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Bernhard KГ¶ster Inhaltsverzeichnis. Bernhard Stern-Szana (geb. Juni in Riga; gest. September in Wien) war ein vielseitiger deutschbaltisch-österreichischer. Bernhard KГ¶ster Other by author. Bernhard Stern-Szana (geb. Juni in Riga; gest. September in Wien) war ein vielseitiger deutschbaltisch-österreichischer. Bernhard Stern ist der Name folgender Personen: Bernhard Stern (Politiker) (–), polnisch-österreichischer Unternehmer und Politiker; Bernhard. Bernhard Stern-Szana (geb. Juni in Riga; gest. September in Wien) war ein vielseitiger deutschbaltisch-österreichischer Schriftsteller. war auf gutem Wege – auch Dank der vielen konstruktiven und originellen Vorschläge für Workshops, Panel-Diskussionen und Stargäste von euch. Sie sind.KГ¶hne CH, Hofheinz R, Mineur L, Letocha H, Greil R, Thaler J, Fernebro E, Gamelin Tinhofer I, Anether G, Senfter M, Pfaller K, Bernhard D, Hara M, Greil R. in myeloma cell lines resistant to steroids and 2-chlorodeoxyadenosine (2-CdA). Bernhard Stern ist der Name folgender Personen: Bernhard Stern (Politiker) (–), polnisch-österreichischer Unternehmer und Politiker; Bernhard. Bernhard Stern-Szana (geb. Juni in Riga; gest. September in Wien) war ein vielseitiger deutschbaltisch-österreichischer Schriftsteller.
Bernhard KГ¶ster Video28 July 2020
Publikationsliste auf der Website des Lehrstuhls abgerufen am 3. Januar In: Faszination Forschung Ausgabe Sommerschule für Proteomik in Neustift, Südtirol , abgerufen am 3.
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Gegen 19 Uhr waren wir wieder in Beverungen. Bereits am Im Jahr haben sich weitere 68 Gesellen dem St. Josephs-Verein angeschlossen. Aus diesem Anlass findet am Dezember ein Dankgottesdienst in der St.
Baptist Kirche in Beverungen statt. Ein Teil dieser jungen Menschen bildet die Kolping Tanzgarde. Diese Gruppe trifft sich monatlich und veranstaltet ein eigenes, seniorengerechtes Programm.
So werden zum Pfarrfest viele Stunden als Arbeitsleistung erbracht. Bei den Prozessionen der Gemeinde wird ein Altar gestaltet und der Himmel wird von den Mitgliedern getragen.
Die Kreuzkapelle auf dem Kapellenberg wurde vor ca. Seitdem beteiligt sich die Kolpingfamilie an ihrem Unterhalt.
Die Veranstaltungen werden im Vereinsprogramm, der heimischen Presse und im Schaukasten bekannt gegeben. Wilhelm Nolte 1. In Beverungen startet der Kurs im kath.
Pfarrheim am Montag, Um Infos und Anmeldung bei Daniela Matrisch Tel. Nicht ganz! In der Krippe lag Jesus als Neugeborener.
Maria und Josef waren arm und fanden keine Herberge. Die Krippe symbolisiert uns dennoch, dass Jesus dort warm und geborgen lag. Zu Beginn des Jahres fand die traditionelle Tannenbaum-Aktion statt.
Vom Juli bis In Kleingruppen wurde dort gespielt, getobt, gelacht, gebastelt und gebacken. Im November trafen sich ca.
Am Dienstag, Herzlich Willkommen. Haben wir gutes Wetter? An diesem Wochenende lernten sich die Leiter untereinander besser kennen und es entwickelte sich ein Team, welches in verschiedenen Gebieten eine Einheit bildete.
Bei diesem Ausflug wurden sogar Promis, wie z. In der zweiten Woche trafen sich die Kinder in festen Kleingruppen, um dort kreativ zu werden.
So wurden z. Zwischen wilden Verfolgungsjagden oder Schusswechseln zuckte so manches Kind zusammen und war begeistert. Beginn ist um Jeder ist herzlich willkommen.
Wir beginnen um Messe in der Pfarrkirche. Aktionen geplant. Die aktuellen Termine werden auf der Jahreshauptversammlung am Ihr Vorstandsteam der kfd Dalhausen Weitere Termine der kfd Januar , als aktiver Sternsinger teilzunehmen.
Wir freuen uns auf eure Anmeldung bis zum Dezember Das Anmeldeschreiben kann bei Frau Gering nach der 1.
Das Vorbereitungstreffen findet am Freitag, dem Januar , im Pfarrheim Dalhausen um In einer Gruppe von Gleichgesinnten werden Sie auf ganz unterschiedliche Art und Weise dazu angeleitet, zur Ruhe zu kommen, in sich hinein zu lauschen, sich auszutauschen und in Kontakt mit Gott zu treten.
