@article{Audain2015, author = {Audain, Enrique and Flower, Darren R. and Ramos, Yassel and Hermjakob, Henning and Perez-Riverol, Yasset}, doi = {10.1093/bioinformatics/btv674}, journal = {Bioinformatics}, month = {nov}, pages = {btv674}, title = {Accurate estimation of Isoelectric Point of Protein and Peptide based on Amino Acid Sequences}, url = {https://academic.oup.com/bioinformatics/article-pdf/32/6/821/6690360/btv674.pdf}, year = {2015} } @article{Deutsch2016, abstract = {Every data-rich community research effort requires a clear plan for ensuring the quality of the data interpretation and comparability of analyses. To address this need within the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), we have developed through broad consultation a set of mass spectrometry data interpretation guidelines that should be applied to all HPP data contributions. For submission of manuscripts reporting HPP protein identification results, the guidelines are presented as a one-page checklist containing 15 essential points followed by two pages of expanded description of each. Here we present an overview of the guidelines and provide an in-depth description of each of the 15 elements to facilitate understanding of the intentions and rationale behind the guidelines, for both authors and reviewers. Broadly, these guidelines provide specific directions regarding how HPP data are to be submitted to mass spectrometry data repositories, how error analysis should be presented, and how detection of novel proteins should be supported with additional confirmatory evidence. These guidelines, developed by the HPP community, are presented to the broader scientific community for further discussion. ; 10 page(s)}, author = {Deutsch, Eric W. and Overall, Christopher M. and Van Eyk, Jennifer E. and Baker, Mark S. and Paik, Young-Ki and Weintraub, Susan T. and Lane, Lydie and Martens, Lennart and Vandenbrouck, Yves and Kusebauch, Ulrike and Hancock, William S. and Aebersold, Ruedi and Hermjakob, Henning and Moritz, Robert L. and Omenn, Gilbert S.}, doi = {10.1021/acs.jproteome.6b00392}, journal = {Journal of Proteome Research}, month = {jan}, pages = {3961-3970}, title = {Human Proteome Project mass spectrometry data interpretation guidelines 2.1}, url = {http://europepmc.org/articles/pmc5096969?pdf=render}, volume = {15}, year = {2016} } @article{Garlid2016, author = {Garlid, Anders Olav and Polson, Jennifer S. and Garlid, Keith D. and Ping, Peipei and Hermjakob, Henning}, doi = {10.1007/164_2016_93}, journal = {Handbook of experimental pharmacology}, month = {jan}, pages = {377-401}, title = {Equipping Physiologists with an Informatics Tool Chest: Toward an Integerated Mitochondrial Phenome.}, url = {https://oadoi.org/10.1007/164_2016_93}, year = {2016} } @article{Gatto2015, author = {Gatto, Laurent and Hansen, Kasper Daniel and Hoopmann, Michael R. and Hermjakob, Henning and Kohlbacher, Oliver and Beyer, Andreas}, doi = {10.1021/acs.jproteome.5b00852}, journal = {Journal of Proteome Research}, month = {nov}, pages = {809-814}, title = {Testing and Validation of Computational Methods for Mass Spectrometry}, url = {http://dx.doi.org/10.15496/publikation-16257}, volume = {15}, year = {2015} } @misc{Hermjakob1998, author = {Hermjakob, Henning}, month = {jul}, title = {SPTR - A comprehensive, non-redundant and up-to-date view of the protein sequence world.}, year = {1998} } @incollection{Hermjakob2005, author = {Hermjakob, Henning and Taylor, Chris and Orchard, Sandra and Zhu, Weimin and Apweiler, Rolf}, doi = {10.1002/047001153x.g405306}, journal = {Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics}, month = {jul}, title = {Data standardization and the HUPO proteomics standards initiative}, url = {https://oadoi.org/10.1002/047001153x.g405306}, year = {2005} } @misc{Hermjakob2013, author = {Hermjakob, Henning}, month = {jan}, title = {Maximising proteomics data for the scientific community}, year = {2013} } @article{Hermjakob2013_2, author = {Hermjakob, Henning}, month = {jun}, title = {A toolkit for the mzIdentML standard: the ProteoIDViewer, the mzidLibrary and the mzidValidator.