@article{Alpi2014, abstract = {In this manuscript, we provide a comprehensive study of the content of UniProt protein data sets for human and mouse. The tryptic search spaces of the UniProtKB (UniProt Knowledgebase) complete proteome sets were compared with other data sets from UniProtKB and with the corresponding IPI (International Protein Index), RefSeq, Ensembl and UniRef100 organism-specific data sets. All protein forms annotated in UniProtKB (both the canonical sequences and isoforms) were evaluated in this study. In addition natural and disease associated amino acid variants annotated in UniProtKB were included in the evaluation. The peptide unicity was also evaluated for each data set. Furthermore, the peptide information in the UniProtKB data sets was also compared against the available peptide-level identifications in the main mass spectrometry based proteomics repositories. Identifying the peptides observed in these repositories is an important resource of information for protein databases as they provide supporting evidence for the existence of otherwise predicted proteins. Likewise, the repositories could use the information available in UniProtKB to direct reprocessing efforts on specific sets of peptides/proteins of interest. In summary, we provide comprehensive information about the different organism-specific sequence data sets available from UniProt, together with the pros and cons for each, in terms of search space for mass spectrometry based bottom-up proteomics workflows. The aim of the analysis is to provide a clear view of the tryptic search space of UniProt and other protein databases to enable scientists to select those most appropriate for their purposes.This article is protected by copyright. All rights reserved}, author = {Alpi, Emanuele and Griss, Johannes and da Silva, Alan Wilter Sousa and Bely, Benoit and Antunes, Ricardo and Zellner, Hermann and Ríos, Daniel and O'Donovan, Claire and Vizcaíno, Juan Antonio and Martin, Maria J.}, doi = {10.1002/pmic.201400227}, journal = {Proteomics}, month = {dec}, pages = {48-57}, title = {Analysis of the tryptic search space in UniProt databases}, url = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/pmic.201400227}, volume = {15}, year = {2014} } @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{Griss2014, abstract = {The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R.}, author = {Griss, Johannes and Jones, Andrew R. and Sachsenberg, Timo and Walzer, Mathias and Gatto, Laurent and Hartler, Jürgen and Thallinger, Gerhard G. and Salek, Reza M. and del Toro, Noemi and Steinbeck, Christoph and Neuhauser, Nadin and Cox, Jürgen and Neumann, Steffen and Fan, Jun and Reisinger, Florian and Xu, Qing-Wei and Del Toro, N. and Perez-Riverol, Yasset and Ghali, Fawaz and Bandeira, Nuno and Xenarios, Ioannis and Kohlbacher, Oliver and Vizcaino, Juan Antonio and Hermjakob, Henning}, doi = {10.1074/mcp.o113.036681}, journal = {Molecular and Cellular Proteomics}, month = {jun}, pages = {2765-2775}, title = {The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience}, url = {https://doi.org/10.1074/mcp.o113.036681}, volume = {13}, year = {2014} } @article{Griss2014_2, abstract = {SRM: Selected Reaction MonitoringTargeted personalized treatment options have become a major hope of clinical (e.g. cancer) research within the past years [1]. Success stories like herceptin [2] in breast cancer and BRAFV600E inhibitors [3] in melanoma have kindled the quest to identify novel biomarkers for diagnosis, patient stratification and personalised treatment options.This article is protected by copyright. All rights reserved}, author = {Griss, Johannes and Perez‐Riverol, Yasset and Hermjakob, Henning and Vizcaíno, Juan Antonio}, doi = {10.1002/prca.201400107}, journal = {Proteomics: Clinical Applications}, month = {oct}, pages = {437-443}, title = {Identifying novel biomarkers through data mining-A realistic scenario?