@article{Bouyssié2023, author = {Bouyssié, David and Altıner, Pınar and Capella-Gutierrez, Salvador and Fernández, José M. and Hagemeijer, Yanick Paco and Horvatovich, Peter and Hubálek, Martin and Levander, Fredrik and Mauri, Pierluigi and Palmblad, Magnus and Raffelsberger, Wolfgang and Rodríguez-Navas, Laura and Di Silvestre, Dario and Kunkli, Balázs Tibor and Uszkoreit, Julian and Vandenbrouck, Yves and Vizcaíno, Juan Antonio and Winkelhardt, Dirk and Schwämmle, Veit}, doi = {10.1021/acs.jproteome.3c00636}, journal = {Journal of Proteome Research}, month = {dec}, pages = {418-429}, title = {WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows}, url = {https://oadoi.org/10.1021/acs.jproteome.3c00636}, volume = {23}, year = {2023} } @article{Claeys2023, abstract = {AbstractPublic proteomics data often lack essential metadata, limiting its potential. To address this, we present lesSDRF, a tool to simplify the process of metadata annotation, thereby ensuring that data leave a lasting, impactful legacy well beyond its initial publication.}, author = {Claeys, Tine and Van Den Bossche, Tim and Perez-Riverol, Yasset and Gevaert, Kris and Vizcaíno, Juan Antonio and Martens, Lennart}, doi = {10.1038/s41467-023-42543-5}, journal = {Nature Communications}, month = {oct}, title = {lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation}, url = {https://doi.org/10.1038/s41467-023-42543-5}, volume = {14}, year = {2023} } @article{Dai2021, abstract = {AbstractThe amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.}, author = {Dai, Chengxin and Füllgrabe, Anja and Pfeuffer, Julianus and Solovyeva, Elizaveta M. and Deng, Jingwen and Moreno, Pablo and Kamatchinathan, Selvakumar and Kundu, Deepti Jaiswal and George, Nancy and Fexova, Silvie and Grüning, Björn and Föll, Melanie Christine and Griss, Johannes and Vaudel, Marc and Audain, Enrique and Locard-Paulet, Marie and Turewicz, Michael and Eisenacher, Martin and Uszkoreit, Julian and Van Den Bossche, Tim and Schwämmle, Veit and Webel, Henry and Schulze, Stefan and Bouyssié, David and Jayaram, Savita and Duggineni, Vinay Kumar and Samaras, Patroklos and Wilhelm, Mathias and Choi, Meena and Wang, Mingxun and Kohlbacher, Oliver and Brazma, Alvis and Papatheodorou, Irene and Bandeira, Nuno and Deutsch, Eric W. and Vizcaíno, Juan Antonio and Bai, Mingze and Sachsenberg, Timo and Levitsky, Lev I. and Perez-Riverol, Yasset}, doi = {10.1038/s41467-021-26111-3}, journal = {Nature Communications}, month = {oct}, title = {A proteomics sample metadata representation for multiomics integration and big data analysis}, url = {https://doi.org/10.1038/s41467-021-26111-3}, volume = {12}, year = {2021} } @article{Deutsch2018, author = {Deutsch, Eric W. and Perez-Riverol, Yasset and Chalkley, Robert J. and Wilhelm, Mathias and Tate, Stephen and Sachsenberg, Timo and Walzer, Mathias and Käll, Lukas and Delanghe, Bernard and Böcker, Sebastian and Schymanski, Emma L. and Wilmes, Paul and Dorfer, Viktoria and Kuster, Bernhard and Volders, Pieter-Jan and Jehmlich, Nico and Vissers, Johannes P. C. and Wolan, Dennis W. and Wang, Ana Y. and Mendoza, Luis and Shofstahl, Jim and Dowsey, Andrew W. and Griss, Johannes and Salek, Reza M. and Neumann, Steffen and Binz, Pierre-Alain and Lam, Henry and Vizcaíno, Juan Antonio and Bandeira, Nuno and Röst, Hannes}, doi = {10.1021/acs.jproteome.8b00485}, journal = {Journal of Proteome Research}, month = {oct}, pages = {4051-4060}, title = {Expanding the Use of Spectral Libraries in Proteomics}, url = {https://oadoi.org/10.1021/acs.jproteome.8b00485}, volume = {17}, year = {2018} } @article{Deutsch2021, author = {Deutsch, Eric W. and Perez-Riverol, Yasset and Carver, Jeremy and Kawano, Shin and Mendoza, Luis and Van Den Bossche, Tim and Gabriels, Ralf and Binz, Pierre-Alain and Pullman, Benjamin and Sun, Zhi and Shofstahl, Jim and Bittremieux, Wout and Mak, Tytus D. and Klein, Joshua and Zhu, Yunping and Lam, Henry and Vizcaíno, Juan Antonio and Bandeira, Nuno}, doi = {10.1038/s41592-021-01184-6}, journal = {Nature Methods}, month = {jun}, pages = {768-770}, title = {Universal Spectrum Identifier for mass spectra}, url = {http://www.nature.com/articles/s41592-021-01184-6.pdf}, volume = {18}, year = {2021} } @article{Garin-Muga2016, author = {Garin-Muga, Alba and Odriozola, Leticia and Martínez-Val, Ana and del Toro, Noemí and Martínez, Rocío and Molina, Manuela and Cantero, Laura and Rivera, Rocío and Garrido, Nicolás and Dominguez, Francisco and Sanchez del Pino, Manuel M. and Corrales, Fernando J. and Vizcaíno, Juan Antonio and Segura, Victor}, doi = {10.1021/acs.jproteome.6b00437}, journal = {Journal of Proteome Research}, month = {sep}, pages = {4101-4115}, title = {Detection of Missing Proteins Using the PRIDE Database as a Source of Mass Spectrometry Evidence.}, url = {https://doi.org/10.1021/acs.jproteome.6b00437}, volume = {15}, year = {2016} } @article{Ja2015, author = {Ja, Vizcaíno and Vizcaíno, Juan Antonio and Csordas, Attila and del-Toro, Noemi and Del Toro, N. and Dianes, José A. and Ja, Dianes and Griss, Johannes and Lavidas, Ilias and Mayer, Gerhard and Perez-Riverol, Yasset and Reisinger, Florian and Ternent, Tobias and Xu, Qing-Wei and Qw, Xu and Wang, Rui and Hermjakob, Henning}, doi = {10.1093/nar/gkw880}, journal = {Nucleic Acids Research}, month = {nov}, pages = {D447-D456}, title = {2016 update of the PRIDE database and its related tools}, url = {https://doi.org/10.1093/nar/gkv1145}, volume = {44}, year = {2015} } @article{LeDuc2022, author = {LeDuc, Richard D. and Deutsch, Eric W. and Binz, Pierre-Alain and Fellers, Ryan T. and Cesnik, Anthony J. and Klein, Joshua A. and Van Den Bossche, Tim and Gabriels, Ralf and Yalavarthi, Arshika and Perez-Riverol, Yasset and Carver, Jeremy and Bittremieux, Wout and Kawano, Shin and Pullman, Benjamin and Bandeira, Nuno and Kelleher, Neil L. and Thomas, Paul M. and Vizcaíno, Juan Antonio}, doi = {10.1021/acs.jproteome.1c00771}, journal = {Journal of Proteome Research}, month = {mar}, pages = {1189-1195}, title = {Proteomics Standards Initiative’s ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms}, url = {https://oadoi.org/10.1021/acs.jproteome.1c00771}, volume = {21}, year = {2022} } @article{Li2015, abstract = {ABSTRACT Candida infection has emerged as a critical health care burden worldwide, owing to the formation of robust biofilms against common antifungals. Recent evidence shows that multidrug-tolerant persisters critically account for biofilm recalcitrance, but their underlying biological mechanisms are poorly understood. Here, we first investigated the phenotypic characteristics of Candida biofilm persisters under consecutive harsh treatments of amphotericin B. The prolonged treatments effectively killed the majority of the cells of biofilms derived from representative strains of Candida albicans , Candida glabrata , and Candida tropicalis but failed to eradicate a small fraction of persisters. Next, we explored the tolerance mechanisms of the persisters through an investigation of the proteomic profiles of C. albicans biofilm persister fractions by liquid chromatography-tandem