Nicht viel: Nur eine Wolldecke, Sehnsucht und Neugierde. Sie stehen jeweils unter einem besonderen Thema und werden mit festen und wechselnden Impulsen und Methoden gestaltet.
Der Nachmittag im Tierpark Sababurg war mit einer Tierparkrallye sehr ereignisreich. Beim T-Shirt pimpen konnte jeder seiner kreativen Ader freien Lauf lassen.
Bei strahlendem Sonnenschein konnten wir mit allen Teilnehmern noch ein Eis essen. Es wird wieder ein buntes Programm geben.
Weihnachtsvesper Feierliche Vigilien Mitternachtshochamt Messe um 9. Mittwoch, den Dienstag, den Mittwoch, den 7.
Begonnen hatte sie am Vatikanischen Konzils bei uns umzusetzen. Zugleich wissen wir uns aber auch solidarisch mit vielen Gemeinden und Gemeinschaften, die vor derselben Herausforderung stehen.
Darum ist am Sonntag, Dezember um Tietelsen St. Diesen drei Frauen ist es zu verdanken, dass sich eine Seniorengemeinschaft bilden konnte.
Auch der gesellige Teil kommt nicht zu kurz, zum Beispiel in der Karnevalszeit. Sie wird von den Kindern des St. Die wurden dann an die Ballons gebunden und in den strahlend blauen Himmel geschickt.
Auch ein paar Erwachsene machten mit. Der Wind trieb alle Ballons Richtung Osten. Zuvor hatte Vikar Stephan Massolle im Gottesdienst sogar einen Ballon bis unter die Kirchendecke steigen lassen und das Thema in seiner Predigt verarbeitet.
Der von Melina Dierkes wurde in Keula gefunden, das liegt bei Nordhausen und ist Kilometer von Tietelsen entfernt. Michael Am 4. Adventssonntag, Pastoralverbundsleiterkonferenz etc.
Weihnachtstag Messe Messe im Seniorenhaus Messe 40 November bis Januar 1. Klinski u. Bremer, Steinhoff u. Alois u.
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Elisabeth Kohlandt Leb. Maegery u. Samolik, Misch u. Matras u. Reddig u. August u. Tegethoff u. Knipping Pas oralverbund Beverunger Land 41 42 Montag, Treffpunkt: Kath.
Pfarrheim Beverungen um Messe in der St. Marien Kirche Dalhausen. The integration of transcriptome and proteome profiles of cells in the NCI cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis.
Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset.
Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes.
We provided novel markers highly associated to tumor molecular subtype combined from four data platforms.
Conclusion: We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals.
The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets.
MCIA provides simple graphical representations for the identification of relationships between large datasets.
Bernhard KГ¶ster Video30 July 2020 Vom 1. Europa krankt auch in der Corona Krise daran keine gemeinsamen Antworten zu finden. Mai zu Wort. Salzburg Cancer Research Institute. Darin war sich die Jury schnell einig. Erratum in: Virtualracing Engl J Med. Bernhard Stern-Szana geb. Int J Clin Pharmacol Ther. Maurizio Cucchiter. B A heatmap-like bodymap superimposing abundance values of tissues, fluids and cell lines biological sources onto their respective tissues of Beste Spielothek in Unterschnatterbach finden. You have entered an invalid code. Antonia Vieth u. Wenn ich dann antworte Beverungen, dann wird oft gesagt, ach das sind die aus Beverungen, die machen doch schon so viel. For this purpose, ProteomicsDB was carefully designed and organized Figure 1. Combined interactive expression heat map. A scalable approach for protein false discovery rate estimation in large proteomic data Beste Spielothek in Sankt Katharein finden. Elisabeth Kohlandt Leb. Ritz C.
The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner.
Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.
Mass spectrometry has developed into the flagship technology for proteome research much akin to what next generation sequencing has become for genomics and transcriptomics 1 , 2.
Since proteins execute and control most biological processes in all domains of life, they are one of the most frequently targeted class of molecules in the context of drug development.
Today, scientists and clinicians anticipate that proteins will also become a major source of biomarkers 3 useful to diagnose disease, to stratify patients for treatment and to monitor response to therapy to name a few.
At the same time, the volume and complexity of proteomics data generated by modern mass spectrometers is challenging our ability to turn data into tractable hypotheses, within and, particularly, across larger projects.
In order to provide access to previously performed experiments, many different repositories have been developed 4 , 5. However, their focus is often limited to a particular aspect of the data and frequently, protein identification is de-coupled from protein quantification.