}, url = {https://doi.org/10.1002/prca.201400107}, volume = {9}, year = {2014} } @article{Griss2015, author = {Griss, J. and Stingl, G. and Schmidt, E. and Bangert, C.}, doi = {10.1111/bjd.13941}, journal = {British Journal of Dermatology}, month = {jun}, pages = {231-233}, title = {A Rare Bullous Variant of Dermatitis Herpetiformis}, url = {https://www.researchgate.net/profile/Christine_Bangert/publication/277724779_A_Rare_Bullous_Variant_of_Dermatitis_Herpetiformis/links/557e770108ae26eada8dbe87.pdf}, volume = {174}, year = {2015} } @article{Griss2015_2, author = {Griss, Johannes}, doi = {10.1002/pmic.201500296}, journal = {Proteomics}, month = {nov}, pages = {729-740}, title = {Spectral Library Searching in Proteomics}, url = {https://oadoi.org/10.1002/pmic.201500296}, volume = {16}, year = {2015} } @article{Griss2019, abstract = {AbstractTumor associated inflammation predicts response to immune checkpoint blockade in human melanoma. Current theories on regulation of inflammation center on anti-tumor T cell responses. Here we show that tumor associated B cells are vital to melanoma associated inflammation. Human B cells express pro- and anti-inflammatory factors and differentiate into plasmablast-like cells when exposed to autologous melanoma secretomes in vitro. This plasmablast-like phenotype can be reconciled in human melanomas where plasmablast-like cells also express T cell-recruiting chemokines CCL3, CCL4, CCL5. Depletion of B cells in melanoma patients by anti-CD20 immunotherapy decreases tumor associated inflammation and CD8+ T cell numbers. Plasmablast-like cells also increase PD-1+ T cell activation through anti-PD-1 blockade in vitro and their frequency in pretherapy melanomas predicts response and survival to immune checkpoint blockade. Tumor associated B cells therefore orchestrate and sustain melanoma inflammation and may represent a predictor for survival and response to immune checkpoint blockade therapy.}, author = {Griss, Johannes and Bauer, Wolfgang and Wagner, Christine and Simon, Martin and Chen, Minyi and Grabmeier-Pfistershammer, Katharina and Maurer-Granofszky, Margarita and Roka, Florian and Penz, Thomas and Bock, Christoph and Zhang, Gao and Herlyn, Meenhard and Glatz, Katharina and Läubli, Heinz and Mertz, Kirsten D. and Petzelbauer, Peter and Wiesner, Thomas and Hartl, Markus and Pickl, Winfried F. and Somasundaram, Rajasekharan and Steinberger, Peter and Wagner, Stephan N.}, doi = {10.1038/s41467-019-12160-2}, journal = {Nature Communications}, month = {sep}, title = {B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma}, url = {https://doi.org/10.1038/s41467-019-12160-2}, volume = {10}, year = {2019} } @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{Kochen2016, author = {Kochen, Michael A. and Chambers, Matthew C. and Holman, Jay D. and Nesvizhskii, Alexey I. and Weintraub, Susan T. and Belisle, John T. and Islam, M. Nurul and Griss, Johannes and Tabb, David L.}, doi = {10.1021/acs.analchem.6b00021}, journal = {Analytical Chemistry}, month = {may}, pages = {5733-5741}, title = {Greazy: Open-Source Software for Automated Phospholipid Tandem Mass Spectrometry Identification}, url = {http://europepmc.org/articles/pmc4996967?pdf=render}, volume = {88}, year = {2016} } @article{Leprevost2017, abstract = {Abstract Motivation BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters). Availability and Implementation The software is freely available at github.com/BioContainers/. }, author = {Leprevost, Felipe da Veiga and da Veiga Leprevost, Felipe and Grüning, Björn A. and Alves Aflitos, Saulo and Röst, Hannes L. and Uszkoreit, Julian and Barsnes, Harald and Vaudel, Marc and Moreno, Pablo and Gatto, Laurent and Weber, Jonas and Bai, Mingze and Jimenez, Rafael C. and Sachsenberg, Timo and Pfeuffer, Julianus and Vera Alvarez, Roberto and Griss, Johannes and Nesvizhskii, Alexey I. and Perez-Riverol, Yasset}, doi = {10.1093/bioinformatics/btx192}, journal = {Bioinformatics}, month = {mar}, pages = {2580-2582}, title = {BioContainers: An open-source and community-driven