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Proteomicsdb: a Multi-Omics and Multi-Organism Resource for Life Science Published online 30 October 2019 Nucleic Acids Research, 2020, Vol. 48, Database issue D1153–D1163 doi: 10.1093/nar/gkz974 ProteomicsDB: a multi-omics and multi-organism resource for life science research Patroklos Samaras 1, Tobias Schmidt 1,MartinFrejno1, Siegfried Gessulat1,2, Maria Reinecke1,3,4, Anna Jarzab1, Jana Zecha1, Julia Mergner1, Piero Giansanti1, Hans-Christian Ehrlich2, Stephan Aiche2, Johannes Rank5,6, Harald Kienegger5,6, Helmut Krcmar5,6, Bernhard Kuster1,7,* and Mathias Wilhelm1,* 1Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), Freising, Bavaria, Germany, 2Innovation Center Network, SAP SE, Potsdam, Germany, 3German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany, 4German Cancer Research Center (DKFZ), Heidelberg, Germany, 5Chair for Information Systems, Technical University of Munich (TUM), Garching, Germany, 6SAP University Competence Center, Technical University of Munich (TUM), Garching, Germany and 7Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University of Munich (TUM), Freising, Bavaria, Germany Received September 14, 2019; Revised October 11, 2019; Editorial Decision October 11, 2019; Accepted October 15, 2019 ABSTRACT INTRODUCTION ProteomicsDB (https://www.ProteomicsDB.org) ProteomicsDB (https://www.ProteomicsDB.org) is an in- started as a protein-centric in-memory database for memory database initially developed for the exploration of the exploration of large collections of quantitative large quantities of quantitative human mass spectrometry- mass spectrometry-based proteomics data. The based proteomics data including the first draft of the hu- data types and contents grew over time to include man proteome (1). Among many features, it allows the real- time exploration and retrieval of protein abundance values RNA-Seq expression data, drug-target interactions across different tissues, cell lines, and body fluids via inter- and cell line viability data. In this manuscript, we active expression heat maps and body maps. Today, Pro- summarize new developments since the previous teomicsDB supports multiple use cases across different dis- update that was published in Nucleic Acids Research ciplines and covering a wide range of data (2). For instance, in 2017. Over the past two years, we have enriched tandem mass spectra, peptide identifications and peptide the data content by additional datasets and extended proteotypicity values can be used as starting points to de- the platform to support protein turnover data. An- velop targeted mass spectrometry assays. Because of the re- other important new addition is that ProteomicsDB cent incorporation of a large amount of reference spectra now supports the storage and visualization of data from the ProteomeTools project (3,4) as well as spectra pre- collected from other organisms, exemplified by dicted by the artificial intelligence Prosit (5), both experi- Arabidopsis thaliana. Due to the generic design of mental and reference spectra can be used for assay devel- opment and to validate the identification of so far unob- ProteomicsDB, all analytical features available for served, or in fact any proteins. The integration of pheno- the original human resource seamlessly transfer typic data allows the exploration of the dose-dependent ef- to other organisms. Furthermore, we introduce a fect of drugs of interest (e.g. clinically approved drugs) on new service in ProteomicsDB which allows users to multiple cell lines (6–9). The dynamic identifier mapping upload their own expression datasets and analyze in ProteomicsDB allows the integration of transcriptomics them alongside with data stored in ProteomicsDB. data from e.g. the Human Protein Atlas project (10)and Initially, users will be able to make use of this feature Bgee (11), and thus facilitates the automated integration of in the interactive heat map functionality as well as different data sources within ProteomicsDB. This, in turn, the drug sensitivity prediction, but ultimately will be allows the development of new tools. A wide range of drug- able to use all analytical features of ProteomicsDB target interaction data can be visualized in ProteomicsDB in this way. as well, which enables the exploration of combination treat- ments in a dose-dependent protein-drug interaction graph in-silico. *To whom correspondence should be addressed. Tel: +49 8161 71 4202; Fax: +49 8161 71 5931; Email: [email protected] Correspondence may also be addressed to Bernhard Kuster. Email: [email protected] C