PRIDE 6 is currently the community-standard for publishing raw data but also peptide and protein identification results including post-translational modifications.
However—until recently—it lacked an intuitive interface for comparing results across different datasets. The protein abundance database PAXDB 9 stores quantification data from publicly available data, but lacks the underlying peptide identification results.
MaxQB 10 does provide both protein identification and quantification data, but is far less comprehensive than any of the other repositories and also does not allow cross-dataset comparison.
While most of these databases can store meta-data such as sample preparation and data acquisition protocols, specific treatments and the different conditions used in the experimental setup are not stored in a programmatically accessible format.
In addition, none of the aforementioned databases allow storage of other data types. This in turn makes it difficult to systematically explore and mine data across proteomic or multi-omics experiments.
ProteomicsDB is filling this gap by not only enabling cross-dataset comparisons of protein abundance, but also by providing the means to store and analyse proteomics data in contexts other than expression analyses.
The protein-centric web interface provides researchers real-time and use-case-specific access to data for single or multiple proteins using interactive visualizations at different levels of detail.
The data model of ProteomicsDB is able to store identification and quantification data from almost all conceivable proteomics experiments including meta-data such as sample preparation protocols, data acquisition parameters and sample treatment conditions.
More recently, its capabilities have been expanded to also host results from other quantitative omics technologies ranging from drug-protein interaction studies and cell-viability experiments to data from public protein interaction databases and transcriptomes.
In this article, we introduce the different analysis options available in ProteomicsDB and highlight the developments accumulated over the past three years.
ProteomicsDB utilizes the in-memory database management system SAP HANA 11 and was developed to enable the real-time interactive exploration of large collections of quantitative mass spectrometry-based proteomics data A major focus during the initial development of ProteomicsDB was to enable the storage of identification and quantification data on both peptide and protein level, irrespective of the experimental setup and analysis method used.
To this end, ProteomicsDB is able to store the output of any algorithm used for the automatic interpretation of mass spectra database search.
The storage of treatment conditions and the overall experimental design facilitate the analysis of more complex relations within and across different datasets, such as dose- and temperature-dependent assays.
Efficient access to the data in combination with modern web-based visualization technologies facilitates real-time interactive exploration of heterogeneous data in an intuitive and simple way.
All figures and tables available in ProteomicsDB can be downloaded, while an application programming interface allows users to directly interact with the database in order to download raw data for off-line processing or storage Figure 1.
ProteomicsDB consists of three major layers. The bottom layer is the data layer providing information to the calculation layer.
It consists of seven major modules enabling the storage and retrieval of meta data, annotations and quantitative information associated with proteins and biological systems.
Due to in-memory storage of the data layer, calculations using the calculation engine structured query language , graph engine and other integrated programming languages e.
R and Python are highly efficient. The results of these calculations can be explored in the presentation layer offering a variety of different interactive visualizations via the web interface or systematic access via the ProteomicsDB application programming interface API.
Because of the in-memory capabilities of SAP HANA, most of the data shown on the website are not pre-computed, avoiding the need for monthly or yearly builds and enabling rapid adjustments.
The different storage layers and versatile processing capabilities available in HANA enabled the integration of graph and standard relational database features.
This facilitated the incorporation of many different data sources and led to the development of a variety of new features. While all protein-related results stored in ProteomicsDB are mapped to UniProt 15 identifiers, a versatile resource identifier mapping system enables a seamless conversion between different resources, which facilitates easy integration of additional data sources not mapped to UniProt e.
In the following sections, we will start by briefly highlighting the data model used by ProteomicsDB and its developments over the past years.
Subsequently, we will introduce the main features available on ProteomicsDB, which are organized in protein-centric visualizations for single and multiple proteins.
The data model of ProteomicsDB is grouped into 7 major modules Figure 1 : i the meta-data, which contains annotations and ontologies; ii the repository, which contains the mapping of raw data to samples, experiments and projects, as well as associated meta-data and experimental designs; iii peptide and protein identification and quantification data, which stores spectra, the associated database search engine results, as well as peptide and protein abundance information; iv reference identification, which contains reference spectra from measurements of synthetic peptide standards; v the quantitative omics model; vi experiment specific models, such as dose-response models and vii cell-viability data.
See Supplementary Text 1 for more details about the data models and its internal mechanisms. ProteomicsDB is designed to enable researchers to quickly interrogate identification and quantification information of single and multiple proteins.
For this purpose, ProteomicsDB offers two major ways to browse all available data. On the one hand, there is the presentation of information available for a single protein of interest.
On the other hand, there are visualizations of specific aspects of the data for multiple proteins. Currently, four analytical views are implemented and offer the cross-experiment analysis of protein expression, single and multiple drug selection and the exploration of cell viability data.