framework for software standardization}, url = {https://academic.oup.com/bioinformatics/article-pdf/doi/10.1093/bioinformatics/btx192/16796098/btx192.pdf}, volume = {33}, year = {2017} } @article{Luo2022, author = {Luo, Xiyang and Bittremieux, Wout and Griss, Johannes and Deutsch, Eric W. and Sachsenberg, Timo and Levitsky, Lev I. and Ivanov, Mark V. and Bubis, Julia A. and Gabriels, Ralf and Webel, Henry and Sanchez, Aniel and Bai, Mingze and Käll, Lukas and Perez-Riverol, Yasset}, doi = {10.1021/acs.jproteome.2c00069}, journal = {Journal of Proteome Research}, month = {may}, pages = {1566-1574}, title = {A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics}, url = {https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.2c00069}, volume = {21}, year = {2022} } @article{Maurer2014, abstract = {Melanoma, the deadliest form of skin cancer, is highly immunogenic and frequently infiltrated with immune cells including B cells. The role of tumor-infiltrating B cells (TIBCs) in melanoma is as yet unresolved, possibly due to technical challenges in obtaining TIBCs in sufficient quantity for extensive studies and due to the limited life span of B cells in vitro. A comprehensive workflow has thus been developed for successful isolation and proteomic analysis of a low number of TIBCs from fresh, human melanoma tissue. In addition, we generated in vitro-proliferating TIBC cultures using simultaneous stimulation with Epstein-Barr virus (EBV) and the TLR9 ligand CpG-oligodesoxynucleotide (CpG ODN). The FASP method and iTRAQ labeling were utilized to obtain a comparative, semiquantitative proteome to assess EBV-induced changes in TIBCs. By using as few as 100 000 B cells (∼5 μg protein)/sample for our proteomic study, a total number of 6507 proteins were identified. EBV-induced changes in TIBCs are similar to those already reported for peripheral B cells and largely involve changes in cell cycle proliferation, apoptosis, and interferon response, while most of the proteins were not significantly altered. This study provides an essential, further step toward detailed characterization of TIBCs including functional in vitro analysis.}, author = {Maurer, Margarita and Müller, André C. and Parapatics, Katja and Pickl, Winfried F. and Wagner, Christine and Rudashevskaya, Elena L. and Breitwieser, Florian P. and Colinge, Jacques and Garg, Kanika and Griss, Johannes and Bennett, Keiryn L. and Wagner, Stephan N.}, doi = {10.1021/pr401270y}, journal = {Journal of Proteome Research}, month = {may}, pages = {2830-2845}, title = {Comprehensive Comparative and Semiquantitative Proteome of a Very Low Number of Native and Matched Epstein-Barr-Virus-Transformed B Lymphocytes Infiltrating Human Melanoma}, url = {https://www.researchgate.net/profile/Stephan_Wagner3/publication/262112266_Comprehensive_Comparative_and_Semiquantitative_Proteome_of_a_Very_Low_Number_of_Native_and_Matched_Epstein-Barr-Virus-Transformed_B_Lymphocytes_Infiltrating_Human_Melanoma/links/00b49537217c78354b000000.pdf}, volume = {13}, year = {2014} } @article{Parsons2019, author = {Parsons, Harriet T. and Stevens, Tim J. and McFarlane, Heather E. and Vidal-Melgosa, Silvia and Griss, Johannes and Lawrence, Nicola and Butler, Richard and Sousa, Mirta M. L. and Salemi, Michelle and Willats, William G. T. and Petzold, Christopher J. and Heazlewood, Joshua L. and Lilley, Kathryn S.}, doi = {10.1105/tpc.19.00081}, journal = {The Plant Cell}, month = {jul}, pages = {2010-2034}, title = {Separating Golgi Proteins from Cis to Trans Reveals Underlying Properties of Cisternal Localization}, url = {https://oadoi.org/10.1105/tpc.19.00081}, volume = {31}, year = {2019} } @article{Paulitschke2013, abstract = {Drug resistance is a major obstacle in melanoma treatment. Recognition of specific resistance patterns, the understanding of the patho-physiology of drug resistance and identification of remaining options for individual melanoma treatment would greatly improve therapeutic success. We performed mass