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emergentec biodevelopment GmbH Rathausstrasse 5/3 A-1010 Vienna, Austria emergentec

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

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emergentec biodevelopment GmbH Rathausstrasse 5/3 A-1010 Vienna, Austria emergentec

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  1. in-silico workflows for biomarker and target identification emergentec biodevelopment GmbH Rathausstrasse 5/3 A-1010 Vienna, Austria www.emergentec.com Bernd Mayer bernd.mayer@emergentec.com

  2. 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.

  3. 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.

  4. ELISA epitope VACCINE THER. AB epitope antibody antibody AB in IHC biomarkers & application

  5. to candidates derived from a functional context from Bornholdt et al. 2005Science 310(5747):449-51 the trend ...from descriptive lists of candidates

  6. agenda I. Sequential (iterative) workflows II. Parallel (integrated) workflows

  7. omics selection genomics transcriptomics proteomics metabolomics ... expression co-regulation interaction networks functional pathways ... verification PCR ELISA, IHC, siRNA ... characterization structure location determinants ... sequential procedure

  8. 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

  9. co-regulation analysis network analysis refinement of SE/SP statistical analysis

  10. 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

  11. 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

  12. 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/

  13. subcellular location 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

  14. and their experimental verification 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

  15. goals object lists ID1 ID2 ... ... IDn • object dependencies • data consolidation • reduce false positives • derive functional context a parallel approach concept

  16. Ansatz for Dynamical Hierarchies. 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

  17. omicsNET characteristics Characterization of protein-interaction networks in tumors. Platzer A, Perco P, Lukas A, Mayer B, BMC Bioinformatics. 2007 Jun 27;8:224.

  18. example analysis 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

  19. and expanding annotation

  20. in brief 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

  21. some tools & 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.

  22. acknowledgements @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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