A ProteomicsDB can be used to interrogate identification and quantification information on either single or multiple proteins. The corresponding domain structure is dynamically generated and alongside it, all observed peptides and post-translational modifications PTMs are displayed.
The features to analyse proteotypicity, reference peptides and FDR estimation for single proteins are fully described in Supplementary Text 2—4 Supplementary Figures S1—4.
The feedback-icon, located on the left on each page, can be used to provide direct feedback, comments or report bugs to us. For the purpose of this paper, we will focus on one protein highlighting all available functions and visualizations throughout the manuscript.
Discoidin Receptor 1 DDR1 is a member of a family of receptor tyrosine kinases RTKs that is activated in response to collagen and is part of the arsenal of cell surface receptors that mediate tumor cell-environment interactions.
The search field can be used to browse proteins by gene name, accession number or protein description. The resulting table shows all available proteins partially matching the search string.
All tables in ProteomicsDB can be filtered and sorted by clicking on a specific column header. Most tables also offer hiding or showing additional columns, which are not shown by default but are always included in downloaded csv files.
Upon selecting a protein of interest, the user sees a brief summary Figure 2B about the information available for the protein, including, but not limited to, the number of peptides which were detected shared and unique on either gene or protein level , the sequence coverage and some basic annotations such as GO terms, chromosomal location, external links and evidence status.
The evidence status is either red, yellow or green indicating missing, questionable and strong evidence for its identification, respectively.
In addition, the domain structure of the protein is dynamically generated and shown in the middle of the page. Aligned to this, all observed peptides and post-translational modifications PTMs are visualized by black bars.
The sequence coverage view can be expanded to investigate which peptides were observed in detail.
Stretches coloured in red indicate that this part is covered by peptides in ProteomicsDB. One or several proteases can be chosen along with different peptide filter criteria, in order to predict which combination of proteases will lead to the highest theoretical cumulative sequence coverage.
This feature can guide users in designing experiments that require high sequence coverage such as PTM or variant identification. The initial view lists all observed peptides including meta-data such as mass, length, uniqueness and the number of observations, as well as different measures of confidence, such as the search engine score.
Each spectrum used for protein inference can be visualized in ProteomicsDB using the built-in spectrum browser. In order to view experimental spectra, an overlay containing all available PSMs for the selected peptide Supplementary Figure S1 top table can be opened by clicking a peptide of interest.
Fragment ions in experimental spectra are annotated on request by an expert system Annotation rules, such as calculated fragment ions and sequence-dependent neutral losses, are stored in the database and can be modified at any time.
Annotation options for the spectrum, general visualization options and a fragmentation table can be opened to the left and right of the spectrum Supplementary Figure S1.
An integrated feature of the spectrum viewer is the mirror representation of a reference spectrum bottom spectrum if available.
These spectra originate from e. This is especially useful when only a few peptides were identified for a specific protein, since such spurious identifications could originate from false matches during the database search.
In case a reference spectrum for the selected peptide is available, the highest scoring PSM matching to the precursor charge and modification status of the selected PSM is chosen and displayed.
Already today, ProteomicsDB stores more than 3 million reference spectra acquired as part of the ProteomeTools project 17 and covers more than k peptides measured in up to 11 different acquisition methods.
For most peptides, multiple reference spectra are available and by default, the one acquired using similar acquisition parameters is shown. An essential feature of ProteomicsDB is the storage and visualization of quantitative data from a wide range of biological sources.
While the initial development focused on the presentation of proteomics data, the generic implementation of ProteomicsDB also enables the storage and visualization of other omics data types, such as RNA-Seq data.
The user can choose the primary data source and can visually explore the expression using a heatmap-like visualization of the human body.
This view also superimposes abundance values of cell lines onto their respective tissue of origin and thus allows the integrated analysis of expression values originating from tissues or body fluids and cell lines.
A ProteomicsDB can visualize expression data from different omics technologies. B A heatmap-like bodymap superimposing abundance values of tissues, fluids and cell lines biological sources onto their respective tissues of origin.
C A bar chart resolving the expression data of b on the level of their biological source. If multiple measurements for the same biological source are available, the error bar indicates the lowest and highest abundance observed for the selected protein.
The bar chart and the bodymap are linked to each other, enabling the selection of either a tissue of origin in the bodymap highlighted in dark red or a biological source in the barchart highlighted in orange.
Here, the lung high expression of DDR1 , was selected in the bodymap, which automatically highlights all corresponding tissues and cell lines in the bar chart EKVX cell and A cell originated from lung tissue.
D A bar chart visualizing sample-specific abundance values of the sources selected in middle bar chart highlighted in orange.