spectrometry-based proteome profiling of A375 melanoma cells and HeLa cells characterized as sensitive to cisplatin in comparison to cisplatin resistant M24met and TMFI melanoma cells. Cells were fractionated into cytoplasm, nuclei and secretome and the proteome profiles classified according to Gene Ontology. The cisplatin resistant cells displayed increased expression of lysosomal as well as Ca2+ ion binding and cell adherence proteins. These findings were confirmed using Lysotracker Red staining and cell adhesion assays with a panel of extracellular matrix proteins. To discriminate specific survival proteins, we selected constitutively expressed proteins of resistant M24met cells which were found expressed upon challenging the sensitive A375 cells. Using the CPL/MUW proteome database, the selected lysosomal, cell adherence and survival proteins apparently specifying resistant cells were narrowed down to 47 proteins representing a potential resistance signature. These were tested against our proteomics database comprising more than 200 different cell types/cell states for its predictive power. We provide evidence that this signature enables the automated assignment of resistance features as readout from proteome profiles of any human cell type. Proteome profiling and bioinformatic processing may thus support the understanding of drug resistance mechanism, eventually guiding patient tailored therapy.}, author = {Paulitschke, Verena and Haudek-Prinz, Verena and Griss, Johannes and Berger, Walter and Mohr, Thomas and Pehamberger, Hubert and Kunstfeld, Rainer and Gerner, Christopher}, doi = {10.1021/pr400124w}, journal = {Journal of Proteome Research}, month = {may}, pages = {3264-3276}, title = {Functional Classification of Cellular Proteome Profiles Support the Identification of Drug Resistance Signatures in Melanoma Cells}, url = {https://doi.org/10.1021/pr400124w}, volume = {12}, year = {2013} } @article{Perez-Riverol2015, abstract = {The original PRIDE Inspector tool was developed as an open source standalone tool to enable the visualization and validation of mass-spectrometry (MS)-based proteomics data before data submission, or already publicly available in the PRIDE (PRoteomics IDEntifications) database. The initial implementation of the tool focused on visualizing PRIDE data by supporting the PRIDE XML format and a direct access to private (password protected) and public experiments in PRIDE. The ProteomeXchange (PX) Consortium has been set up to enable a better integration of existing public proteomics repositories, maximizing its benefit to the scientific community through the implementation of standard submission and dissemination pipelines. Within the Consortium, PRIDE is focused on supporting submissions of tandem MS data. The increasing use and popularity of the new PSI (Proteomics Standards Initiative) data standards such as mzIdentML and mzTab, and the diversity of workflows supported by the PX resources, prompted us to design and implement a new suite of algorithms and libraries that would build upon the success of the original PRIDE Inspector and would enable users to visualize and validate PX complete submissions. The PRIDE Inspector Toolsuite supports the handling and visualization of different experimental output files, ranging from spectra (mzML, mzXML and the most popular peak lists formats), peptide and protein identification results (mzIdentML, PRIDE XML, mzTab), to quantification data (mzTab, PRIDE XML), using a modular and extensible set of open-source, cross-platform libraries. We believe that the PRIDE Inspector Toolsuite represents a milestone in the visualization and quality assessment of proteomics data. It is freely available at http://github.com/PRIDE-Toolsuite/.