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A multivariate approach to the integration of multi-omics datasets. Author Meng, Chen. It allows the selection of promising drug combinations suitable to inhibit a given target protein here DDR1.

The graph-view shows the protein-drug interaction landscape of selected drugs. Predicted inhibitory effects are highlighted in the graph by dark grey edges of varying thickness proportional to the EC 50 and proteins coloured in different shades of blue indicates the level of inhibition.

Predicted inhibitory effects are only shown in case they surpass a user-defined cutoff left vertical slider. The concentration of a drug can be adjusted by either clicking an edge sets the concentration of the drug to the EC 50 of that interaction , by manually adjusting the concentration using the sliders on the left or by entering the desired concentration into the textbox left; next to sliders.

On this basis, all drugs showing at least one inhibitory effect on one of the proteins are taken into consideration. In case both fields are used, the union of all drugs, either inhibiting at least one of the target proteins or selected manually, is used.

The graph-view shows the protein-drug interaction landscape of the selected drugs. Each drug selected for the analysis is displayed on the left hand side of the view.

The checkbox can be used to disable hide a drug from both views. In addition, the dose of each drug can be adjusted by moving the slider or by manually entering a desired drug-concentration.

The predicted inhibition of a particular protein in both the graph and the table view are updated in real-time based on the given concentration of a drug.

Predicted inhibitory effects are highlighted in the graph by grey edges of varying thickness proportional to EC 50 and blue proteins, the shading of which indicates the level of inhibition.

In addition to the manual drug concentration, selecting an edge between a protein:drug pair sets the concentration of the drug to the EC 50 of that interaction.

With the inclusion of dose-resolved viability data from several large-scale drug sensitivity studies 27 — 30 , ProteomicsDB is now providing tools for fast exploration of dose-response curves quantifying sensitivity and resistance of hundreds of cell lines across hundreds of inhibitors Figure 7.

We downloaded dose-resolved viability data from various sources and converted them to relative viabilities, in order to bring the different datasets onto the same scale.

ProteomicsDB incorporates several publicly available large-scale drug sensitivity screens. A Each drug sensitivity dataset in ProteomicsDB can be explored in a cell-line- or inhibitor-centric way and general statistics are shown for a given selection.

B Users can interactively filter dose-response models based on multiple parameters such as AUC, R 2 , lower bound, pEC 50 and relative effect percent decrease in viability over the tested concentration range.

C The distribution of a given parameter is visualized in a bar chart on selection of an axis in B. D The underlying raw and fitted data can be investigated on click on one or many of the bars highlighted in orange.

The scatter plot highlights the EC 50 for the selected cell line:drug pairs. After selecting a dataset of interest, analyses can be either cell line- or inhibitor-centric.

For this purpose, either one cell line can be chosen, comparing all available inhibitors on this single cell line, or, vice versa, an inhibitor can be selected in order to compare the viabilities of all tested cell lines Figure 7A.

Using a parallel coordinates plot, it is possible to filter for multiple model parameters and different summary statistics simultaneously Figure 7B.

The large collection of experimental and reference spectra stored in ProteomicsDB opens the door for the development of new functionalities.

For example, since the ProteomeTools project covers the entire human proteome with reference spectra, a systematic orthogonal evaluation of protein FDR using synthetic spectra becomes possible.

This will provide further validation of protein identification events in ProteomicsDB. Similarly, spectra in ProteomicsDB could be downloaded or compared directly to user data in order to validate the identification of proteins with no prior observations.

Furthermore, the combination of reference and experimental spectra and their chromatographic properties will enable the development of tools to guide the development of directed and targeted experiments by custom data-driven spectral library generation.

Such tools could make use of the cell line- and tissue-specific protein background identified before and could provide experiment-driven estimates of interfering peptides.

This was essential in the development of the picked-FDR approach While a significant proportion of the data stored in ProteomicsDB is already programmatically accessible, an obvious next step is the extension of this service to enable systematic access to all data.

By extending the accessibility of data, ProteomicsDB might become an important infrastructure for computational scientists to develop and test new algorithms and for biologists to generate and test new hypotheses.

We will further broaden the scope of ProteomicsDB over the next years. One of the upcoming extensions is to provide protein abundance estimates for other organisms, such as Mus musculus and Arabidopsis thaliana.

While the data model already supports the import of data from other model organisms, the user interface will need to be adjusted.

With the ability to store expression patterns from other organisms, cross-species comparisons becomes possible enabling a plethora of questions to be asked and answered.

With the integration of other data sources into ProteomicsDB, the comparative visualization of multiple orthogonal dataset is within reach.

However, the integrated data model of ProteomicsDB will also enable the interactive visualization of multiple data sources at once in order to provide a more comprehensive view on omics data.

Given the computational power of the underlying hardware, it is even conceivable to provide the infrastructure and interfaces to users to upload their own data for direct comparison and for direct model training on their phenotypic measurements Supplementary Data are available at NAR online.

Conflict of interest statement. They have no operational role in the company and their involvement had no impact on the current study.

Aebersold R. Mass spectrometry-based proteomics. Google Scholar. Han X. Hawkridge A. Mass spectrometry-based biomarker discovery: toward a global proteome index of individuality.

Palo Alto Calif. Riffle M. Proteomics data repositories. Perez-Riverol Y. Making proteomics data accessible and reusable: current state of proteomics databases and repositories.

Vizcaino J. Nucleic Acids Res. Desiere F. The PeptideAtlas project. Craig R. Open source system for analyzing, validating, and storing protein identification data.

Proteome Res. Wang M. PaxDb, a database of protein abundance averages across all three domains of life. Cell Proteomics. Schaab C. Analysis of high accuracy, quantitative proteomics data in the MaxQB database.

Färber F. Wilhelm M. Mass-spectrometry-based draft of the human proteome. Legrain P. The human proteome project: current state and future direction.

Gaudet P. The neXtProt knowledgebase on human proteins: update. Neuhauser N. Zolg D. Building ProteomeTools based on a complete synthetic human proteome.

PROCAL: A set of 40 peptide standards for retention time indexing, column performance monitoring and collision energy calibration.

Bantscheff M. Quantitative mass spectrometry in proteomics: a critical review. Lemeer S. Comparing immobilized kinase inhibitors and covalent ATP probes for proteomic profiling of kinase expression and drug selectivity.

Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Savitski M. Tracking cancer drugs in living cells by thermal profiling of the proteome.

Ritz C. Bioassay Analysis using R. Jensen L. Kanehisa M. KEGG: new perspectives on genomes, pathways, diseases and drugs.

Shannon P. Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Genome Res. Barretina J. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Iorio F.

A landscape of pharmacogenomic interactions in cancer. Medico E. The molecular landscape of colorectal cancer cell lines unveils clinically actionable kinase targets.

Rees M. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Gujral T.

Exploiting polypharmacology for drug target deconvolution. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.

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Volume Article Contents Abstract. Oxford Academic. Patroklos Samaras. Martin Frejno. Siegfried Gessulat. Maximilian Barnert.

Harald Kienegger. Helmut Krcmar. Judith Schlegl. Hans-Christian Ehrlich. Stephan Aiche. Bernhard Kuster , Bernhard Kuster. To whom correspondence should be addressed.

Mathias Wilhelm. Correspondence may also be addressed to Bernhard Kuster. Email: kuster tum. These authors contributed equally to this work as first authors.

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Bernhard Köster CCCIT | Center for Clinical Cancer and Immunology Trials

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28 July 2020