The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] D1154 Nucleic Acids Research, 2020, Vol. 48, Database issue ProteomicsDB is becoming an increasingly valuable re- Prosit. The right section includes links that trigger the se- source in (proteomic) life science research, evidenced by lection of the corresponding organism. To make organism the increasing number of external resources linking to Pro- selection available throughout the web interface, we addi- teomicsDB, such as UniProt (12) and GeneCards (13), as tionally adjusted the left sidebar to show one icon per avail- well as resources making use of our application program- able organism. The ‘Feedback’ button that that was previ- ming interface (API) to show e.g. protein expression infor- ously located in that position was transferred to the right mation, as done by OmniPathDB (14) and Gene Info eX- pane below the ‘Help’ button. In light of these changes, all tension (GIX) (15). internal procedures and endpoints (e.g. API) were adjusted In this version, we expanded the data content of Pro- to support the new data types and organisms. teomicsDB by including additional publically available as Figure 2B depicts the data expansion in ProteomicsDB well as in-house generated proteomic and transcriptomic since 2017, grouped by categories. By re-analyzing and up- studies. Furthermore, we expanded the drug-target interac- loading more publically available proteomics studies, we in- tion data now covering ∼1500 kinase inhibitors and tool creased the tissue coverage of ProteomicsDB by ∼70 hu- compounds. The cell line viability data were enriched with man tissues and cell lines (+∼30%), to a total of almost an additional large dataset (16) now covering >20 000 300 tissues and cell lines. The broader coverage of biolog- drugs against 1500 cell lines. We further increased the ical systems has direct impact on visualizations like the hu- amount of protein property information that is stored in man body map or expression heat map. The plethora of data ProteomicsDB, such as 13 000 melting points of proteins in ProteomicsDB allows not only the further online explo- obtained by thermal proteome profiling (17). In addition, ration of the proteome and its properties but also enables we expanded the biochemical assays section to include pro- the development of new tools integrating different omics tein turnover data with synthesis and degradation curves for data sources. Currently, human proteomics and transcrip- >6000 proteins. We further increased the number of refer- tomics data are available for ∼17 000 genes and ∼60 tissues ence tandem mass spectra in ProteomicsDB to >5 million (Figure 2C, D). This large overlap enabled the implemen- from synthetic peptides and 40 million from predictions, tation of a new missing value imputation approach which which, in total, are represented by 3 billion fragment ions. makes use of transcriptomics or proteomics data to esti- mate the presence and abundance of protein or RNA not covered in individual data sets. For ∼13 000 proteins, ad- RESULTS ditional information derived from other biochemical assays such as melting behavior or synthesis or degradation curves Overview are available. By integrating additional publicly available ProteomicsDB aims to provide real-time analytical func- datasets, the overlap at the tissue- and protein level will in- tions to users, including computationally challenging tasks. crease further over the next years and eventually cover all For this purpose, ProteomicsDB was carefully designed and the >1000 (cancer) cell lines for which we already have cell organized (Figure 1). It consists of a production unit, a viability data. This, in turn, will aid the development of a computing unit, and a storage unit, all intra-connected via better understanding of the molecular factors that govern a 16Gbit local network. The production unit hosts the pro- the life of a particular cell. duction server as well as the entire development and test- ing environment. The computing unit is one machine with a New biochemical assay data, covering more protein properties fully dockerized environment which currently handles two main tasks. First, an R server that handles R-procedures In addition to importing additional expression profile from ProteomicsDB such as the clustering available in the datasets, we further extended our biochemical assay por- heat map. Second, a docker container with various services tal by integrating the results of three additional studies handling requests to our deep learning tool Prosit which is covering target information of small molecule kinase
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