}, year = {2013} } @article{Hermjakob2013_3, author = {Hermjakob, Henning}, month = {sep}, title = {The Characterization, Design, and Function of the Mitochondrial Proteome: From Organs to Organisms.}, year = {2013} } @article{Hermjakob2015, author = {Hermjakob, Henning}, month = {feb}, title = {BioModels: Content, Features, Functionality, and Use.}, year = {2015} } @article{Hermjakob2015_2, author = {Hermjakob, Henning}, month = {dec}, title = {The Reactome pathway Knowledgebase.}, year = {2015} } @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{Milacic2012, abstract = {Reactome describes biological pathways as chemical reactions that closely mirror the actual physical interactions that occur in the cell. Recent extensions of our data model accommodate the annotation of cancer and other disease processes. First, we have extended our class of protein modifications to accommodate annotation of changes in amino acid sequence and the formation of fusion proteins to describe the proteins involved in disease processes. Second, we have added a disease attribute to reaction, pathway, and physical entity classes that uses disease ontology terms. To support the graphical representation of “cancer” pathways, we have adapted our Pathway Browser to display disease variants and events in a way that allows comparison with the wild type pathway, and shows connections between perturbations in cancer and other biological pathways. The curation of pathways associated with cancer, coupled with our efforts to create other disease-specific pathways, will interoperate with our existing pathway and network analysis tools. Using the Epidermal Growth Factor Receptor (EGFR) signaling pathway as an example, we show how Reactome annotates and presents the altered biological behavior of EGFR variants due to their altered kinase and ligand-binding properties, and the mode of action and specificity of anti-cancer therapeutics.}, author = {Milacic, Marija and Haw, Robin and Rothfels, Karen and Wu, Guanming and Croft, David and Hermjakob, Henning and D'Eustachio, Peter and Stein, Lincoln}, doi = {10.3390/cancers4041180}, journal = {Cancers}, month = {nov}, pages = {1180-1211}, title = {Annotating Cancer Variants and Anti-Cancer Therapeutics in Reactome}, url = {http://dx.doi.org/10.3390/cancers4041180}, volume = {4}, year = {2012} } @article{Morris2003, author = {Morris, L. and Hermjakob, Henning and Kersey, P. J. and Apweiler, R.}, doi = {10.1267/meth03020154}, journal = {Methods of Information in Medicine}, month = {feb}, pages = {154-160}, title = {Integr8: enhanced inter-operability of European molecular biology databases}, url = {https://oadoi.org/10.1267/meth03020154}, volume = {42}, year = {2003} } @article{Orchard2004, author = {Orchard, Sandra and Montecchi-Palazzi, Luisa and Hermjakob, Henning and Apweiler, Rolf}, doi = {10.1142/9789812702456_0018}, journal = {Biocomputing 2005}, month = {dec}, title = {The Use of Common Ontologies and Controlled Vocabularies to Enable Data Exchange and Deposition for Complex Proteomic Experiments}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.111.1176}, year = {2004} } @article{Orchard2007, author = {Orchard, S. and Martens, Lennart and Apweiler, R. and Hermjakob, Henning}, month = {sep}, title = {Human Proteome Organization Proteomics Standards Initiative: data standardization, a view on developments and policy.}, year = {2007} } @article{Perez-Riverol2015, abstract = {Summary: The ms-data-core-api is a free, open-source library for developing computational proteomics tools and pipelines. The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model. The data model is based on controlled vocabularies/ontologies and captures the whole range of data types included in common proteomics experimental workflows, going from spectra to peptide/protein identifications to quantitative results. The library contains readers for three of the most used Proteomics Standards Initiative standard file formats: mzML, mzIdentML, and mzTab. In addition to mzML, it also supports other common mass spectra data formats: dta, ms2, mgf, pkl, apl (text-based), mzXML and mzData (XML-based). Also, it can be used to read PRIDE XML, the original format used by the PRIDE database, one of the world-leading proteomics resources. Finally, we present a set of algorithms and tools whose implementation illustrates the simplicity of developing applications using the library.