mass spectrometry. The C. albicans biofilm persisters displayed a specific proteomic signature, with an array of 205 differentially expressed proteins. The crucial enzymes involved in glycolysis, the tricarboxylic acid cycle, and protein synthesis were markedly downregulated, indicating that major metabolic activities are subdued in the persisters. It is noteworthy that certain metabolic pathways, such as the glyoxylate cycle, were able to be activated with significantly increased levels of isocitrate lyase and malate synthase. Moreover, a number of important proteins responsible for Candida growth, virulence, and the stress response were greatly upregulated. Interestingly, the persisters were tolerant to oxidative stress, despite highly induced intracellular superoxide. The current findings suggest that delicate metabolic control and a coordinated stress response may play a crucial role in mediating the survival and antifungal tolerance of Candida biofilm persisters. }, author = {Li, Peng and Seneviratne, Chaminda J. and Alpi, Emanuele and Vizcaino, Juan A. and Emanuele, A. and Vizacino, Ja and Jin, Lijian}, doi = {10.1128/aac.00543-15}, journal = {Antimicrobial Agents and Chemotherapy}, month = {jul}, pages = {6101-6112}, title = {Delicate Metabolic Control and Coordinated Stress Response Critically Determine Antifungal Tolerance of Candida albicans Biofilm Persisters}, url = {https://journals.asm.org/doi/pdf/10.1128/AAC.00543-15}, volume = {59}, year = {2015} } @article{Martens2017, author = {Martens, Lennart and Vizcaíno, Juan Antonio}, doi = {10.1016/j.tibs.2017.01.001}, journal = {Trends in Biochemical Sciences}, month = {jan}, pages = {333-341}, title = {A Golden Age for Working with Public Proteomics Data}, url = {https://oadoi.org/10.1016/j.tibs.2017.01.001}, volume = {42}, year = {2017} } @article{Moreno2021, abstract = {Abstract The EMBL-EBI Expression Atlas is an added value knowledge base that enables researchers to answer the question of where (tissue, organism part, developmental stage, cell type) and under which conditions (disease, treatment, gender, etc) a gene or protein of interest is expressed. Expression Atlas brings together data from >4500 expression studies from >65 different species, across different conditions and tissues. It makes these data freely available in an easy to visualise form, after expert curation to accurately represent the intended experimental design, re-analysed via standardised pipelines that rely on open-source community developed tools. Each study's metadata are annotated using ontologies. The data are re-analyzed with the aim of reproducing the original conclusions of the underlying experiments. Expression Atlas is currently divided into Bulk Expression Atlas and Single Cell Expression Atlas. Expression Atlas contains data from differential studies (microarray and bulk RNA-Seq) and baseline studies (bulk RNA-Seq and proteomics), whereas Single Cell Expression Atlas is currently dedicated to Single Cell RNA-Sequencing (scRNA-Seq) studies. The resource has been in continuous development since 2009 and it is available at https://www.ebi.ac.uk/gxa.