunogenicity scoring

experimental verification

example workflow

Meta-UP (86)

up-regulated genes

3

Meta-UP (192)

publication meta-analysis

SEREX (81)

exp. derived autoantigens

Meta-DOWN (106)

down-regulated genes

1


and their experimental verification epitopes on proteins.

Linking the ovarian cancer transcriptome and immunome.

R Rapberger, P Perco, C Sax, T Pangerl, C Siehs, D Pils, A Bernthaler, A Lukas, B Mayer, M Krainer,

BMC Systems Biology, 3;2:2. (2008).

Identification of a novel melanoma biomarker derived from melanoma-associated endogenous retroviruses.

J Humer, A Waltenberger, A Grassauer, M Kurz, J Valencak, R Rapberger, K Wolff, T Muster, B Mayer, H Pehamberger,

Cancer Research 66, 1658-1663 (2006).

exp. verification

interpreting candidates in their context


goals epitopes on proteins.

object lists

ID1

ID2

...

...

IDn

  • object dependencies

  • data consolidation

  • reduce false positives

  • derive functional context

a parallel approach

concept


Ansatz for Dynamical Hierarchies. epitopes on proteins.

S Rasmussen, N Baas, B Mayer, M Nilsson, MW Olesen. Artificial Life 7, 329-353 (2001).

omicsNET construction

No sequential data enrichment, but one-step data integration


omicsNET characteristics epitopes on proteins.

Characterization of protein-interaction networks in tumors.

Platzer A, Perco P, Lukas A, Mayer B, BMC Bioinformatics. 2007 Jun 27;8:224.


example analysis epitopes on proteins.

Rosenwald et al., 2001

Rosenwald et al., 2002

Rosenwald et al., 2002

Rosenwald et al., 2001

Zhan et al., 2002

A dependency graph approach for analysis of differential gene expression profiles.

A Bernthaler, I Mühlberger, R Fechete, P Perco, A Lukas, B Mayer

Molecular Biosystems2009; June 3 ahead of print


and expanding annotation epitopes on proteins.


in brief epitopes on proteins.

Bioinformatics as support / driver for biomarker and target discovery:

  • Clearcut experimental study designs

  • Thorough local data management

  • Integrate the data sources out there with your own (Omics) data

  • Make use of the public domain analysis workflows

  • Do not stick with statistics but go for functional context in candidate selection


some tools epitopes on proteins.& databases

For data preparation and statistics:

R-Bioconductor, MeV, etc.

For coregulation:

oPOSSUM, CORG, CONFAC, etc.

For protein interactions:

OPHID, INTACT, etc.

For pathways:

KEGG, PANTHER, GO, etc.

For reference data repositories:

arrayExpress, GEO, SMD, Oncomine, Swiss2D Page, etc.

For annotation:

GeneCards, iHOP, etc.

For viewing:

Cytoscape, etc.

For context analysis:

STRING

For target characterization:

PSORT, PROSITE, SABLE, PSIPRED, etc.


acknowledgements epitopes on proteins.

@emergentec

Paul Perco and team

Johannes Soellner and team

Martin Haiduk and team

OVCAD

SYNLET

predictIV

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data.

Perco P, Rapberger R, Siehs C, Lukas A, Oberbauer R, Mayer G, Mayer B

Electrophoresis2006; 27(13):2659-2675.

A dependency graph approach for analysis of differential gene expression profiles.

Bernthaler A, Mühlberger I, Fechete R, Perco P, Lukas A, Mayer B

Molecular Biosystems2009; June 3 ahead of print


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