On click on one of the bars, the corresponding sample preparation protocol can be examined. The expression view consists of two major components comprising data selection Figure 3A and visualization Figure 3B — D.
To enable meaningful cross-experiment comparison of expression values, only data from similar sources can be selected. For proteomics, MS1 and MS2 quantification techniques 19 cannot be compared directly, thus the filters only support the selection of either type.
Likewise, the comparison of protein abundance measures originating from full proteome data unbiased expression analysis or affinity type experiments biased abundance analysis is not possible.
The data visualization is composed of three interactive and interconnected elements: i a heatmap-like body map Figure 3B , ii a cell type aggregated bar chart Figure 3C and iii a sample specific bar chart Figure 3D.
The expression of DDR1 is restricted to epithelial cells, particularly in the kidney, lung, gastrointestinal tract and brain. Upon selection of a specific tissue in the heatmap, the middle barchart highlights all cell lines and tissues, which are connected to this tissue e.
Likewise, the selection of a bar in the middle bar chart will highlight the corresponding tissue in the bodymap. This will also trigger the display of an additional bar chart, depicting the expression of the selected protein in a sample-specific manner.
This view directly enables users to investigate the sample preparation and data acquisition parameters for each measurement by clicking on any bar in the bar chart on the right hand side.
Besides visualizing global expression patterns of proteins, ProteomicsDB is also able to make use of the stored experimental design to show changes in protein abundance upon specific treatments and sample handling steps.
Here, we will focus on the description of the Kinobeads data. Beyond this specific example, any relative protein abundance measured as a function of e.
This view lists all available data for the selected protein, including direct and indirect targets as well as background proteins.
In order to filter for binders, different filters are available and can be activated or deactivated. Dose response curves are fitted using a 4-parameter-log-logistic regression Depending on the protein and the selected filters, the table will show multiple potential small molecules, which exhibit a dose-dependent effect.
The experimental data is plotted using black circles, whereas the blue line shows the calculated dose response curve. This information was mapped to canonical isoforms using UniProt.
Therefore, selection of any protein isoform displays the PPI network and functional annotations of the corresponding canonical isoform.
This reduces the complexity of the PPI network and focuses the attention on relations of interest. This generates a force-directed graph in the network window to the right.
Proteins and functional annotations are represented by circular and square nodes, respectively, while edges between nodes represent the relations between them.
Relations between two nodes without directionality information are merged into a single edge to further reduce redundancy in the graph.
At any point in time, the graph in the network window can be downloaded as a figure. Cytoscape An in-depth description to control the visualization can be found in Supplementary Text 5.
So far, all analyses focused on the exploration of data relating to a single protein. Currently, it offers four visualizations covering multi-protein expression pattern analysis, drug selection for single and combination treatments and the exploration of cell viability data.
The comparison of protein expression profiles across different tissues, fluids and cell lines can give rise to new hypotheses and puts protein expression into context.
While the expression tab of a single protein allows the analysis of expression patterns over multiple biological sources, it does not enable the analysis of multiple proteins simultaneously.
Proteins and biological sources are shown as rows and columns, respectively. The dendrograms show the result of hierarchically clustering proteins and biological sources, respectively.
Branches can be selected and either removed or used to perform GO-enrichment analyses proteins. Here, all beta-units of the proteasome are displayed, suggesting differential expression of the canonical expression of PSMB5, 6 and 7 and induced expression of PSMB8, 9 and 10 proteasome across tissues and cell lines.
The heatmap is fully interactive and provides multiple options to adjust and explore the data. Additional features of the heatmap are explained in Supplementary Text 6.
One topic of great scientific interest is finding the most selective and potent drug against a specific target of interest.
For this purpose, ProteomicsDB enables the interactive exploration of dose-dependent competition-binding data in a multi-protein-centric view Figure 5.
The pEC50 —log 10 EC 50 in nM distribution of all targets meeting the filter criteria for each drug showing a dose-dependent effect on the selected target are plotted in separate violin charts Figure 5B.
The red marker indicates the EC 50 of the selected protein for each drug. The selectivity of each compound can be evaluated by the numbers above and below the red marker, which depict the number of targets with higher or lower potency compared to the selected protein, respectively.
ProteomicsDB enables the exploration of drug selectivity data from various sources. A Starting with the selection of a target protein, the user can filter fitted selectivity curves using several criteria: the EC 50 range, the R 2 and BIC.
B Violin plots depicting the pEC 50 -log 10 EC 50 distributions for all compounds targeting the selected protein given the filter criteria from A.
Numbers above and below the red marker indicate the number of other target proteins with higher or lower potency, respectively.
