Publications – Proteomics Edit Proteomics Managing and reusing public proteomics data Home Members Publications Collaborations Contact Publications Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues. Wang S, García-Seisdedos D, Prakash A, Kundu DJ, Collins A, George N, Fexova S, Moreno P, Papatheodorou I, Jones AR, Vizcaíno JA. PLoS computational biology 2022 doi:10.1371/journal.pcbi.1010174. Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas. Walzer M, García-Seisdedos D, Prakash A, Brack P, Crowther P, Graham RL, George N, Mohammed S, Moreno P, Papatheodorou I, Hubbard SJ, Vizcaíno JA. Scientific data 2022 doi:10.1038/s41597-022-01380-9. Method for Independent Estimation of the False Localization Rate for Phosphoproteomics. Ramsbottom KA, Prakash A, Riverol YP, Camacho OM, Martin MJ, Vizcaíno JA, Deutsch EW, Jones AR. Journal of proteome research 2022 doi:10.1021/acs.jproteome.1c00827. A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics. Luo X, Bittremieux W, Griss J, Deutsch EW, Sachsenberg T, Levitsky LI, Ivanov MV, Bubis JA, Gabriels R, Webel H, Sanchez A, Bai M, Käll L, Perez-Riverol Y. Journal of proteome research 2022 doi:10.1021/acs.jproteome.2c00069. An interactive mass spectrometry atlas of histone posttranslational modifications in T-cell acute leukemia Provez L, Van Puyvelde B, Corveleyn L, Demeulemeester N, Verhelst S, Lintermans B, Daled S, Roels J, Clement L, Martens L, Deforce D, Van Vlierberghe P, Dhaenens M. 2022 doi:10.1101/2022.05.05.490796. A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. Van Puyvelde B, Daled S, Willems S, Gabriels R, Gonzalez de Peredo A, Chaoui K, Mouton-Barbosa E, Bouyssié D, Boonen K, Hughes CJ, Gethings LA, Perez-Riverol Y, Bloomfield N, Tate S, Schiltz O, Martens L, Deforce D, Dhaenens M. Scientific data 2022 doi:10.1038/s41597-022-01216-6. Proteomics Standards Initiative's ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms. LeDuc RD, Deutsch EW, Binz PA, Fellers RT, Cesnik AJ, Klein JA, Van Den Bossche T, Gabriels R, Yalavarthi A, Perez-Riverol Y, Carver J, Bittremieux W, Kawano S, Pullman B, Bandeira N, Kelleher NL, Thomas PM, Vizcaíno JA. Journal of proteome research 2022 doi:10.1021/acs.jproteome.1c00771. A panoramic perspective on human phosphosites Ramasamy P, Vandermarliere E, vranken W, Martens L. 2022 doi:10.1101/2022.03.08.483252. A comprehensive evaluation of consensus spectrum generation methods in proteomics Luo X, Bittremieux W, Griss J, Deutsch EW, Sachsenberg T, Levitsky LI, Ivanov MV, Bubis JA, Gabriels R, Webel H, Sanchez A, Bai M, Kall L, Perez-Riverol Y. 2022 doi:10.1101/2022.01.25.477699. Expression Atlas update: gene and protein expression in multiple species. Moreno P, Fexova S, George N, Manning JR, Miao Z, Mohammed S, Muñoz-Pomer A, Fullgrabe A, Bi Y, Bush N, Iqbal H, Kumbham U, Solovyev A, Zhao L, Prakash A, García-Seisdedos D, Kundu DJ, Wang S, Walzer M, Clarke L, Osumi-Sutherland D, Tello-Ruiz MK, Kumari S, Ware D, Eliasova J, Arends MJ, Nawijn MC, Meyer K, Burdett T, Marioni J, Teichmann S, Vizcaíno JA, Brazma A, Papatheodorou I. Nucleic acids research 2022 doi:10.1093/nar/gkab1030. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S, Kamatchinathan S, Kundu DJ, Prakash A, Frericks-Zipper A, Eisenacher M, Walzer M, Wang S, Brazma A, Vizcaíno JA. Nucleic acids research 2022 doi:10.1093/nar/gkab1038. Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues Wang S, García-Seisdedos D, Prakash A, Kundu DJ, Collins A, George N, Fexova S, Moreno P, Papatheodorou I, Jones AR, Vizcaíno JA. 2021 doi:10.1101/2021.12.20.473413. Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides. Umer HM, Audain E, Zhu Y, Pfeuffer J, Sachsenberg T, Lehtiö J, Branca R, Perez-Riverol Y. Bioinformatics (Oxford, England) 2021 doi:10.1093/bioinformatics/btab838. Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database. Almeida N, Rodriguez J, Pla Parada I, Perez-Riverol Y, Woldmar N, Kim Y, Oskolas H, Betancourt L, Valdés JG, Sahlin KB, Pizzatti L, Szasz AM, Kárpáti S, Appelqvist R, Malm J, B Domont G, C S Nogueira F, Marko-Varga G, Sanchez A. Cancers 2021 doi:10.3390/cancers13246224. A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics Van Puyvelde B, Daled S, Willems S, Gabriels R, de Peredo AG, Chaoui K, Mouton-Barbosa E, Bouyssié D, Boonen K, Hughes CJ, Gethings LA, Perez-Riverol Y, Bloomfield N, Tate S, Schiltz O, Martens L, Deforce D, Dhaenens M. 2021 doi:10.1101/2021.11.24.469852. MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates Declercq A, Bouwmeester R, Hirschler A, Carapito C, Degroeve S, Degroeve S, Martens L, Gabriels R. 2021 doi:10.1101/2021.11.02.466886. A method for independent estimation of false localisation rate for phosphoproteomics Ramsbottom KA, Prakash A, Riverol YP, Camacho OM, Martin M, Vizcaíno JA, Deutsch EW, Jones AR. 2021 doi:10.1101/2021.10.18.464791. A proteomics sample metadata representation for multiomics integration and big data analysis. Dai C, Füllgrabe A, Pfeuffer J, Solovyeva EM, Deng J, Moreno P, Kamatchinathan S, Kundu DJ, George N, Fexova S, Grüning B, Föll MC, Griss J, Vaudel M, Audain E, Locard-Paulet M, Turewicz M, Eisenacher M, Uszkoreit J, Van Den Bossche T, Schwämmle V, Webel H, Schulze S, Bouyssié D, Jayaram S, Duggineni VK, Samaras P, Wilhelm M, Choi M, Wang M, Kohlbacher O, Brazma A, Papatheodorou I, Bandeira N, Deutsch EW, Vizcaíno JA, Bai M, Sachsenberg T, Levitsky LI, Perez-Riverol Y. Nature communications 2021 doi:10.1038/s41467-021-26111-3. The growing need for controlled data access models in clinical proteomics and metabolomics. Keane TM, O'Donovan C, Vizcaíno JA. Nature communications 2021 doi:10.1038/s41467-021-26110-4. Correction: Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease. Audain E, Wilsdon A, Breckpot J, Izarzugaza JMG, Fitzgerald TW, Kahlert AK, Sifrim A, Wünnemann F, Perez-Riverol Y, Abdul-Khaliq H, Bak M, Bassett AS, Benson DW, Berger F, Daehnert I, Devriendt K, Dittrich S, Daubeney PE, Garg V, Hackmann K, Hoff K, Hofmann P, Dombrowsky G, Pickardt T, Bauer U, Keavney BD, Klaassen S, Kramer HH, Marshall CR, Milewicz DM, Lemaire S, Coselli JS, Mitchell ME, Tomita-Mitchell A, Prakash SK, Stamm K, Stewart AFR, Silversides CK, Siebert R, Stiller B, Rosenfeld JA, Vater I, Postma AV, Caliebe A, Brook JD, Andelfinger G, Hurles ME, Thienpont B, Larsen LA, Hitz MP. PLoS genetics 2021 doi:10.1371/journal.pgen.1009809. An integrated view of baseline protein expression in human tissues Prakash A, García-Seisdedos D, Wang S, Kundu DJ, Collins A, George N, Moreno P, Papatheodorou I, Jones AR, Vizcaíno JA. 2021 doi:10.1101/2021.09.10.459811. Pout2Prot: an efficient tool to create protein (sub)groups from Percolator output files Schallert K, Verschaffelt P, Mesuere B, Benndorf D, Martens L, Bossche TVD. 2021 doi:10.1101/2021.08.11.455803. Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease. Audain E, Wilsdon A, Breckpot J, Izarzugaza JMG, Fitzgerald TW, Kahlert AK, Sifrim A, Wünnemann F, Perez-Riverol Y, Abdul-Khaliq H, Bak M, Bassett AS, Benson DW, Berger F, Daehnert I, Devriendt K, Dittrich S, Daubeney PE, Garg V, Hackmann K, Hoff K, Hofmann P, Dombrowsky G, Pickardt T, Bauer U, Keavney BD, Klaassen S, Kramer HH, Marshall CR, Milewicz DM, Lemaire S, Coselli JS, Mitchell ME, Tomita-Mitchell A, Prakash SK, Stamm K, Stewart AFR, Silversides CK, Siebert R, Stiller B, Rosenfeld JA, Vater I, Postma AV, Caliebe A, Brook JD, Andelfinger G, Hurles ME, Thienpont B, Larsen LA, Hitz MP. PLoS genetics 2021 doi:10.1371/journal.pgen.1009679. MaxDIA enables library-based and library-free data-independent acquisition proteomics. Sinitcyn P, Hamzeiy H, Salinas Soto F, Itzhak D, McCarthy F, Wichmann C, Steger M, Ohmayer U, Distler U, Kaspar-Schoenefeld S, Prianichnikov N, Yılmaz Ş, Rudolph JD, Tenzer S, Perez-Riverol Y, Nagaraj N, Humphrey SJ, Cox J. Nature biotechnology 2021 doi:10.1038/s41587-021-00968-7. ionbot: a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification Degroeve S, Gabriels R, Velghe K, Bouwmeester R, Tichshenko N, Martens L. 2021 doi:10.1101/2021.07.02.450686. Universal Spectrum Identifier for mass spectra. Deutsch EW, Perez-Riverol Y, Carver J, Kawano S, Mendoza L, Van Den Bossche T, Gabriels R, Binz PA, Pullman B, Sun Z, Shofstahl J, Bittremieux W, Mak TD, Klein J, Zhu Y, Lam H, Vizcaíno JA, Bandeira N. Nature methods 2021 doi:10.1038/s41592-021-01184-6. Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides Umer HM, Zhu Y, Pfeuffer J, Sachsenberg T, Lehtiö J, Branca R, Perez-Riverol Y. 2021 doi:10.1101/2021.06.08.447496. Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas Walzer M, García-Seisdedos D, Prakash A, Brack P, Crowther P, Graham RL, George N, Mohammed S, Moreno P, Papathedourou I, Hubbard SJ, Vizcaíno JA. 2021 doi:10.1101/2021.06.08.447493. A proteomics sample metadata representation for multiomics integration, and big data analysis Dai C, Füllgrabe A, Pfeuffer J, Solovyeva E, Deng J, Moreno P, Kamatchinathan S, Kundu DJ, George N, Fexova S, Grüning B, Föll MC, Griss J, Vaudel M, Audain E, Locard-Paulet M, Turewicz M, Eisenacher M, Uszkoreit J, Van Den Bossche T, Schwämmle V, Webel H, Schulze S, Bouyssié D, Jayaram S, Duggineni VK, Samaras P, Wilhelm M, Choi M, Wang M, Kohlbacher O, Brazma A, Papatheodorou I, Bandeira N, Deutsch EW, Vizcaíno JA, Bai M, Sachsenberg T, Levitsky L, Perez-Riverol Y. 2021 doi:10.1101/2021.05.21.445143. Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison. Schmidt T, Samaras P, Dorfer V, Panse C, Kockmann T, Bichmann L, van Puyvelde B, Perez-Riverol Y, Deutsch EW, Kuster B, Wilhelm M. Journal of proteome research 2021 doi:10.1021/acs.jproteome.1c00096. An integrated landscape of protein expression in human cancer. Jarnuczak AF, Najgebauer H, Barzine M, Kundu DJ, Ghavidel F, Perez-Riverol Y, Papatheodorou I, Brazma A, Vizcaíno JA. Scientific data 2021 doi:10.1038/s41597-021-00890-2. Sensitive and specific spectral library searching with COSS and Percolator Shiferaw GA, Gabriels R, Bouwmeester R, Van Den Bossche T, Van Den Bossche T, Vandermarliere E, Martens L, Volders P. 2021 doi:10.1101/2021.04.09.438700. User-friendly, scalable tools and workflows for single-cell RNA-seq analysis. Moreno P, Huang N, Manning JR, Mohammed S, Solovyev A, Polanski K, Bacon W, Chazarra R, Talavera-López C, Doyle MA, Marnier G, Grüning B, Rasche H, George N, Fexova SK, Alibi M, Miao Z, Perez-Riverol Y, Haeussler M, Brazma A, Teichmann S, Meyer KB, Papatheodorou I. Nature methods 2021 doi:10.1038/s41592-021-01102-w. DeepLC can predict retention times for peptides that carry as-yet unseen modifications Martens L, Bouwmeester R, Gabriels R, Hulstaert N, Degroeve S. 2021 doi:10.21203/rs.3.rs-275246/v1. Data Management of Sensitive Human Proteomics Data: Current Practices, Recommendations, and Perspectives for the Future. Bandeira N, Deutsch EW, Kohlbacher O, Martens L, Vizcaíno JA. Molecular & cellular proteomics : MCP 2021 doi:10.1016/j.mcpro.2021.100071. BioContainers Registry: Searching Bioinformatics and Proteomics Tools, Packages, and Containers. Bai J, Bandla C, Guo J, Vera Alvarez R, Bai M, Vizcaíno JA, Moreno P, Grüning B, Sallou O, Perez-Riverol Y. Journal of proteome research 2021 doi:10.1021/acs.jproteome.0c00904. The Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection Salz R, Bouwmeester R, Gabriels R, Degroeve S, Martens L, Volders P, ’t Hoen PA. 2020 doi:10.1101/2020.12.11.419523. Universal Spectrum Identifier for mass spectra Deutsch EW, Perez-Riverol Y, Carver J, Kawano S, Mendoza L, Van Den Bossche T, Gabriels R, Binz P, Pullman B, Sun Z, Shofstahl J, Bittremieux W, Mak TD, Klein J, Zhu Y, Lam H, Vizcaíno JA, Bandeira N. 2020 doi:10.1101/2020.12.07.415539. Deep learning embedder method and tool for mass spectra similarity search. Qin C, Luo X, Deng C, Shu K, Zhu W, Griss J, Hermjakob H, Bai M, Perez-Riverol Y. Journal of proteomics 2020 doi:10.1016/j.jprot.2020.104070. MegaGO: a fast yet powerful approach to assess functional similarity across meta-omics data sets Verschaffelt P, Van Den Bossche T, Gabriel W, Burdukiewicz M, Soggiu A, Martens L, Renard BY, Schiebenhoefer H, Mesuere B. 2020 doi:10.1101/2020.11.16.384834. Using Deep Learning to Extrapolate Protein Expression Measurements. Barzine MP, Freivalds K, Wright JC, Opmanis M, Rituma D, Ghavidel FZ, Jarnuczak AF, Celms E, Čerāns K, Jonassen I, Lace L, Vizcaíno JA, Choudhary JS, Brazma A, Viksna J. Proteomics 2020 doi:10.1002/pmic.202000009. OpenPepXL: An Open-Source Tool for Sensitive Identification of Cross-Linked Peptides in XL-MS. Netz E, Dijkstra TMH, Sachsenberg T, Zimmermann L, Walzer M, Monecke T, Ficner R, Dybkov O, Urlaub H, Kohlbacher O. Molecular & cellular proteomics : MCP 2020 doi:10.1074/mcp.tir120.002186. A high-stringency blueprint of the human proteome. Adhikari S, Nice EC, Deutsch EW, Lane L, Omenn GS, Pennington SR, Paik YK, Overall CM, Corrales FJ, Cristea IM, Van Eyk JE, Uhlén M, Lindskog C, Chan DW, Bairoch A, Waddington JC, Justice JL, LaBaer J, Rodriguez H, He F, Kostrzewa M, Ping P, Gundry RL, Stewart P, Srivastava S, Srivastava S, Nogueira FCS, Domont GB, Vandenbrouck Y, Lam MPY, Wennersten S, Vizcaino JA, Wilkins M, Schwenk JM, Lundberg E, Bandeira N, Marko-Varga G, Weintraub ST, Pineau C, Kusebauch U, Moritz RL, Ahn SB, Palmblad M, Snyder MP, Aebersold R, Baker MS. Nature communications 2020 doi:10.1038/s41467-020-19045-9. Toward Increased Reliability, Transparency, and Accessibility in Cross-linking Mass Spectrometry. Leitner A, Bonvin AMJJ, Borchers CH, Chalkley RJ, Chamot-Rooke J, Combe CW, Cox J, Dong MQ, Fischer L, Götze M, Gozzo FC, Heck AJR, Hoopmann MR, Huang L, Ishihama Y, Jones AR, Kalisman N, Kohlbacher O, Mechtler K, Moritz RL, Netz E, Novak P, Petrotchenko E, Sali A, Scheltema RA, Schmidt C, Schriemer D, Sinz A, Sobott F, Stengel F, Thalassinos K, Urlaub H, Viner R, Vizcaíno JA, Wilkins MR, Rappsilber J. Structure (London, England : 1993) 2020 doi:10.1016/j.str.2020.09.011. Toward a Sample Metadata Standard in Public Proteomics Repositories. Perez-Riverol Y, European Bioinformatics Community for Mass Spectrometry. Journal of proteome research 2020 doi:10.1021/acs.jproteome.0c00376. MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets. Choi M, Carver J, Chiva C, Tzouros M, Huang T, Tsai TH, Pullman B, Bernhardt OM, Hüttenhain R, Teo GC, Perez-Riverol Y, Muntel J, Müller M, Goetze S, Pavlou M, Verschueren E, Wollscheid B, Nesvizhskii AI, Reiter L, Dunkley T, Sabidó E, Bandeira N, Vitek O. Nature methods 2020 doi:10.1038/s41592-020-0955-0. Universal Spectrum Explorer: A standalone (web-)application for cross-resource spectrum comparison Schmidt T, Samaras P, Dorfer V, Panse C, Kockmann T, Bichmann L, van Puyvelde B, Perez-Riverol Y, Deutsch EW, Kuster B, Wilhelm M. 2020 doi:10.1101/2020.09.08.287557. BioContainers Registry: searching for bioinformatics tools, packages and containers Bai J, Bandla C, Guo J, Alvarez RV, Vizcaíno JA, Bai M, Moreno P, Grüning BA, Sallou O, Perez-Riverol Y. 2020 doi:10.1101/2020.07.21.187609. The omics discovery REST interface. Dass G, Vu MT, Xu P, Audain E, Hitz MP, Grüning BA, Hermjakob H, Perez-Riverol Y. Nucleic acids research 2020 doi:10.1093/nar/gkaa326. Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease Audain E, Wilsdon A, Breckpot J, Izarzugaza J, Fitzgerald T, Kahlert A, Sifrim A, Wünnemann F, Perez-Riverol Y, Abdul-Khaliq H, Bak M, Bassett A, Belmont J, Benson D, Berger F, Daehnert I, Devriendt K, Dittrich S, Daubeney P, Garg V, Hackmann K, Hoff K, Hofmann P, Dombrowsky G, Pickardt T, Bauer U, Keavney B, Klaassen S, Kramer H, Marshall C, Milewicz D, Lemaire S, Coselli J, Mitchell M, Tomita-Mitchell A, Prakash S, Stamm K, Stewart A, Silversides C, Siebert R, Stiller B, Rosenfeld J, Vater I, Postma A, Caliebe A, Brook J, Andelfinger G, Hurles M, Thienpont B, Larsen L, Hitz M. 2020 doi:10.1101/2020.06.25.169573. User-friendly, scalable tools and workflows for single-cell analysis Moreno P, Huang N, Manning J, Mohammed S, Solovyev A, Polanski K, Chazarra R, Talavera-Lopez C, Doyle M, Marnier G, Grüning B, Rasche H, Bacon W, Perez-Riverol Y, Haeussler M, Meyer K, Teichmann S, Papatheodorou I. 2020 doi:10.1101/2020.04.08.032698. Precursor intensity-based label-free quantification software tools for proteomic and multiomic analysis within the Galaxy Platform Mehta S, Easterly C, Sajulga R, Millikin RJ, Argentini A, Eguinoa I, Martens L, Shortreed MR, Smith LM, McGowan T, Kumar P, Johnson JE, Griffin TJ, Jagtap P. 2020 doi:10.1101/2020.04.01.003988. