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dc.contributor.advisor Beißbarth, Tim Prof. Dr.
dc.contributor.author Wachter, Astrid
dc.date.accessioned 2017-05-17T09:38:42Z
dc.date.available 2017-05-17T09:38:42Z
dc.date.issued 2017-05-17
dc.identifier.uri http://hdl.handle.net/11858/00-1735-0000-0023-3E4B-9
dc.language.iso eng de
dc.relation.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc 570 de
dc.title Data Integration of High-Throughput Proteomic and Transcriptomic Data based on Public Database Knowledge de
dc.type doctoralThesis de
dc.contributor.referee Beißbarth, Tim Prof. Dr.
dc.date.examination 2017-03-22
dc.description.abstracteng With the advance of high-throughput methods enabling deep characterization of the cell on different cellular layers, ideas to combine different data types for inference of regulatory processes have emerged. Such integration promises an improved molecular understanding of physiological and pathophysiological mechanisms, which aids in the identification of drug targets and in the design of therapies. Current integration approaches are based on the idea of reducing false negatives by reinforcing concordant information between datasets. In most cases optimized for a specific integration setting and data structure, these approaches are rarely accompanied by bioinformatic tools enabling researchers to work on their own datasets. In this thesis I present the public knowledge guided integration of phosphoproteomic, transcriptomic and proteomic time series datasets on the basis of signaling pathways. This integration allows to follow signaling cascades, to identify feedback regulation mechanisms and to observe the coordination of molecular processes in the cell by monitoring temporal variation upon external perturbation. To extract these cellular characteristics the cellular layers on which the individual datasets have been generated are taken into consideration. Separate downstream and upstream analyses of phosphoproteome and transcriptome data, respectively, and subsequent intersection analysis are coupled with a combination of network reconstruction and inference methods. Graphical consensus networks and co-regulation patterns can be extracted by this cross-platform analysis. Moreover, it provides high flexibility in terms of high-throughput platforms used for data generation as analysis is based on preprocessed datasets. On the examples of epidermal growth factor signaling and B cell receptor signaling we were able to show that the results gained by this integration method confirm known regulatory patterns but also point to interactions that were not described previously in these contexts. This is demonstrated by performing a response-specific analysis instead of the typical layer-specific analysis. Limitations of the approach described here are linked to database-bias and -dependency, to the low temporal resolution of high-throughput measurements and to data standardization. While overcoming these issues constitutes a challenge for the whole systems biology community, the integration approach itself can be optimized in future by working with refined disease-specific and tissue-specific signaling pathway models and database entries. The presented integration method was implemented as R software package 'pwOmics' and made available to other researchers. de
dc.contributor.coReferee Wingender, Edgar Prof. Dr.
dc.subject.eng cellular layer de
dc.subject.eng data integration de
dc.subject.eng high-throughput data de
dc.subject.eng proteomics de
dc.subject.eng transcriptomics de
dc.identifier.urn urn:nbn:de:gbv:7-11858/00-1735-0000-0023-3E4B-9-7
dc.affiliation.institute Göttinger Graduiertenschule für Neurowissenschaften, Biophysik und molekulare Biowissenschaften (GGNB) de
dc.subject.gokfull Biologie (PPN619462639) de
dc.identifier.ppn 887658547

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