}, author = {Perez-Riverol, Yasset and Uszkoreit, Julian and Sanchez, Aniel and Ternent, Tobias and del Toro, Noemi and Vizcaíno, Juan Antonio and Wang, Rui and Hermjakob, Henning}, doi = {10.1093/bioinformatics/btv250}, journal = {Bioinformatics}, month = {apr}, pages = {2903-2905}, title = {ms-data-core-api: An open-source, metadata-oriented library for computational proteomics.}, url = {https://academic.oup.com/bioinformatics/article-pdf/31/17/2903/17085007/btv250.pdf}, volume = {31}, year = {2015} } @misc{Perez-Riverol2016, author = {Perez-Riverol, Yasset and Bai, Mingze and Leprevost, Felipe and Squizzato, Silvano and Haug, Ove Kenneth and Carroll, Adam J. and Mi Park, Young and Ok, Haug and Spalding, Dylan and Paschall, Justin and Aj, Carroll and Buso, Nicola and Wang, Mingxun and Bandeira, Nuno and del-Toro, Noemi and Deutsch, Eric and Campbell, David S. and Ternent, Tobias and Beavis, Ronald C. and Zhang, Peng and Salek, Reza and Nesvizhskii, Alexey and Sansone, Susanna-Assunta and Steinbeck, Christoph and Lopez, Rodrigo and Vizcaíno, Juan Antonio and Ping, Peipei and Hermjakob, Henning}, month = {apr}, title = {Omics Discovery Index - Discovering and Linking Public Omics Datasets}, year = {2016} } @article{Perez-Riverol2016_2, author = {Perez-Riverol, Yasset and Griss, Johannes and Lewis, Steve and Tabb, David L. and del-Toro, Noemi and Dianes, José A. and Walzer, Mathias and Rurik, Marc and Kohlbacher, Oliver and Hermjakob, Henning and Wang, Rui and Vizcaíno, Juan Antonio}, doi = {10.1038/nmeth.3902}, journal = {Nature Methods}, month = {jun}, pages = {651-656}, title = {Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets.}, url = {http://europepmc.org/articles/pmc4968634?pdf=render}, volume = {13}, year = {2016} } @article{Zong2013, abstract = { Rationale : Omics sciences enable a systems-level perspective in characterizing cardiovascular biology. Integration of diverse proteomics data via a computational strategy will catalyze the assembly of contextualized knowledge, foster discoveries through multidisciplinary investigations, and minimize unnecessary redundancy in research efforts. Objective : The goal of this project is to develop a consolidated cardiac proteome knowledgebase with novel bioinformatics pipeline and Web portals, thereby serving as a new resource to advance cardiovascular biology and medicine. Methods and Results : We created Cardiac Organellar Protein Atlas Knowledgebase (COPaKB; www.HeartProteome.org ), a centralized platform of high-quality cardiac proteomic data, bioinformatics tools, and relevant cardiovascular phenotypes. Currently, COPaKB features 8 organellar modules, comprising 4203 LC-MS/MS experiments from human, mouse, drosophila, and Caenorhabditis elegans , as well as expression images of 10 924 proteins in human myocardium. In addition, the Java-coded bioinformatics tools provided by COPaKB enable cardiovascular investigators in all disciplines to retrieve and analyze pertinent organellar protein properties of interest. Conclusions : COPaKB provides an innovative and interactive resource that connects research interests with the new biological discoveries in protein sciences. With an array of intuitive tools in this unified Web server, nonproteomics investigators can conveniently collaborate with proteomics specialists to dissect the molecular signatures of cardiovascular phenotypes. }, author = {Zong, Nobel C. and Li, Haomin and Li, Hua and Lam, Maggie P. Y. and Kim, Christina S. and Jimenez, Rafael C. and Kim, Allen K. and Deng, Ning and Zelaya, Ivette and Liem, David and Choi, Jeong Ho and Meyer, David and Odeberg, Jacob and Lu, Hao-Jie and Fang, Caiyun and Xu, Tao and Weiss, James and Duan, Huilong and Uhlen, Mathias and Yates, John R. and Apweiler, Rolf and Ge, Junbo and Hermjakob, Henning and Ping, Peipei}, doi = {10.1161/circresaha.113.301151}, journal = {Circulation Research}, month = {aug}, pages = {1043-1053}, title = {Integration of Cardiac Proteome Biology and Medicine by a Specialized Knowledgebase}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23965338}, volume = {113}, year = {2013} }