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences. Drysdale R, Cook CE, Petryszak R, Baillie-Gerritsen V, Barlow M, Gasteiger E, Gruhl F, Haas J, Lanfear J, Lopez R, Redaschi N, Stockinger H, Teixeira D, Venkatesan A, Elixir Core Data Resource Forum, Blomberg N, Durinx C, McEntyre J. Bioinformatics (Oxford, England) 2020 doi:10.1093/bioinformatics/btz959. DeepLC can predict retention times for peptides that carry as-yet unseen modifications Bouwmeester R, Gabriels R, Hulstaert N, Martens L, Degroeve S. 2020 doi:10.1101/2020.03.28.013003. Pitfalls in re-analysis of observational omics studies: a post-mortem of the human pathology atlas Gilis J, Taelman S, Davey L, Martens L, Clement L. 2020 doi:10.1101/2020.03.16.994038. The Omics Discovery REST interface Dass G, Vu M, Xu P, Audain E, Hitz M, Hermjakob H, Perez-Riverol Y. 2020 doi:10.1101/2020.02.10.939967. Generalized calibration across LC-setups for generic prediction of small molecule retention times Bouwmeester R, Martens L, Degroeve S. 2020 doi:10.1101/2020.01.14.905844. The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics. Deutsch EW, Bandeira N, Sharma V, Perez-Riverol Y, Carver JJ, Kundu DJ, García-Seisdedos D, Jarnuczak AF, Hewapathirana S, Pullman BS, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, Hermjakob H, MacLean B, MacCoss MJ, Zhu Y, Ishihama Y, Vizcaíno JA. Nucleic acids research 2020 doi:10.1093/nar/gkz984. Expression Atlas update: from tissues to single cells. Papatheodorou I, Moreno P, Manning J, Fuentes AM, George N, Fexova S, Fonseca NA, Füllgrabe A, Green M, Huang N, Huerta L, Iqbal H, Jianu M, Mohammed S, Zhao L, Jarnuczak AF, Jupp S, Marioni J, Meyer K, Petryszak R, Prada Medina CA, Talavera-López C, Teichmann S, Vizcaino JA, Brazma A. Nucleic acids research 2020 doi:10.1093/nar/gkz947. Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics. Walzer M, Vizcaíno JA. Methods in molecular biology (Clifton, N.J.) 2020 doi:10.1007/978-1-4939-9744-2_15. Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines. Perez-Riverol Y, Moreno P. Proteomics 2019 doi:10.1002/pmic.201900147. The functional landscape of the human phosphoproteome. Ochoa D, Jarnuczak AF, Viéitez C, Gehre M, Soucheray M, Mateus A, Kleefeldt AA, Hill A, Garcia-Alonso L, Stein F, Krogan NJ, Savitski MM, Swaney DL, Vizcaíno JA, Noh KM, Beltrao P. Nature biotechnology 2019 doi:10.1038/s41587-019-0344-3. ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion. Hulstaert N, Shofstahl J, Sachsenberg T, Walzer M, Barsnes H, Martens L, Perez-Riverol Y. Journal of proteome research 2019 doi:10.1021/acs.jproteome.9b00328. Phoenix Enhancer: proteomics data mining using clustered spectra Bai M, Qin C, Shu K, Griss J, Perez-Riverol Y, Zhu W, Hermjakob H. 2019 doi:10.1101/846303. The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases. Vizcaíno JA, Kubiniok P, Kovalchik KA, Ma Q, Duquette JD, Mongrain I, Deutsch EW, Peters B, Sette A, Sirois I, Caron E. Molecular & cellular proteomics : MCP 2019 doi:10.1074/mcp.r119.001743. Non-reversible tissue fixation retains extracellular vesicles for in situ imaging. Gupta MP, Tandalam S, Ostrager S, Lever AS, Fung AR, Hurley DD, Alegre GB, Espinal JE, Remmel HL, Mukherjee S, Levine BM, Robins RP, Molina H, Dill BD, Kenific CM, Tuschl T, Lyden D, D'Amico DJ, Pena JTG. Nature methods 2019 doi:10.1093/nar/gky1106. Sodium dodecyl sulfate free gel electrophoresis/electroelution sorting for peptide fractionation. Ramos Y, González A, Sosa-Acosta P, Perez-Riverol Y, García Y, Castellanos-Serra L, Gil J, Sánchez A, González LJ, Besada V. Journal of separation science 2019 doi:10.1002/jssc.201900495. Novel functional proteins coded by the human genome discovered in metastases of melanoma patients. Sanchez A, Kuras M, Murillo JR, Pla I, Pawlowski K, Szasz AM, Gil J, Nogueira FCS, Perez-Riverol Y, Eriksson J, Appelqvist R, Miliotis T, Kim Y, Baldetorp B, Ingvar C, Olsson H, Lundgren L, Ekedahl H, Horvatovich P, Sugihara Y, Welinder C, Wieslander E, Kwon HJ, Domont GB, Malm J, Rezeli M, Betancourt LH, Marko-Varga G. Cell biology and toxicology 2019 doi:10.1007/s10565-019-09494-4. A five-level classification system for proteoform identifications. Smith LM, Thomas PM, Shortreed MR, Schaffer LV, Fellers RT, LeDuc RD, Tucholski T, Ge Y, Agar JN, Anderson LC, Chamot-Rooke J, Gault J, Loo JA, Paša-Tolić L, Robinson CV, Schlüter H, Tsybin YO, Vilaseca M, Vizcaíno JA, Danis PO, Kelleher NL. Nature methods 2019 doi:10.1038/s41592-019-0573-x. MSqRob takes the missing hurdle: uniting intensity- and count-based proteomics Goeminne LJ, Sticker A, Martens L, Gevaert K, Clement L. 2019 doi:10.1101/782466. BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services Katayama T, Kawashima S, Micklem G, Kawano S, Kim J, Kocbek S, Okamoto S, Wang Y, Wu H, Yamaguchi A, Yamamoto Y, Antezana E, Aoki-Kinoshita KF, Arakawa K, Banno M, Baran J, Bolleman JT, Bonnal RJP, Bono H, Fernández-Breis JT, Buels R, Campbell MP, Chiba H, Cock PJA, Cohen KB, Dumontier M, Fujisawa T, Fujiwara T, Garcia L, Gaudet P, Hattori E, Hoehndorf R, Itaya K, Ito M, Jamieson D, Jupp S, Juty N, Kalderimis A, Kato F, Kawaji H, Kawashima T, Kinjo AR, Komiyama Y, Kotera M, Kushida T, Malone J, Matsubara M, Mizuno S, Mizutani S, Mori H, Moriya Y, Murakami K, Nakazato T, Nishide H, Nishimura Y, Ogishima S, Ohta T, Okuda S, Ono H, Perez-Riverol Y, Shinmachi D, Splendiani A, Strozzi F, Suzuki S, Takehara J, Thompson M, Tokimatsu T, Uchiyama I, Verspoor K, Wilkinson MD, Wimalaratne S, Yamada I, Yamamoto N, Yarimizu M, Kawamoto S, Takagi T. 2019 doi:10.12688/f1000research.18238.1. Quantifying the impact of public omics data. Perez-Riverol Y, Zorin A, Dass G, Vu MT, Xu P, Glont M, Vizcaíno JA, Jarnuczak AF, Petryszak R, Ping P, Hermjakob H. Nature communications 2019 doi:10.1038/s41467-019-11461-w. A simple approach for accurate peptide quantification in MS-based proteomics Maia TM, Staes A, Plasman K, Pauwels J, Boucher K, Argentini A, Martens L, Montoye T, Gevaert K, Impens F. 2019 doi:10.1101/703397. Removing the hidden data dependency of DIA with predicted spectral libraries Van Puyvelde B, Willems S, Gabriels R, Daled S, De Clerck L, Vande Casteele S, Staes A, Impens F, Deforce D, Martens L, Degroeve S, Dhaenens M. 2019 doi:10.1101/681429. Robust summarization and inference in proteome-wide label-free quantification Sticker A, Goeminne L, Martens L, Clement L. 2019 doi:10.1101/668863. An integrated landscape of protein expression in human cancer Jarnuczak AF, Najgebauer H, Barzine M, Kundu DJ, Ghavidel F, Perez-Riverol Y, Papatheodorou I, Brazma A, Vizcaíno JA. 2019 doi:10.1101/665968. Proteomics Standards Initiative Extended FASTA Format. Binz PA, Shofstahl J, Vizcaíno JA, Barsnes H, Chalkley RJ, Menschaert G, Alpi E, Clauser K, Eng JK, Lane L, Seymour SL, Sánchez LFH, Mayer G, Eisenacher M, Perez-Riverol Y, Kapp EA, Mendoza L, Baker PR, Collins A, Van Den Bossche T, Deutsch EW. Journal of proteome research 2019 doi:10.1021/acs.jproteome.9b00064. COSS: A fast and user-friendly tool for spectral library searching Shiferaw GA, Vandermarliere E, Hulstaert N, Gabriels R, Martens L, Volders P. 2019 doi:10.1101/640458. Proteomics Standards Initiative Extended FASTA Format (PEFF) Binz P, Shofstahl J, Vizcaíno JA, Barsnes H, Chalkley RJ, Menschaert G, Alpi E, Clauser K, Eng JK, Lane L, Seymour SL, Sánchez LFH, Mayer G, Eisenacher M, Perez-Riverol Y, Kapp EA, Mendoza L, Baker PR, Collins A, Van Den Bossche T, Deutsch EW. 2019 doi:10.1101/624494. Multi-omics discovery of exome-derived neoantigens in hepatocellular carcinoma. Löffler MW, Mohr C, Bichmann L, Freudenmann LK, Walzer M, Schroeder CM, Trautwein N, Hilke FJ, Zinser RS, Mühlenbruch L, Kowalewski DJ, Schuster H, Sturm M, Matthes J, Riess O, Czemmel S, Nahnsen S, Königsrainer I, Thiel K, Nadalin S, Beckert S, Bösmüller H, Fend F, Velic A, Maček B, Haen SP, Buonaguro L, Buonaguro L, Kohlbacher O, Stevanović S, Königsrainer A, HEPAVAC Consortium, Rammensee HG. Genome medicine 2019 doi:10.1186/s13073-019-0636-8. ThermoRawFileParser: modular, scalable and cross-platform RAW file conversion Hulstaert N, Sachsenberg T, Walzer M, Barsnes H, Martens L, Perez-Riverol Y. 2019 doi:10.1101/622852. Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines Perez-Riverol Y, Moreno P. 2019 doi:10.1101/604413. Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins. Griss J, Stanek F, Hudecz O, Dürnberger G, Perez-Riverol Y, Vizcaíno JA, Mechtler K. Journal of proteome research 2019 doi:10.1021/acs.jproteome.8b00377. Recommendations for the packaging and containerizing of bioinformatics software Gruening B, Sallou O, Moreno P, da Veiga Leprevost F, Ménager H, Søndergaard D, Röst H, Sachsenberg T, O'Connor B, Madeira F, Dominguez Del Angel V, Crusoe MR, Varma S, Blankenberg D, Jimenez RC, Perez-Riverol Y, BioContainers Community. 2019 doi:10.12688/f1000research.15140.2. mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics. Hoffmann N, Rein J, Sachsenberg T, Hartler J, Haug K, Mayer G, Alka O, Dayalan S, Pearce JTM, Rocca-Serra P, Qi D, Eisenacher M, Perez-Riverol Y, Vizcaíno JA, Salek RM, Neumann S, Jones AR. Analytical chemistry 2019 doi:10.1021/acs.analchem.8b04310. Updated MS2PIP web server delivers fast and accurate MS2 peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques Gabriels R, Martens L, Degroeve S. 2019 doi:10.1101/544965. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, Inuganti A, Griss J, Mayer G, Eisenacher M, Pérez E, Uszkoreit J, Pfeuffer J, Sachsenberg T, Yilmaz S, Tiwary S, Cox J, Audain E, Walzer M, Jarnuczak AF, Ternent T, Brazma A, Vizcaíno JA. Nucleic acids research 2019 doi:10.1093/nar/gky1106. Quantitative Proteomics Data in the Public Domain: Challenges and Opportunities. Jarnuczak AF, Ternent T, Vizcaíno JA. Methods in molecular biology (Clifton, N.J.) 2019 doi:10.1007/978-1-4939-9232-4_14. Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud Moreno P, Pireddu L, Roger P, Goonasekera N, Afgan E, van den Beek M, He S, Larsson A, Schober D, Ruttkies C, Johnson D, Rocca-Serra P, Weber RJ, Gruening B, Salek RM, Kale N, Perez-Riverol Y, Papatheodorou I, Spjuth O, Neumann S. 2018 doi:10.1101/488643. The Human RNA-Binding Proteome and Its Dynamics during Translational Arrest. Trendel J, Schwarzl T, Horos R, Prakash A, Bateman A, Hentze MW, Krijgsveld J. Cell 2018 doi:10.1016/j.cell.2018.11.004. Protein Inference Using PIA Workflows and PSI Standard File Formats. Uszkoreit J, Perez-Riverol Y, Eggers B, Marcus K, Eisenacher M. Journal of proteome research 2018 doi:10.1021/acs.jproteome.8b00723. A well-ordered nanoflow LC-MS/MS approach for proteome profiling using 200 cm long micro pillar array columns De Beeck JO, Pauwels J, Van Landuyt N, Jacobs P, De Malsche W, Desmet G, Argentini A, Staes A, Martens L, Impens F, Gevaert K. 2018 doi:10.1101/472134. Expanding the Use of Spectral Libraries in Proteomics. Deutsch EW, Perez-Riverol Y, Chalkley RJ, Wilhelm M, Tate S, Sachsenberg T, Walzer M, Käll L, Delanghe B, Böcker S, Schymanski EL, Wilmes P, Dorfer V, Kuster B, Volders PJ, Jehmlich N, Vissers JPC, Wolan DW, Wang AY, Mendoza L, Shofstahl J, Dowsey AW, Griss J, Salek RM, Neumann S, Binz PA, Lam H, Vizcaíno JA, Bandeira N, Röst H. Journal of proteome research 2018 doi:10.1021/acs.jproteome.8b00485. Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions C. Silva AS, Martens L, Degroeve S. 2018 doi:10.1101/428805. Protein inference using PIA workflows and PSI standard file formats Uszkoreit J, Perez-Riverol Y, Eggers B, Marcus K, Eisenacher M. 2018 doi:10.1101/424473. Mass spectrometry evaluation of a neuroblastoma SH-SY5Y cell culture protocol. Murillo JR, Pla I, Goto-Silva L, Nogueira FCS, Domont GB, Perez-Riverol Y, Sánchez A, Junqueira M. Analytical biochemistry 2018 doi:10.1016/j.ab.2018.08.013. Future Prospects of Spectral Clustering Approaches in Proteomics. Perez-Riverol Y, Vizcaíno JA, Griss J. Proteomics 2018 doi:10.1002/pmic.201700454. Bioconda: sustainable and comprehensive software distribution for the life sciences. Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J, Bioconda Team. Nature methods 2018 doi:10.1038/s41592-018-0046-7. ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data. Lee JY, Choi H, Colangelo CM, Davis D, Hoopmann MR, Käll L, Lam H, Payne SH, Perez-Riverol Y, The M, Wilson R, Weintraub ST, Palmblad M. Journal of biomolecular techniques : JBT 2018 doi:10.7171/jbt.18-2902-003. Recommendations for the packaging and containerizing of bioinformatics software. Gruening B, Sallou O, Moreno P, da Veiga Leprevost F, Ménager H, Søndergaard D, Röst H, Sachsenberg T, O'Connor B, Madeira F, Dominguez Del Angel V, Crusoe MR, Varma S, Blankenberg D, Jimenez RC, BioContainers Community, Perez-Riverol Y. F1000Research 2018 doi:10.12688/f1000research.15140.2. Minimal Information About an Immuno-Peptidomics Experiment (MIAIPE). Lill JR, van Veelen PA, Tenzer S, Admon A, Caron E, Elias JE, Heck AJR, Marcilla M, Marino F, Müller M, Peters B, Purcell A, Sette A, Sturm T, Ternette N, Vizcaíno JA, Bassani-Sternberg M. Proteomics 2018 doi:10.1002/pmic.201800110. Response to "Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra". Griss J, Perez-Riverol Y, The M, Käll L, Vizcaíno JA. Journal of proteome research 2018 doi:10.1021/acs.jproteome.7b00824. A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms. The M, Edfors F, Perez-Riverol Y, Payne SH, Hoopmann MR, Palmblad M, Forsström B, Käll L. Journal of proteome research 2018 doi:10.1021/acs.jproteome.7b00899. Quantifying the impact of public omics data Perez-Riverol Y, Zorin A, Dass G, Glont M, Vizcaíno JA, Jarnuczak AF, Petryszak R, Ping P, Hermjakob H. 2018 doi:10.1101/282517. ProForma: A Standard Proteoform Notation. LeDuc RD, Schwämmle V, Shortreed MR, Cesnik AJ, Solntsev SK, Shaw JB, Martin MJ, Vizcaino JA, Alpi E, Danis P, Kelleher NL, Smith LM, Ge Y, Agar JN, Chamot-Rooke J, Loo JA, Pasa-Tolic L, Tsybin YO. Journal of proteome research 2018 doi:10.1021/acs.jproteome.7b00851. Comprehensive and empirical evaluation of machine learning algorithms for LC retention time prediction Bouwmeester R, Martens L, Degroeve S. 2018 doi:10.1101/259168. The proBAM and proBed standard formats: enabling a seamless integration of genomics and proteomics data. Menschaert G, Wang X, Jones AR, Ghali F, Fenyö D, Olexiouk V, Zhang B, Deutsch EW, Ternent T, Vizcaíno JA. Genome biology 2018 doi:10.1186/s13059-017-1377-x. Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit Boone M, Ramasamy P, Zuallaert J, Bouwmeester R, Van Moer B, Maddelein D, Turan D, Hulstaert N, Eeckhaut H, Vandermarliere E, Martens L, Degroeve S, De Neve W, Vranken W, Callewaert N. 2018 doi:10.1101/241349. The SysteMHC Atlas project. Shao W, Pedrioli PGA, Wolski W, Scurtescu C, Schmid E, Vizcaíno JA, Courcelles M, Schuster H, Kowalewski D, Marino F, Arlehamn CSL, Vaughan K, Peters B, Sette A, Ottenhoff THM, Meijgaarden KE, Nieuwenhuizen N, Kaufmann SHE, Schlapbach R, Castle JC, Nesvizhskii AI, Nielsen M, Deutsch EW, Campbell DS, Moritz RL, Zubarev RA, Ytterberg AJ, Purcell AW, Marcilla M, Paradela A, Wang Q, Costello CE, Ternette N, van Veelen PA, van Els CACM, Heck AJR, de Souza GA, Sollid LM, Admon A, Stevanovic S, Rammensee HG, Thibault P, Perreault C, Bassani-Sternberg M, Aebersold R, Caron E. Nucleic acids research 2018 doi:10.1093/nar/gkx664. ProteomeXchange submissions via PRIDE Vizcaino JA, Ternent T, Sehra M, Csordas A. 2018 doi:10.6019/tol.pxd-t.2014.00001.1. Expression Atlas: gene and protein expression across multiple studies and organisms. Papatheodorou I, Fonseca NA, Keays M, Tang YA, Barrera E, Bazant W, Burke M, Füllgrabe A, Fuentes AM, George N, Huerta L, Koskinen S, Mohammed S, Geniza M, Preece J, Jaiswal P, Jarnuczak AF, Huber W, Stegle O, Vizcaino JA, Brazma A, Petryszak