}, author = {Perez-Riverol, Yasset and Xu, Qing-Wei and Wang, Rui and Uszkoreit, Julian and Griss, Johannes and Sanchez, Aniel and Reisinger, Florian and Csordas, Attila and Ternent, Tobias and del-Toro, Noemi and Dianes, Jose A. and Eisenacher, Martin and Hermjakob, Henning and Vizcaíno, Juan Antonio}, doi = {10.1074/mcp.o115.050229}, journal = {Molecular and Cellular Proteomics}, month = {nov}, pages = {305-317}, title = {PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets}, url = {http://www.mcponline.org/content/15/1/305.full.pdf}, volume = {15}, year = {2015} } @article{Perez-Riverol2016, 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{Schittmayer2016, author = {Schittmayer, Matthias and Fritz, Katarina and Liesinger, Laura and Griss, Johannes and Birner-Gruenberger, Ruth}, doi = {10.1021/acs.jproteome.5b01105}, journal = {Journal of Proteome Research}, month = {mar}, pages = {1222-1229}, title = {Cleaning out the litterbox of proteomic scientists´ favorite pet: optimized data analysis avoiding trypsin artifacts}, url = {https://doi.org/10.1021/acs.jproteome.5b01105}, volume = {15}, year = {2016} } @article{Weiss2014, abstract = {Detecting serum-autoantibodies by anti-Desmoglein-1 (anti-Dsg1) and anti-Dsg3 ELISAs as well as indirect immunofluorescence (IIF) are established complementary methods to diagnose pemphigus. Whether autoantibody levels also reflect clinical disease activity is still a matter of debate, as head-to-head comparisons of ELISA values and IIF titres with clinical activity over a longer treatment period are scarce. In our retrospective study, we compared aggregated repetitive intra-patient ELISA values and IIF titres with grades of clinical disease (1 = remission, 2 = moderate activity, 3 = exacerbation) in 47 patients suffering from pemphigus vulgaris (PV, n = 36) and pemphigus foliaceus (PF, n=11). We found that anti-Dsg1 ELISA values in PF and mucocutaneous PV as well as anti-Dsg3 ELISA values in PV best reflect disease activity. IIF titres, by contrast, did not show a significant association with disease severity. From these data we conclude that ELISA index values can be a valuable tool to monitor disease in patients with pemphigus, whereas IIF titres reflect clinical activity only insufficiently.}, author = {Weiss, Doris and Ristl, Robin and Griss, Johannes and Bangert, Christine and Foedinger, Dagmar and Stingl, Georg and Brunner, Patrick M.}, doi = {10.2340/00015555-2023}, journal = {Acta Dermato-Venereologica}, month = {nov}, pages = {559-564}, title = {Autoantibody Levels and Clinical Disease Severity in Patients with Pemphigus: Comparison of Aggregated Anti-desmoglein ELISA Values and Indirect Immunofluorescence Titres}, url = {https://www.medicaljournals.se/acta/content_files/download.php?doi=10.2340/00015555-2023}, volume = {95}, year = {2014} } @article{Xu2014, abstract = {mzTab is the most recent standard format developed by the Proteomics Standards Initiative (PSI). mzTab is a flexible tab-delimited file that can capture identification and quantification results coming from mass spectrometry (MS)-based proteomics and metabolomics approaches. We here present an open-source Java Application Programming Interface (API) for mzTab called jmzTab.The software allows the efficient processing of mzTab files, providing read and write capabilities, and is designed to be embedded in other software packages. The second key feature of the jmzTab model is that it provides a flexible framework to maintain the logical integrity between the metadata and the table-based sections in the mzTab files. In this article, as two example implementations, we also describe two stand-alone tools that can be used to validate mzTab files and to convert PRIDE XML files to mzTab. The library is freely available at http://mztab.googlecode.com.This article is protected by copyright. All rights reserved}, author = {Xu, Qing-Wei and Griss, Johannes and Wang, Rui and Jones, Andrew R. and Hermjakob, Henning and Vizcaíno, Juan Antonio}, doi = {10.1002/pmic.201300560}, journal = {Proteomics}, month = {apr}, pages = {1328-1332}, title = {jmzTab: A Java interface to the mzTab data standard}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24659499}, volume = {14}, year = {2014} }