At the time of writing, Bafetinib shows the most potent and selective inhibition of DDR1 with the given filters. C Bar chart displaying the distribution of pEC 50 values for Imatinib depicting all of its protein:drug interactions available in ProteomicsDB.
D The underlying raw data and the fitted model can be investigated on click on one of the bars black border. The scatter plot highlights the EC 50 for the selected protein:drug pair.
Users can inspect the pEC50 distribution of all targets for a given drug in an ordered bar chart by selecting the radio button underneath the corresponding violin plot.
This bar chart enables the investigation of all other targets of the selected drug, which could—depending on its use—increase the risk of unwanted side effects.
Individual dose—response plots can be investigated by selecting a specific drug:protein interaction in the bar chart.
The potency analysis provides an interface to select an inhibitor for a single protein of interest. However, in some applications, targeting multiple proteins can lead to a more effective treatment e.
This enables the selection of the most promising drug-combination to inhibit a set of proteins, while maintaining the lowest number of off-targets to decrease the chances of unwanted side-effects.
Two views are available, which show the predicted target profile of the selected drugs at a certain dose as i a protein—drug interaction graph and ii a table showing the predicted inhibition effects.
Both views are based on the dose-dependent models stored in ProteomicsDB. 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.
Users can save and use their session ID to load their session to any other computer or browser. Custom data analysis area of ProteomicsDB. The datasets are session-specific so that no other user has access to this uploaded data.
The first use case we highlight is the comparison of custom expression data to expression data stored in ProteomicsDB. For this to be successful, we highly recommend making use of the normalization feature available upon upload.
The uploaded expression profiles are normalized via MComBat 24 using the total sum normalized proteomics expression values of ProteomicsDB as a reference set.
Because MComBat normalization depends on the calculation of a mean and variance for any given protein, only datasets with three or more samples can be normalized using this method.
Every uploaded dataset has to adhere to a pre-defined comma-separated format. By uploading an expression dataset, back-end procedures take care of the data modelling and transformation, so that they are compatible to existing tools with no major differences to the data available in ProteomicsDB.
The first tool making use of this is the interactive expression heat map. The heat map allows interactive visualization of expression patterns of multiple groups of proteins.
Because the heat map automatically aggregates tissues, duplicated tissue names provided in the custom dataset will appear as one column.
They can further allow a comparison of some or all datasets that they have uploaded to the in-database data. Users should expect that uploaded datasets that were not subjected to normalization during uploading, will clustered together.
If the normalization step was enabled, then user samples should cluster with tissues or cell lines that have similar expression profiles in ProteomicsDB, ideally from the same origin.
Figure 4 shows such an example where a custom dataset was co-clustered with data stored in ProteomicsDB. Some of the uploaded expression profiles of cell lines co-cluster with the respective cell lines stored in ProteomicsDB here lung and liver samples.
There are cases though here ovary that cluster with other tissues here uterus. This feature enables users to find the closest cell lines for which ProteomicsDB contains, e.
ProteomicsDB stores a large collection of transcriptomics expression profiles alongside the respective proteomic profiles. Having access to expression data from both sources and to the automatic mapping using the built-in Resource Identifier Relation Model, ProteomicsDB is able to perform data-driven missing value imputation using either data type.
Especially proteomics data depending on the depth of measurement can show a large number of missing values. Data selected for imputation might come from different projects for both omics types.
Even projects of the same omics type might differ in the distribution of their expression values. We performed intra-omics normalization and batch effect correction using ComBat Next, we apply MComBat 24 to perform inter-omics correction of systematic differences.
MComBat, in contrast to ComBat, allows selecting a reference dataset so that all other datasets will be normalized based on the reference.
Transcriptomics data are then transferred to the same scale of the proteomics expression data. Previous experiments showed that the correlation across all tissues between mRNA and protein expression data is higher with than without such an adjustment Finally, we implemented the mRNA-guided missing value imputation method, described in For this purpose, we train linear regression models and extrapolate protein abundance from transcriptomics abundance.
To validate the performance of the generated models, we created artificial missing values in a random subset of the protein expression data that are stored in ProteomicsDB.
We then used our models to extrapolate the protein abundances and compared them to two other common missing value imputation strategies: a replacing missing values with the minimum protein abundance of the corresponding sample and b random sampling from the corresponding sample's protein abundance distribution, as the created missing values originate from the whole abundance distribution.
The mRNA-guided missing value imputation method showed the best correlation to the measured values Supplementary Figure S1 which is why we implemented it.
The entire procedure, from data normalization to training the regression model is performed by the R server Figure 1.
Missing value imputation is available in the interactive heat map Figure 5 and can be activated by the respective button. Once activated, and only if matching expression profiles are available, the model trained above and the adjusted transcriptomics expression data are used to fill in missing values in the protein expression matrix.