R. Nucleic acids research 2018 doi:10.1093/nar/gkx1158. Accurate and fast feature selection workflow for high-dimensional omics data. Perez-Riverol Y, Kuhn M, Vizcaíno JA, Hitz MP, Audain E. PloS one 2017 doi:10.1371/journal.pone.0189875. A protein standard that emulates homology for the characterization of protein inference algorithms The M, Edfors F, Perez-Riverol Y, Payne SH, Hoopmann MR, Palmblad M, Forsström B, Käll L. 2017 doi:10.1101/236471. Bioconda: A sustainable and comprehensive software distribution for the life sciences Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Caprez A, Batut B, Haudgaard M, Cokelaer T, Beauchamp KA, Pedersen BS, Hoogstrate Y, Bretaudeau A, Ryan D, Corguillé GL, Yusuf D, Luna-Valero S, Kirchner R, Brinda K, Wollmann T, Raden M, Heeringen SJv, Soranzo N, Pantano L, Charlop-Powers Z, Unneberg P, Smet MD, Martin M, Kuster GV, Antao T, Miladi M, Thornton K, Brueffer C, Beek Mvd, Maticzka D, Blank C, Will S, Gravouil K, Wolff J, Holtgrewe M, Fallmann J, Piro VC, Shlyakhter I, Yousif A, Mabon P, Zhang X, Shen W, Cabral J, Thomas C, Enns E, Brown J, Boekel J, Hollander Md, Kelleher J, Turaga N, Ruiter JRd, Bouvier D, Gladman S, Choudhary S, Harding N, Eggenhofer F, Kratz A, Fang Z, Kleinkauf R, Timm H, Cock PJA, Seiler E, Brislawn C, Nguyen H, Stovner EB, Ewels P, Chambers M, Johnson JE, Hägglund E, Ye S, Guimera RV, Pruesse E, Dunn WA, Parsons L, Patro R, Koppstein D, Grassi E, Wohlers I, Reynolds A, Cornwell M, Stoler N, Blankenberg D, He G, Bargull M, Junge A, Farouni R, Freeberg M, Singh S, Bogema DR, Cumbo F, Wang L, Larson DE, Workentine ML, Devisetty UK, Laurent S, Roger P, Garnier X, Agren R, Khan A, Eppley JM, Li W, Stöcker BK, Rausch T, Taylor J, Wright PR, Taranto AP, Chicco D, Sennblad B, Baaijens JA, Gopez M, Abdennur N, Milne I, Preussner J, Pinello L, Srivastava A, Chande AT, Kensche PR, Pirola Y, Knudsen M, Bruijn Id, Blin K, Gonnella G, Enache OM, Rai V, Waters NR, Hiltemann S, Bendall ML, Stahl C, Miles A, Boursin Y, Perez-Riverol Y, Schmeier S, Clarke E, Arvai K, Jung M, Domenico TD, Seiler J, Rasche E, Kornobis E, Beisser D, Rahmann S, Mikheyev AS, Tran C, Capellades J, Schröder C, Salatino AE, Dirmeier S, Webster TH, Moskalenko O, Stephen G, Köster J. 2017 doi:10.1101/207092. Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach. Guruceaga E, Garin-Muga A, Prieto G, Bejarano B, Marcilla M, Marín-Vicente C, Perez-Riverol Y, Casal JI, Vizcaíno JA, Corrales FJ, Segura V. Journal of proteome research 2017 doi:10.1021/acs.jproteome.7b00388. OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets. Perez-Riverol Y, Ternent T, Koch M, Barsnes H, Vrousgou O, Jupp S, Vizcaíno JA. Proteomics 2017 doi:10.1002/pmic.201700244. Proteomics Standards Initiative: Fifteen Years of Progress and Future Work. Deutsch EW, Orchard S, Binz PA, Bittremieux W, Eisenacher M, Hermjakob H, Kawano S, Lam H, Mayer G, Menschaert G, Perez-Riverol Y, Salek RM, Tabb DL, Tenzer S, Vizcaíno JA, Walzer M, Jones AR. Journal of proteome research 2017 doi:10.1021/acs.jproteome.7b00370. Using the PRIDE Database and ProteomeXchange for Submitting and Accessing Public Proteomics Datasets. Jarnuczak AF, Vizcaíno JA. Current protocols in bioinformatics 2017 doi:10.1002/cpbi.30. BioContainers: an open-source and community-driven framework for software standardization. da Veiga Leprevost F, Grüning BA, Alves Aflitos S, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Vera Alvarez R, Griss J, Nesvizhskii AI, Perez-Riverol Y. Bioinformatics (Oxford, England) 2017 doi:10.1093/bioinformatics/btx192. Four simple recommendations to encourage best practices in research software. Jiménez RC, Kuzak M, Alhamdoosh M, Barker M, Batut B, Borg M, Capella-Gutierrez S, Chue Hong N, Cook M, Corpas M, Flannery M, Garcia L, Gelpí JL, Gladman S, Goble C, González Ferreiro M, Gonzalez-Beltran A, Griffin PC, Grüning B, Hagberg J, Holub P, Hooft R, Ison J, Katz DS, Leskošek B, López Gómez F, Oliveira LJ, Mellor D, Mosbergen R, Mulder N, Perez-Riverol Y, Pergl R, Pichler H, Pope B, Sanz F, Schneider MV, Stodden V, Suchecki R, Svobodová Vařeková R, Talvik HA, Todorov I, Treloar A, Tyagi S, van Gompel M, Vaughan D, Via A, Wang X, Watson-Haigh NS, Crouch S. F1000Research 2017 doi:10.12688/f1000research.11407.1. A community proposal to integrate proteomics activities in ELIXIR. Vizcaíno JA, Walzer M, Jiménez RC, Bittremieux W, Bouyssié D, Carapito C, Corrales F, Ferro M, Heck AJR, Horvatovich P, Hubalek M, Lane L, Laukens K, Levander F, Lisacek F, Novak P, Palmblad M, Piovesan D, Pühler A, Schwämmle V, Valkenborg D, van Rijswijk M, Vondrasek J, Eisenacher M, Martens L, Kohlbacher O. F1000Research 2017 doi:10.12688/f1000research.11751.1. A community proposal to integrate proteomics activities in ELIXIR Vizcaíno JA, Walzer M, Jiménez RC, Bittremieux W, Bouyssié D, Carapito C, Corrales F, Ferro M, Heck AJ, Horvatovich P, Hubalek M, Lane L, Laukens K, Levander F, Lisacek F, Novak P, Palmblad M, Piovesan D, Pühler A, Schwämmle V, Valkenborg D, van Rijswijk M, Vondrasek J, Eisenacher M, Martens L, Kohlbacher O. 2017 doi:10.12688/f1000research.11751.1. Accurate and Fast feature selection workflow for high-dimensional omics data Perez-Riverol Y, Kun M, Vizcaíno JA, Hitz M, Audain E. 2017 doi:10.1101/144162. OpenMS - A platform for reproducible analysis of mass spectrometry data. Pfeuffer J, Sachsenberg T, Alka O, Walzer M, Fillbrunn A, Nilse L, Schilling O, Reinert K, Kohlbacher O. Journal of biotechnology 2017 doi:10.1016/j.jbiotec.2017.05.016. The mzIdentML Data Standard Version 1.2, Supporting Advances in Proteome Informatics. Vizcaíno JA, Mayer G, Perkins S, Barsnes H, Vaudel M, Perez-Riverol Y, Ternent T, Uszkoreit J, Eisenacher M, Fischer L, Rappsilber J, Netz E, Walzer M, Kohlbacher O, Leitner A, Chalkley RJ, Ghali F, Martínez-Bartolomé S, Deutsch EW, Jones AR. Molecular & cellular proteomics : MCP 2017 doi:10.1074/mcp.m117.068429. Discovering and linking public omics data sets using the Omics Discovery Index. Perez-Riverol Y, Bai M, da Veiga Leprevost F, Squizzato S, Park YM, Haug K, Carroll AJ, Spalding D, Paschall J, Wang M, Del-Toro N, Ternent T, Zhang P, Buso N, Bandeira N, Deutsch EW, Campbell DS, Beavis RC, Salek RM, Sarkans U, Petryszak R, Keays M, Fahy E, Sud M, Subramaniam S, Barbera A, Jiménez RC, Nesvizhskii AI, Sansone SA, Steinbeck C, Lopez R, Vizcaíno JA, Ping P, Hermjakob H. Nature biotechnology 2017 doi:10.1038/nbt.3790. Synthetic human proteomes for accelerating protein research. Perez-Riverol Y, Vizcaíno JA. Nature methods 2017 doi:10.1038/nmeth.4191. A Golden Age for Working with Public Proteomics Data. Martens L, Vizcaíno JA. Trends in biochemical sciences 2017 doi:10.1016/j.tibs.2017.01.001. Omics Discovery Index - Discovering and Linking Public Omics Datasets Perez-Riverol Y, Bai M, Leprevost F, Squizzato S, Park YM, et al. 2016 doi:10.1101/049205. Erratum to "Personalized peptide vaccine-induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient". Löffler MW, Chandran PA, Laske K, Schroeder C, Bonzheim I, Walzer M, Hilke FJ, Trautwein N, Kowalewski DJ, Schuster H, Günder M, Carcamo Yañez VA, Mohr C, Sturm M, Nguyen HP, Riess O, Bauer P, Nahnsen S, Nadalin S, Zieker D, Glatzle J, Thiel K, Schneiderhan-Marra N, Clasen S, Bösmüller H, Fend F, Kohlbacher O, Gouttefangeas C, Stevanović S, Königsrainer A, Rammensee HG. Journal of hepatology 2016 doi:10.1016/j.jhep.2016.10.021. Erratum to: Making sense of big data in health research: towards an EU action plan. Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, Bernal-Delgado E, Blomberg N, Bock C, Conesa A, Del Signore S, Delogne C, Devilee P, Di Meglio A, Eijkemans M, Flicek P, Graf N, Grimm V, Guchelaar HJ, Guo YK, Gut IG, Hanbury A, Hanif S, Hilgers RD, Honrado Á, Hose DR, Houwing-Duistermaat J, Hubbard T, Janacek SH, Karanikas H, Kievits T, Kohler M, Kremer A, Lanfear J, Lengauer T, Maes E, Meert T, Müller W, Nickel D, Oledzki P, Pedersen B, Petkovic M, Pliakos K, Rattray M, I Màs JR, Schneider R, Sengstag T, Serra-Picamal X, Spek W, Vaas LA, van Batenburg O, Vandelaer M, Varnai P, Villoslada P, Vizcaíno JA, Wubbe JP, Zanetti G. Genome medicine 2016 doi:10.1186/s13073-016-0376-y. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Deutsch EW, Csordas A, Sun Z, Jarnuczak A, Perez-Riverol Y, Ternent T, Campbell DS, Bernal-Llinares M, Okuda S, Kawano S, Moritz RL, Carver JJ, Wang M, Ishihama Y, Bandeira N, Hermjakob H, Vizcaíno JA. Nucleic acids research 2016 doi:10.1093/nar/gkw936. A multicenter study benchmarks software tools for label-free proteome quantification. Navarro P, Kuharev J, Gillet LC, Bernhardt OM, MacLean B, Röst HL, Tate SA, Tsou CC, Reiter L, Distler U, Rosenberger G, Perez-Riverol Y, Nesvizhskii AI, Aebersold R, Tenzer S. Nature biotechnology 2016 doi:10.1038/nbt.3685. 2016 update of the PRIDE database and its related tools. Vizcaíno JA, Csordas A, Del-Toro N, Dianes JA, Griss J, Lavidas I, Mayer G, Perez-Riverol Y, Reisinger F, Ternent T, Xu QW, Wang R, Hermjakob H. Nucleic acids research 2016 doi:10.1093/nar/gkw880. Detection of Missing Proteins Using the PRIDE Database as a Source of Mass Spectrometry Evidence. Garin-Muga A, Odriozola L, Martínez-Val A, Del Toro N, Martínez R, Molina M, Cantero L, Rivera R, Garrido N, Dominguez F, Sanchez Del Pino MM, Vizcaíno JA, Corrales FJ, Segura V. Journal of proteome research 2016 doi:10.1021/acs.jproteome.6b00437. In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics. Audain E, Uszkoreit J, Sachsenberg T, Pfeuffer J, Liang X, Hermjakob H, Sanchez A, Eisenacher M, Reinert K, Tabb DL, Kohlbacher O, Perez-Riverol Y. Journal of proteomics 2016 doi:10.1016/j.jprot.2016.08.002. Ten Simple Rules for Taking Advantage of Git and GitHub. Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, Leprevost Fda V, Fufezan C, Ternent T, Eglen SJ, Katz DS, Pollard TJ, Konovalov A, Flight RM, Blin K, Vizcaíno JA. PLoS computational biology 2016 doi:10.1371/journal.pcbi.1004947. Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets. Griss J, Perez-Riverol Y, Lewis S, Tabb DL, Dianes JA, Del-Toro N, Rurik M, Walzer MW, Kohlbacher O, Hermjakob H, Wang R, Vizcaíno JA. Nature methods 2016 doi:10.1038/nmeth.3902. Making sense of big data in health research: Towards an EU action plan. Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, Bernal-Delgado E, Blomberg N, Bock C, Conesa A, Del Signore S, Delogne C, Devilee P, Di Meglio A, Eijkemans M, Flicek P, Graf N, Grimm V, Guchelaar HJ, Guo YK, Gut IG, Hanbury A, Hanif S, Hilgers RD, Honrado Á, Hose DR, Houwing-Duistermaat J, Hubbard T, Janacek SH, Karanikas H, Kievits T, Kohler M, Kremer A, Lanfear J, Lengauer T, Maes E, Meert T, Müller W, Nickel D, Oledzki P, Pedersen B, Petkovic M, Pliakos K, Rattray M, I Màs JR, Schneider R, Sengstag T, Serra-Picamal X, Spek W, Vaas LA, van Batenburg O, Vandelaer M, Varnai P, Villoslada P, Vizcaíno JA, Wubbe JP, Zanetti G. Genome medicine 2016 doi:10.1186/s13073-016-0323-y. Ten Simple Rules for Taking Advantage of git and GitHub Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, Veiga Leprevost Fd, Fufezan C, Ternent T, Eglen SJ, Katz DS, Pollard TJ, Konovalov A, Flight RM, Blin K, Vizcaino JA. 2016 doi:10.1101/048744. Exploring the potential of public proteomics data. Vaudel M, Verheggen K, Csordas A, Raeder H, Berven FS, Martens L, Vizcaíno JA, Barsnes H. Proteomics 2015 doi:10.1002/pmic.201500295. Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences. Audain E, Ramos Y, Hermjakob H, Flower DR, Perez-Riverol Y. Bioinformatics (Oxford, England) 2015 doi:10.1093/bioinformatics/btv674. PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets. Perez-Riverol Y, Xu QW, Wang R, Uszkoreit J, Griss J, Sanchez A, Reisinger F, Csordas A, Ternent T, Del-Toro N, Dianes JA, Eisenacher M, Hermjakob H, Vizcaíno JA. Molecular & cellular proteomics : MCP 2015 doi:10.1074/mcp.o115.050229. 2016 update of the PRIDE database and its related tools. Vizcaíno JA, Csordas A, del-Toro N, Dianes JA, Griss J, Lavidas I, Mayer G, Perez-Riverol Y, Reisinger F, Ternent T, Xu QW, Wang R, Hermjakob H. Nucleic acids research 2015 doi:10.1093/nar/gkv1145. Computational proteomics: Integrating mass spectral data into a biological context. Carvalho PC, Padron G, Calvete JJ, Perez-Riverol Y. Journal of proteomics 2015 doi:10.1016/j.jprot.2015.10.013. Delicate Metabolic Control and Coordinated Stress Response Critically Determine Antifungal Tolerance of Candida albicans Biofilm Persisters. Li P, Seneviratne CJ, Alpi E, Vizcaino JA, Jin L. Antimicrobial agents and chemotherapy 2015 doi:10.1128/aac.00543-15. Data for comparative proteomics analysis of the antitumor effect of CIGB-552 peptide in HT-29 colon adenocarcinoma cells. Núñez de Villavicencio-Díaz T, Ramos Gómez Y, Oliva Argüelles B, Fernández Masso JR, Rodríguez-Ulloa A, Cruz García Y, Guirola-Cruz O, Perez-Riverol Y, Javier González L, Tiscornia I, Victoria S, Bollati-Fogolín M, Besada Pérez V, Guerra Vallespi M. Data in brief 2015 doi:10.1016/j.dib.2015.06.024. Comparative proteomics analysis of the antitumor effect of CIGB-552 peptide in HT-29 colon adenocarcinoma cells. Núñez de Villavicencio-Díaz T, Ramos Gómez Y, Oliva Argüelles B, Fernández Masso JR, Rodríguez-Ulloa A, Cruz García Y, Guirola-Cruz O, Perez-Riverol Y, Javier González L, Tiscornia I, Victoria S, Bollati-Fogolín M, Besada Pérez V, Guerra Vallespi M. Journal of proteomics 2015 doi:10.1016/j.jprot.2015.05.024. ms-data-core-api: an open-source, metadata-oriented library for computational proteomics. Perez-Riverol Y, Uszkoreit J, Sanchez A, Ternent T, Del Toro N, Hermjakob H, Vizcaíno JA, Wang R. Bioinformatics (Oxford, England) 2015 doi:10.1093/bioinformatics/btv250. Introducing the PRIDE Archive RESTful web services. Reisinger F, del-Toro N, Ternent T, Hermjakob H, Vizcaíno JA. Nucleic acids research 2015 doi:10.1093/nar/gkv382. Proteomics data visualisation. Vizcaíno JA, Barsnes H, Hermjakob H. Proteomics 2015 doi:10.1002/pmic.201570063. Making proteomics data accessible and reusable: current state of proteomics databases and repositories. Perez-Riverol Y, Alpi E, Wang R, Hermjakob H, Vizcaíno JA. Proteomics 2015 doi:10.1002/pmic.201400302. A public repository for mass spectrometry imaging data. Römpp A, Wang R, Albar JP, Urbani A, Hermjakob H, Spengler B, Vizcaíno JA. Analytical and bioanalytical chemistry 2015 doi:10.1007/s00216-014-8357-8. Development of data representation standards by the human proteome organization proteomics standards initiative. Deutsch EW, Albar JP, Binz PA, Eisenacher M, Jones AR, Mayer G, Omenn GS, Orchard S, Vizcaíno JA, Hermjakob H. Journal of the American Medical Informatics Association : JAMIA 2015 doi:10.1093/jamia/ocv001. Open source libraries and frameworks for biological data visualisation: a guide for developers. Wang R, Perez-Riverol Y, Hermjakob H, Vizcaíno JA. Proteomics 2015 doi:10.1002/pmic.201400377. Identifying novel biomarkers through data mining-a realistic scenario? Griss J, Perez-Riverol Y, Hermjakob H, Vizcaíno JA. Proteomics. Clinical applications 2015 doi:10.1002/prca.201400107. PRIDE and ProteomeXchange: webinar Vizcaino JA. 2015 doi:10.6019/tol.pride-px-w.2015.00001.1. Embedding standards in metabolomics: the Metabolomics Society data standards task group Salek RM, Arita M, Dayalan S, Ebbels T, Jones AR, Neumann S, Rocca-Serra P, Viant MR, Vizcaíno JA. Metabolomics : Official journal of the Metabolomic Society 2015 doi:10.1007/s11306-015-0821-8. Analysis of the tryptic search space in UniProt databases. Alpi E, Griss J, da Silva AW, Bely B, Antunes R, Zellner H, Ríos D, O'Donovan C, Vizcaíno JA, Martin MJ. Proteomics 2014 doi:10.1002/pmic.201400227. Meeting new challenges: The 2014 HUPO-PSI/COSMOS Workshop: 13-15 April 2014, Frankfurt, Germany. Orchard S, Albar JP, Binz PA, Kettner C, Jones AR, Salek RM, Vizcaino JA, Deutsch EW, Hermjakob H. Proteomics 2014 doi:10.1002/pmic.201470164. A standardized framing for reporting protein identifications in mzIdentML 1.2. Seymour SL, Farrah T, Binz PA, Chalkley RJ, Cottrell JS, Searle BC, Tabb DL, Vizcaíno JA, Prieto G, Uszkoreit J, Eisenacher M, Martínez-Bartolomé S, Ghali F, Jones