}, author = {Moreno, Pablo and Fexova, Silvie and George, Nancy and Manning, Jonathan R. and Miao, Zhichiao and Mohammed, Suhaib and Muñoz-Pomer, Alfonso and Fullgrabe, Anja and Bi, Yalan and Bush, Natassja and Iqbal, Haider and Kumbham, Upendra and Solovyev, Andrey and Zhao, Lingyun and Prakash, Ananth and García-Seisdedos, David and Kundu, Deepti J and Wang, Shengbo and Walzer, Mathias and Clarke, Laura and Osumi-Sutherland, David and Tello-Ruiz, Marcela Karey and Kumari, Sunita and Ware, Doreen and Eliasova, Jana and Arends, Mark J and Nawijn, Martijn C and Meyer, Kerstin and Burdett, Tony and Marioni, John and Teichmann, Sarah and Vizcaíno, Juan Antonio and Brazma, Alvis and Papatheodorou, Irene}, doi = {10.1093/nar/gkab1030}, journal = {Nucleic Acids Research}, month = {nov}, pages = {D129-D140}, title = {Expression Atlas update: gene and protein expression in multiple species}, url = {https://doi.org/10.1093/nar/gkab1030}, volume = {50}, year = {2021} } @article{Neely2023, author = {Neely, Benjamin A. and Dorfer, Viktoria and Martens, Lennart and Bludau, Isabell and Bouwmeester, Robbin and Degroeve, Sven and Deutsch, Eric W. and Gessulat, Siegfried and Käll, Lukas and Palczynski, Pawel and Payne, Samuel H. and Rehfeldt, Tobias Greisager and Schmidt, Tobias and Schwämmle, Veit and Uszkoreit, Julian and Vizcaíno, Juan Antonio and Wilhelm, Mathias and Palmblad, Magnus}, doi = {10.1021/acs.jproteome.2c00711}, journal = {Journal of Proteome Research}, month = {feb}, pages = {681-696}, title = {Toward an Integrated Machine Learning Model of a Proteomics Experiment}, url = {https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.2c00711}, volume = {22}, year = {2023} } @article{Ochoa2019, author = {Ochoa, David and Jarnuczak, Andrew F. and Gehre, Maja and Viéitez, Cristina and Soucheray, Margaret and Mateus, André and Kleefeldt, Askar A. and Hill, Anthony and Garcia-Alonso, Luz and Stein, Frank and Krogan, Nevan J. and Savitski, Mikhail M. and Swaney, Danielle L. and Vizcaíno, Juan Antonio and Noh, Kyung-Min and Beltrao, Pedro}, doi = {10.1038/s41587-019-0344-3}, journal = {Nature Biotechnology}, month = {dec}, pages = {365-373}, title = {The functional landscape of the human phosphoproteome}, url = {http://www.nature.com/articles/s41587-019-0344-3.pdf}, volume = {38}, year = {2019} } @misc{Perez-Riverol2016, author = {Perez-Riverol, Yasset and Bai, Mingze and Leprevost, Felipe and Squizzato, Silvano and Haug, Ove Kenneth and Carroll, Adam J. and Mi Park, Young and Ok, Haug and Spalding, Dylan and Paschall, Justin and Aj, Carroll and Buso, Nicola and Wang, Mingxun and Bandeira, Nuno and del-Toro, Noemi and Deutsch, Eric and Campbell, David S. and Ternent, Tobias and Beavis, Ronald C. and Zhang, Peng and Salek, Reza and Nesvizhskii, Alexey and Sansone, Susanna-Assunta and Steinbeck, Christoph and Lopez, Rodrigo and Vizcaíno, Juan Antonio and Ping, Peipei and Hermjakob, Henning}, month = {apr}, title = {Omics Discovery Index - Discovering and Linking Public Omics Datasets}, year = {2016} } @article{Perez-Riverol2016_2, author = {Perez-Riverol, Yasset and Griss, Johannes and Lewis, Steve and Tabb, David L. and del-Toro, Noemi and Dianes, José A. and Walzer, Mathias and Rurik, Marc and Kohlbacher, Oliver and Hermjakob, Henning and Wang, Rui and Vizcaíno, Juan Antonio}, doi = {10.1038/nmeth.3902}, journal = {Nature Methods}, month = {jun}, pages = {651-656}, title = {Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets.}, url = {http://europepmc.org/articles/pmc4968634?pdf=render}, volume = {13}, year = {2016} } @article{Perez-Riverol2017, author = {Perez-Riverol, Yasset and Bai, Mingze and da Veiga Leprevost, Felipe and Squizzato, Silvano and Park, Young Mi and Haug, Kenneth and Carroll, Adam J. and Spalding, Dylan and Paschall, Justin and Wang, Mingxun and del-Toro, Noemi and Ternent, Tobias and Zhang, Peng and Buso, Nicola and Bandeira, Nuno and Deutsch, Eric W. and Campbell, David S. and Beavis, Ronald C. and Salek, Reza M. and Sarkans, Ugis and Petryszak, Robert and Keays, Maria and Fahy, Eoin and Sud, Manish and Subramaniam, Shankar and Barbera, Ariana and Jiménez, Rafael C. and Nesvizhskii, Alexey I. and Sansone, Susanna-Assunta and Steinbeck, Christoph and Lopez, Rodrigo and Vizcaíno, Juan A. and Ping, Peipei and Hermjakob, Henning}, doi = {10.1038/nbt.3790}, journal = {Nature Biotechnology}, month = {may}, pages = {406-409}, title = {Discovering and linking public omics data sets using the Omics Discovery Index}, url = {http://europepmc.org/articles/pmc5831141?pdf=render}, volume = {35}, year = {2017} } @article{Salek2015, author = {Salek, Reza M. and Arita, Masanori and Dayalan, Saravanan and Ebbels, Timothy and Jones, Andrew R. and Neumann, Steffen and Rocca-Serra, Philippe and Viant, Mark R. and Vizcaíno, Juan-Antonio}, doi = {10.1007/s11306-015-0821-8}, journal = {Metabolomics}, month = {jun}, pages = {782-783}, title = {Embedding standards in metabolomics: the Metabolomics Society data standards task group}, url = {https://link.springer.com/content/pdf/10.1007%2Fs11306-015-0821-8.pdf}, volume = {11}, year = {2015} } @article{Shao2017, author = {Shao, Wenguang and Pedrioli, Patrick G. A. and Wolski, Witold and Scurtescu, Cristian and Schmid, Emanuel and Vizcaíno, Juan A. and Courcelles, Mathieu and Schuster, Heiko and Kowalewski, Daniel and Marino, Fabio and Arlehamn, Cecilia S. L. L. and Vaughan, Kerrie and Peters, Bjoern and Sette, Alessandro and Ottenhoff, Tom H. M. M. and Meijgaarden, Krista E. and Nieuwenhuizen, Natalie and Kaufmann, Stefan H. E. E. and Schlapbach, Ralph and Castle, John C. and Nesvizhskii, Alexey I. and Nielsen, Morten and Deutsch, Eric W. and Campbell, David S. and Moritz, Robert L. and Zubarev, Roman A. and Ytterberg, Anders Jimmy and Purcell, Anthony W. and Marcilla, Miguel and Paradela, Alberto and Wang, Qi and Costello, Catherine E. and Ternette, Nicola and van Veelen, Peter A. and van Els, Cécile A. C. M. C. M. and Heck, Albert J. R. R. and de Souza, Gustavo A. and Sollid, Ludvig M. and Admon, Arie and Stevanovic, Stefan and Rammensee, Hans-Georg and Thibault, Pierre and Perreault, Claude and Bassani-Sternberg, Michal and Aebersold, Ruedi and Caron, Etienne}, doi = {10.1093/nar/gkx664}, journal = {Nucleic Acids Research}, month = {jul}, pages = {D1237-D1247}, title = {The SysteMHC Atlas project}, url = {https://doi.org/10.1093/nar/gkx664}, volume = {46}, year = {2017} } @article{Vaudel2015, abstract = {In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available mass spectrometry-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data. This article is protected by copyright. All rights reserved.}, author = {Vaudel, Marc and Verheggen, Kenneth and Csordas, Attila and Raeder, Helge and Berven, Frode S. and Martens, Lennart and Vizcaíno, Juan A. and Barsnes, Harald}, doi = {10.1002/pmic.201500295}, journal = {Proteomics}, month = {oct}, pages = {214-225}, title = {Exploring the potential of public proteomics data}, url = {https://doi.org/10.1002/pmic.201500295}, volume = {16}, year = {2015} } @article{Wang2015, author = {Wang, R. and Qw, Xu and Perez-Riverol, Yasset and Uszkoreit, J. and Griss, J. and Sanchez, A. and Reisinger, F. and Csordas, A. and Ternent, T. and Del Toro, N. and Ja, Dianes and Eisenacher, M. and Hermjakob, H. and Ja, Vizcaino and Vizcaíno, Juan Antonio}, month = {nov}, title = {PRIDE Inspector Toolsuite: moving towards a universal visualization tool for proteomics data standard formats and quality assessment of ProteomeXchange datasets.}, year = {2015} }