The authors point out that missing value imputation can lead to issues and should therefore be carefully considered and evaluated on a case by case basis.
Especially in the case of mRNA-guided missing value imputation, it becomes less accurate if the RNA dataset or protein expression data has a limited number of samples.
Moreover, not all missing values can be imputed if RNASeq matching data is missing. Combined interactive expression heat map.
User datasets can be clustered along with data stored in ProteomicsDB for a combined analysis. User datasets marked in orange that were normalized using MComBat subsequent to upload, cluster close to samples in ProteomicsDB in blue that were generated from the same or similar tissues or cell types.
However, the list of cell lines for which this data is available is necessarily incomplete and likely entirely unavailable or impossible to generate if cells lines were derived from say patient tissue in a particular laboratory.
In order to obtain an estimate of the susceptibility of such cell lines to drugs, without performing an experiment, ProteomicsDB provides a tool to model and estimate drug sensitivity, based on expression profiles.
Recent proteome profiling of the NCI60 28 and the CRC65 27 cancer cell line panels, and an additional panel of 20 breast cancer cell lines 29 showed that protein signatures can predict drug sensitivity or resistance.
On this basis, we implemented elastic net regression 30 in ProteomicsDB to model drug sensitivity as a function of quantitative protein expression profiles.
Here, users can select from a variety of tissues and cell lines whose proteomic profiles are stored in ProteomicsDB.
Next, a drug or compound can be selected to check for its effect on the selected cell line Figure 6A. Figure 6B shows the result of the prediction as bar plots - one for each predicted feature area under the curve, pEC50, relative effect.
Error bars show the range of the predictions of all bootstraps of the corresponding model. Each drug in ProteomicsDB might be accompanied by multiple models multiple bars in each bar plot , because the drug may have been used in more than one drug sensitivity screen which was imported into ProteomicsDB max.
It is important to point out that each model includes a certain set of predictor-proteins. If the sample on which a user wants to predict drug sensitivity does not contain some of the required proteins, prediction from some models is not possible.
Selecting a bar of any bar plot generates a volcano plot Figure 6C , which shows information for the interpretation of the trained model.
The x-axis shows how strong the expression of a particular protein is associated with drug sensitivity or resistance, analogous to a correlation.
The y-axis shows the number of bootstrap models contained the particular protein as a predictor, when training the elastic net model.
Proteins that appear in the top left and right areas of the volcano plot Figure 6C are frequently selected from the models as predictors, as they have a high positive or negative correlation with drug sensitivity or resistance and can, therefore, represent potential biomarkers.
Predictions can be applied to all user datasets, although it is highly recommended to use normalization upon uploading, as the models were trained on data stored in ProteomicsDB and expect values from the same or similar expression distributions.
Drug sensitivity prediction. A Prediction is enabled for both, data stored in ProteomicsDB or user uploaded datasets. B This view visualizes the predicted sensitivity of a chosen cell line to a chosen drug expressed by area under the curve AUC, left bar , the negative log of the effective concentration of the drug EC50, middle bars and the relative cell killing effect right bars.
If more than one bar is shown, more than one training data set was available for the particular drug and either one or several predictions are shown.
Each dot in the volcano plot, represents a protein that is associated to drug sensitivity or resistance on the basis of the elastic net model generated during training.
ProteomicsDB was initially developed for the exploration of the human proteome. As a result, every database view and endpoint was designed without explicit support for multiple organisms.
In order to support the storage, handling and visualization of data from multiple organisms, all layers of ProteomicsDB Figure 1 required modifications and extensive testing.
In the new version presented here, we modified all backend procedures to support querying of data for a specific taxonomy. The API endpoints were modified to require a taxcode in order to respond with the desired data.
With this functionality in place, we prepared the database and the data models to support and handle the protein sequence space of any organism.
Similarly, the user interface was modified to support the visualization of data from a selected organism.
Users can change the selected organisms by using the respective icons on the left hand side of each view, or directly on the front page of ProteomicsDB Figure 2A.
For the protein expression visualization, new interactive body maps for Arabidopsis thaliana and Mus musculus were generated Figure 7A , Supplementary Figure S2 and function in the same way as the human body map.
ProteomicsDB as a multi-organism and multi-omics platform. A Proteome or transcriptome expression data are visualized in the tissues of a chosen organism left and numerical expression data medians in case multiple samples of the same tissue are available are shown on the right for each tissue the protein was found in.
Tissue bars selected by users turn orange and the respective tissue is highlighted on the body map on the left view projects the tissue aggregated omics expression values to the corresponding organism's body map.