AR. Proteomics 2014 doi:10.1002/pmic.201400080. How to submit MS proteomics data to ProteomeXchange via the PRIDE database. Ternent T, Csordas A, Qi D, Gómez-Baena G, Beynon RJ, Jones AR, Hermjakob H, Vizcaíno JA. Proteomics 2014 doi:10.1002/pmic.201400120. Analysis of the protein domain and domain architecture content in fungi and its application in the search of new antifungal targets. Barrera A, Alastruey-Izquierdo A, Martín MJ, Cuesta I, Vizcaíno JA. PLoS computational biology 2014 doi:10.1371/journal.pcbi.1003733. On best practices in the development of bioinformatics software. Leprevost Fda V, Barbosa VC, Francisco EL, Perez-Riverol Y, Carvalho PC. Frontiers in genetics 2014 doi:10.3389/fgene.2014.00199. The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience. Griss J, Jones AR, Sachsenberg T, Walzer M, Gatto L, Hartler J, Thallinger GG, Salek RM, Steinbeck C, Neuhauser N, Cox J, Neumann S, Fan J, Reisinger F, Xu QW, Del Toro N, Pérez-Riverol Y, Ghali F, Bandeira N, Xenarios I, Kohlbacher O, Vizcaíno JA, Hermjakob H. Molecular & cellular proteomics : MCP 2014 doi:10.1074/mcp.o113.036681. jmzTab: a java interface to the mzTab data standard. Xu QW, Griss J, Wang R, Jones AR, Hermjakob H, Vizcaíno JA. Proteomics 2014 doi:10.1002/pmic.201300560. qcML: an exchange format for quality control metrics from mass spectrometry experiments. Walzer M, Pernas LE, Nasso S, Bittremieux W, Nahnsen S, Kelchtermans P, Pichler P, van den Toorn HW, Staes A, Vandenbussche J, Mazanek M, Taus T, Scheltema RA, Kelstrup CD, Gatto L, van Breukelen B, Aiche S, Valkenborg D, Laukens K, Lilley KS, Olsen JV, Heck AJ, Mechtler K, Aebersold R, Gevaert K, Vizcaíno JA, Hermjakob H, Kohlbacher O, Martens L. Molecular & cellular proteomics : MCP 2014 doi:10.1074/mcp.m113.035907. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Vizcaíno JA, Deutsch EW, Wang R, Csordas A, Reisinger F, Ríos D, Dianes JA, Sun Z, Farrah T, Bandeira N, Binz PA, Xenarios I, Eisenacher M, Mayer G, Gatto L, Campos A, Chalkley RJ, Kraus HJ, Albar JP, Martinez-Bartolomé S, Apweiler R, Omenn GS, Martens L, Jones AR, Hermjakob H. Nature biotechnology 2014 doi:10.1038/nbt.2839. PRIDE: Quick tour Vizcaino JA. 2014 doi:10.6019/tol.pride-qt.2014.00001.1. A survey of molecular descriptors used in mass spectrometry based proteomics. Audain E, Sanchez A, Vizcaíno JA, Perez-Riverol Y. Current topics in medicinal chemistry 2014 doi:10.2174/1568026613666131204113537. Editorial: Genomics and proteomics behind drug design. Perez-Riverol Y, Carvalho PC. Current topics in medicinal chemistry 2014 doi:10.2174/1568026613666131204101110. Preparing to work with big data in proteomics - a report on the HUPO-PSI Spring Workshop: April 15-17, 2013, Liverpool, UK. Orchard S, Binz PA, Jones AR, Vizcaino JA, Deutsch EW, Hermjakob H. Proteomics 2013 doi:10.1002/pmic.201370166. SCX charge state selective separation of tryptic peptides combined with 2D-RP-HPLC allows for detailed proteome mapping. Betancourt LH, De Bock PJ, Staes A, Timmerman E, Perez-Riverol Y, Sanchez A, Besada V, Gonzalez LJ, Vandekerckhove J, Gevaert K. Journal of proteomics 2013 doi:10.1016/j.jprot.2013.06.033. JBioWH: an open-source Java framework for bioinformatics data integration. Vera R, Perez-Riverol Y, Perez S, Ligeti B, Kertész-Farkas A, Pongor S. Database : the journal of biological databases and curation 2013 doi:10.1093/database/bat051. Tools (Viewer, Library and Validator) that facilitate use of the peptide and protein identification standard format, termed mzIdentML. Ghali F, Krishna R, Lukasse P, Martínez-Bartolomé S, Reisinger F, Hermjakob H, Vizcaíno JA, Jones AR. Molecular & cellular proteomics : MCP 2013 doi:10.1074/mcp.o113.029777. Pinpointing differentially expressed domains in complex protein mixtures with the cloud service of PatternLab for Proteomics. Leprevost FV, Lima DB, Crestani J, Perez-Riverol Y, Zanchin N, Barbosa VC, Carvalho PC. Journal of proteomics 2013 doi:10.1016/j.jprot.2013.06.013. LipidHome: a database of theoretical lipids optimized for high throughput mass spectrometry lipidomics. Foster JM, Moreno P, Fabregat A, Hermjakob H, Steinbeck C, Apweiler R, Wakelam MJ, Vizcaíno JA. PloS one 2013 doi:10.1371/journal.pone.0061951. From Peptidome to PRIDE: public proteomics data migration at a large scale. Csordas A, Wang R, Ríos D, Reisinger F, Foster JM, Slotta DJ, Vizcaíno JA, Hermjakob H. Proteomics 2013 doi:10.1002/pmic.201200514. The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics. Walzer M, Qi D, Mayer G, Uszkoreit J, Eisenacher M, Sachsenberg T, Gonzalez-Galarza FF, Fan J, Bessant C, Deutsch EW, Reisinger F, Vizcaíno JA, Medina-Aunon JA, Albar JP, Kohlbacher O, Jones AR. Molecular & cellular proteomics : MCP 2013 doi:10.1074/mcp.o113.028506. Pride-asap: automatic fragment ion annotation of identified PRIDE spectra. Hulstaert N, Reisinger F, Rameseder J, Barsnes H, Vizcaíno JA, Martens L. Journal of proteomics 2013 doi:10.1016/j.jprot.2013.04.011. Shorthand notation for lipid structures derived from mass spectrometry. Liebisch G, Vizcaíno JA, Köfeler H, Trötzmüller M, Griffiths WJ, Schmitz G, Spener F, Wakelam MJO. Journal of lipid research 2013 doi:10.1194/jlr.m033506. HI-bone: a scoring system for identifying phenylisothiocyanate-derivatized peptides based on precursor mass and high intensity fragment ions. Perez-Riverol Y, Sánchez A, Noda J, Borges D, Carvalho PC, Wang R, Vizcaíno JA, Betancourt L, Ramos Y, Duarte G, Nogueira FC, González LJ, Padrón G, Tabb DL, Hermjakob H, Domont GB, Besada V. Analytical chemistry 2013 doi:10.1021/ac303239g. The HUPO proteomics standards initiative- mass spectrometry controlled vocabulary. Mayer G, Montecchi-Palazzi L, Ovelleiro D, Jones AR, Binz PA, Deutsch EW, Chambers M, Kallhardt M, Levander F, Shofstahl J, Orchard S, Vizcaíno JA, Hermjakob H, Stephan C, Meyer HE, Eisenacher M, HUPO-PSI Group. Database : the journal of biological databases and curation 2013 doi:10.1093/database/bat009. Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective. Perez-Riverol Y, Wang R, Hermjakob H, Müller M, Vesada V, Vizcaíno JA. Biochimica et biophysica acta 2013 doi:10.1016/j.bbapap.2013.02.032. Effectively addressing complex proteomic search spaces with peptide spectrum matching. Borges D, Perez-Riverol Y, Nogueira FC, Domont GB, Noda J, da Veiga Leprevost F, Besada V, França FM, Barbosa VC, Sánchez A, Carvalho PC. Bioinformatics (Oxford, England) 2013 doi:10.1093/bioinformatics/btt106. Controlled vocabularies and ontologies in proteomics: overview, principles and practice. Mayer G, Jones AR, Binz PA, Deutsch EW, Orchard S, Montecchi-Palazzi L, Vizcaíno JA, Hermjakob H, Oveillero D, Julian R, Stephan C, Meyer HE, Eisenacher M. Biochimica et biophysica acta 2013 doi:10.1016/j.bbapap.2013.02.017. PRIDE Cluster: building a consensus of proteomics data. Griss J, Foster JM, Hermjakob H, Vizcaíno JA. Nature methods 2013 doi:10.1038/nmeth.2343. Computational proteomics pitfalls and challenges: HavanaBioinfo 2012 workshop report. Perez-Riverol Y, Hermjakob H, Kohlbacher O, Martens L, Creasy D, Cox J, Leprevost F, Shan BP, Pérez-Nueno VI, Blazejczyk M, Punta M, Vierlinger K, Valiente PA, Leon K, Chinea G, Guirola O, Bringas R, Cabrera G, Guillen G, Padron G, Gonzalez LJ, Besada V. Journal of proteomics 2013 doi:10.1016/j.jprot.2013.01.019. Proteomics data exchange and storage: the need for common standards and public repositories. Jiménez RC, Vizcaíno JA. Methods in molecular biology (Clifton, N.J.) 2013 doi:10.1007/978-1-62703-392-3_14. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Vizcaíno JA, Côté RG, Csordas A, Dianes JA, Fabregat A, Foster JM, Griss J, Alpi E, Birim M, Contell J, O'Kelly G, Schoenegger A, Ovelleiro D, Pérez-Riverol Y, Reisinger F, Ríos D, Wang R, Hermjakob H. Nucleic acids research 2012 doi:10.1093/nar/gks1262. The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium. Côté RG, Griss J, Dianes JA, Wang R, Wright JC, van den Toorn HW, van Breukelen B, Heck AJ, Hulstaert N, Martens L, Reisinger F, Csordas A, Ovelleiro D, Perez-Rivevol Y, Barsnes H, Hermjakob H, Vizcaíno JA. Molecular & cellular proteomics : MCP 2012 doi:10.1074/mcp.o112.021543. Ten years of standardizing proteomic data: a report on the HUPO-PSI Spring Workshop: April 12-14th, 2012, San Diego, USA. Orchard S, Binz PA, Borchers C, Gilson MK, Jones AR, Nicola G, Vizcaino JA, Deutsch EW, Hermjakob H. Proteomics 2012 doi:10.1002/pmic.201270126. Improvements in the Protein Identifier Cross-Reference service. Wein SP, Côté RG, Dumousseau M, Reisinger F, Hermjakob H, Vizcaíno JA. Nucleic acids research 2012 doi:10.1093/nar/gks338. PRIDE: quality control in a proteomics data repository. Csordas A, Ovelleiro D, Wang R, Foster JM, Ríos D, Vizcaíno JA, Hermjakob H. Database : the journal of biological databases and curation 2012 doi:10.1093/database/bas004. jmzIdentML API: A Java interface to the mzIdentML standard for peptide and protein identification data. Reisinger F, Krishna R, Ghali F, Ríos D, Hermjakob H, Vizcaíno JA, Jones AR. Proteomics 2012 doi:10.1002/pmic.201100577. jmzReader: A Java parser library to process and visualize multiple text and XML-based mass spectrometry data formats. Griss J, Reisinger F, Hermjakob H, Vizcaíno JA. Proteomics 2012 doi:10.1002/pmic.201100578. Introducing an Asp-Pro linker in the synthesis of random one-bead-one-compound hexapeptide libraries compatible with ESI-MS analysis. Masforrol Y, Gil J, González LJ, Pérez-Riverol Y, Fernández-de-Cossío J, Sánchez A, Betancourt LH, Garay HE, Cabrales A, Albericio F, Yang H, Zubarev RA, Besada V, Acosta OR. ACS combinatorial science 2012 doi:10.1021/co200159r. The mzIdentML data standard for mass spectrometry-based proteomics results. Jones AR, Eisenacher M, Mayer G, Kohlbacher O, Siepen J, Hubbard SJ, Selley JN, Searle BC, Shofstahl J, Seymour SL, Julian R, Binz PA, Deutsch EW, Hermjakob H, Reisinger F, Griss J, Vizcaíno JA, Chambers M, Pizarro A, Creasy D. Molecular & cellular proteomics : MCP 2012 doi:10.1074/mcp.m111.014381. PRIDE Inspector: a tool to visualize and validate MS proteomics data. Wang R, Fabregat A, Ríos D, Ovelleiro D, Foster JM, Côté RG, Griss J, Csordas A, Perez-Riverol Y, Reisinger F, Hermjakob H, Martens L, Vizcaíno JA. Nature biotechnology 2012 doi:10.1038/nbt.2112. Isoelectric point optimization using peptide descriptors and support vector machines. Perez-Riverol Y, Audain E, Millan A, Ramos Y, Sanchez A, Vizcaíno JA, Wang R, Müller M, Machado YJ, Betancourt LH, González LJ, Padrón G, Besada V. Journal of proteomics 2012 doi:10.1016/j.jprot.2012.01.029. From proteomics data representation to public data flow: a report on the HUPO-PSI workshop September 2011, Geneva, Switzerland. Orchard S, Albar JP, Deutsch EW, Eisenacher M, Binz PA, Martinez-Bartolomé S, Vizcaíno JA, Hermjakob H. Proteomics 2012 doi:10.1002/pmic.201290016. Selective isolation of multiply charged peptides: a confident strategy for protein identification using a linear trap quadrupole mass spectrometer. Sanchez A, Sun W, Ma J, Betancourt L, Perez-Riverol Y, de-Cossio JF, Padron G, Jiang Y, He F, Gonzalez LJ, Besada V. European journal of mass spectrometry (Chichester, England) 2012 doi:10.1255/ejms.1204. Enabling BioSharing - a report on the Annual Spring Workshop of the HUPO-PSI April 11-13, 2011, EMBL-Heidelberg, Germany. Orchard S, Albar JP, Deutsch EW, Eisenacher M, Vizcaíno JA, Hermjakob H. Proteomics 2011 doi:10.1002/pmic.201190117. Critical amino acid residues in proteins: a BioMart integration of Reactome protein annotations with PRIDE mass spectrometry data and COSMIC somatic mutations. Ndegwa N, Côté RG, Ovelleiro D, D'Eustachio P, Hermjakob H, Vizcaíno JA, Croft D. Database : the journal of biological databases and curation 2011 doi:10.1093/database/bar047. Consequences of the discontinuation of the International Protein Index (IPI) database and its substitution by the UniProtKB "complete proteome" sets. Griss J, Martín M, O'Donovan C, Apweiler R, Hermjakob H, Vizcaíno JA. Proteomics 2011 doi:10.1002/pmic.201100363. Published and perished? The influence of the searched protein database on the long-term storage of proteomics data. Griss J, Côté RG, Gerner C, Hermjakob H, Vizcaíno JA. Molecular & cellular proteomics : MCP 2011 doi:10.1074/mcp.m111.008490. Charge state-selective separation of peptides by reversible modification of amino groups and strong cation-exchange chromatography: evaluation in proteomic studies using peptide-centric database searches. Betancourt LH, Sánchez A, Pérez Y, Fernandez de Cossio J, Gil J, Toledo P, Iguchi S, Aimoto S, González LJ, Padrón G, Takao T, Besada V. Journal of proteomics 2011 doi:10.1016/j.jprot.2011.04.029. Peptide fractionation by acid pH SDS-free electrophoresis. Ramos Y, Garcia Y, Pérez-Riverol Y, Leyva A, Padrón G, Sánchez A, Castellanos-Serra L, González LJ, Besada V. Electrophoresis 2011 doi:10.1002/elps.201000677. Quality control in proteomics. Martens L, Vizcaíno JA, Banks R. Proteomics 2011 doi:10.1002/pmic.201190020. Submitting proteomics data to PRIDE using PRIDE Converter. Barsnes H, Vizcaíno JA, Reisinger F, Eidhammer I, Martens L. Methods in molecular biology (Clifton, N.J.) 2011 doi:10.1007/978-1-60761-977-2_16. PRIDE and "Database on Demand" as valuable tools for computational proteomics. Vizcaíno JA, Reisinger F, Côté R, Martens L. Methods in molecular biology (Clifton, N.J.) 2011 doi:10.1007/978-1-60761-987-1_6. EST analysis pipeline: use of distributed computing resources. González FJ, Vizcaíno JA. Methods in molecular biology (Clifton, N.J.) 2011 doi:10.1007/978-1-61779-040-9_7. Proteomic temporal profile of human brain endothelium after oxidative stress. Ning M, Sarracino DA, Kho AT, Guo S, Lee SR, Krastins B, Buonanno FS, Vizcaíno JA, Orchard S, McMullin D, Wang X, Lo EH. Stroke 2010 doi:10.1161/strokeaha.110.585703. Organelle proteomics experimental designs and analysis. Gatto L, Vizcaíno JA, Hermjakob H, Huber W, Lilley KS. Proteomics 2010 doi:10.1002/pmic.201000244. Proteomics data repositories: providing a safe haven for your data and acting as a springboard for further research. Vizcaíno JA, Foster JM, Martens L. Journal of proteomics 2010 doi:10.1016/j.jprot.2010.06.008. 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Generation, annotation, and analysis of ESTs from four different Trichoderma strains grown under conditions related to biocontrol. Vizcaíno JA, Redondo J, Suárez MB, Cardoza RE, Hermosa R, González FJ, Rey M, Monte E. Applied microbiology and biotechnology 2007 doi:10.1007/s00253-007-0885-0. Partial silencing of a hydroxy-methylglutaryl-CoA reductase-encoding gene in Trichoderma harzianum CECT 2413 results in a lower level of resistance to lovastatin and lower antifungal activity. Cardoza RE, Hermosa MR, Vizcaíno JA, González F, Llobell A, Monte E, Gutiérrez S. Fungal genetics and biology : FG & B 2007 doi:10.1016/j.fgb.2006.11.013. A comparison of the phenotypic and genetic stability of recombinant Trichoderma spp. generated by protoplast- and Agrobacterium-mediated transformation. Cardoza RE, Vizcaino JA, Hermosa MR, Monte E, Gutiérrez S. Journal of microbiology (Seoul, Korea) 2006 doi:. Cloning and characterization of the erg1 gene of Trichoderma harzianum: effect of the erg1 silencing on ergosterol biosynthesis and resistance to terbinafine. Cardoza RE, Vizcaíno JA, Hermosa MR, Sousa S, González FJ, Llobell A, Monte E, Gutiérrez S. Fungal genetics and biology : FG & B 2006 doi:10.1016/j.fgb.2005.11.002. EMBL-EBI is the home for big data in biology. We help scientists exploit complex information to make discoveries that benefit humankind. Services By topic By name (A-Z) Help & Support Licensing Research Publications Research groups Postdocs & PhDs Training Live training On-demand training Support for trainers Contact organisers Industry Members Area Contact Industry team About Contact us Events Jobs News People & groups Intranet for staff EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK. Tel: +44 (0)1223 49 44 44 Full contact details Copyright © EMBL 2022 EMBL-EBI is part of the European Molecular Biology Laboratory Terms of use Edit