B Venn diagram is showing the overlap of gene-level data available for proteomics and transcriptomics for Arabidopsis thaliana. C Venn diagram showing the overlap of tissues for which proteomics and transcriptomics expression values are available in ProteomicsDB.
To bring Arabidopsis thaliana into ProteomicsDB, we downloaded, processed and imported the protein sequence space from UniProt, following the same mechanism as for human proteins.
Upon import, appropriate decoy sequences were created for every protease, to allow false discovery FDR estimation by the picked FDR approach already implemented in ProteomicsDB We furthermore imported the Plant Ontology PO 32 to be able to make use of ontologies for the different plant tissues.
This step was not necessary for Mus musculus , since the Brenda Tissue Ontology BTO 33 that was previously imported into ProteomicsDB to support the analysis of human proteins covers any mammalian tissue.
The latter data were processed and transformed for import into our triple-store data model, which allows the automatic mapping of the respective STRING and KEGG identifiers to the corresponding UniProt accessions and our internal protein identifiers.
With the meta-data imported, the proteomics and transcriptomics expression profiles for Arabidopsis thaliana were imported.
The project covers 30 different tissues, including a tissue-derived cell line that was derived from callus tissue. Because of the generic design of ProteomicsDB, any analytical view e.
However, due to the limited datasets available for phenotypic drug responses and the respective drug targets , other views do not show any A. As a next step, we imported more than 10 million Prosit-predicted peptide spectra, in three different charge states and 3 different collision energies.
By chance, these spectra also represent 70 peptides from Arabidopsis thaliana because their sequences are identical in either organism.
In addition, we added predicted spectra for all peptides present in the experimental data set. Thus, akin to the human case, these reference spectra can be used to validate peptide identifications in experimental data using the mirror spectrum viewer integrated in ProteomicsDB.
Since ProteomicsDB contains up to 14 different types of reference spectra 11 fragmentation settings from ProteomeTools and 3 normalized collision energies from Prosit as indicated in the list of available reference spectra, users can select the optimal match The two separate views exist because for some proteins, no experimental spectra of endogenous proteins might be available, while many reference spectra might be available because the ProteomeTools synthesized all meaningful peptides for a hitherto unobserved protein.
For proteins where experimental data from endogenous proteins is available, users can take experimental proteotypicity of peptides into account and thus rationalize which peptide to choose for an assay.
Additionally, this view can be used to compare spectra created by different fragmentation methods and, more importantly, different collision energies to optimize their targeted assays for collision energies which generate desired fragment ions e.
Furthermore, spectra can now be downloaded in the mirrored spectrum viewer as msp-files. Finally, as mentioned above, ProteomicsDB is also ready to support Mus musculus data.
However, the selection of mouse in ProteomicsDB will only be enabled once the data has been published.
The continuous updates introduced over the last years have transformed ProteomicsDB into a multi-omics resource for life science research covering proteomic and transcriptomic expression, pathway, protein-protein and protein-drug interactions, and cell viability data Supplementary Figure S3.
For example, e. One particular strength of ProteomicsDB is its versatile mapping service allowing the seamless connection between different data types.
This enables subsequent modelling and data mining to further evolve ProteomicsDB from an information database to a knowledge platform.
Along these lines, we plan to extend our analytical toolbox such that scientists in life science research can directly benefit from the wealth of data stored in ProteomicsDB.
Here, we show the first steps into this direction by extending the toolbox as well as enabling users to upload their own expression data.
For this purpose, we are also planning to further extend the data content of ProteomicsDB to include, e. Two more extensions are planned that will allow the further integration and exploitation of reference spectra.
The first one is to use synthetic or predicted reference spectra to systematically validate and assess the confidence of experimental data by evaluating their spectral similarity.
As shown earlier, the integration of intensity information can lead to drastic improvements in either the number of identified peptides or the ability to differentiate correct from incorrect matches 5.
Especially the latter will help to increase the confidence of each peptide identification and thus also increase the quality of identification and quantification results stored in ProteomicsDB.
The second extension is the implementation of a smart tool which will allow users to build targeted assays based on data stored in ProteomicsDB as described.
Ultimately, the collected data and generated knowledge should culminate in actionable hypotheses. These may drive the design of laboratory experiments or eventually aid decision making in patient care.
One way how ProteomicsDB could be used for the latter is by providing tools that assist molecular tumor boards. We plan to provide pipelines where researchers and clinicians will be able to upload the protein profiles of patient samples in a fully anonymized fashion and have in-depth bioinformatic analysis reports returned, spiked with a wide range of information including, e.
The authors wish to thank all members of the Kuster laboratory for fruitful discussions and technical assistance. Conflict of interest statement.
They have no operational role in the company. Neither company affiliation had any influence on the results presented in this study.
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