@article{Caufield2018, abstract = {AbstractClinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials.}, author = {Caufield, J. Harry and Zhou, Yijiang and Garlid, Anders O. and Setty, Shaun P. and Liem, David A. and Cao, Quan and Lee, Jessica M. and Murali, Sanjana and Spendlove, Sarah and Wang, Wei and Zhang, Li and Sun, Yizhou and Bui, Alex and Hermjakob, Henning and Watson, Karol E. and Ping, Peipei}, doi = {10.1038/sdata.2018.258}, journal = {Scientific Data}, month = {nov}, title = {A reference set of curated biomedical data and metadata from clinical case reports}, url = {https://doi.org/10.1038/sdata.2018.258}, volume = {5}, year = {2018} } @article{del Toro2021, abstract = {Abstract The IntAct molecular interaction database (https://www.ebi.ac.uk/intact) is a curated resource of molecular interactions, derived from the scientific literature and from direct data depositions. As of August 2021, IntAct provides more than one million binary interactions, curated by twelve global partners of the International Molecular Exchange consortium, for which the IntAct database provides a shared curation and dissemination platform. The IMEx curation policy has always emphasised a fine-grained data and curation model, aiming to capture the relevant experimental detail essential for the interpretation of the provided molecular interaction data. Here, we present recent curation focus and progress, as well as a completely redeveloped website which presents IntAct data in a much more user-friendly and detailed way.}, author = {del Toro, Noemi and Shrivastava, Anjali and Ragueneau, Eliot and Meldal, Birgit and Combe, Colin and Barrera, Elisabet and Perfetto, Livia and How, Karyn and Ratan, Prashansa and Shirodkar, Gautam and Lu, Odilia and Mészáros, Bálint and Watkins, Xavier and Pundir, Sangya and Licata, Luana and Iannuccelli, Marta and Pellegrini, Matteo and Martin, Maria Jesus and Panni, Simona and Duesbury, Margaret and Vallet, Sylvain D. and Rappsilber, Juri and Ricard-Blum, Sylvie and Cesareni, Gianni and Salwinski, Lukasz and Orchard, Sandra and Porras, Pablo and Panneerselvam, Kalpana and Hermjakob, Henning}, doi = {10.1093/nar/gkab1006}, journal = {Nucleic Acids Research}, month = {nov}, pages = {D648-D653}, title = {The IntAct database: efficient access to fine-grained molecular interaction data}, url = {https://doi.org/10.1093/nar/gkab1006}, volume = {50}, year = {2021} } @article{dummy-Author_name2017, author = {dummy-Author_name, and Wolstencroft, Katy and McMurry, Julie A. and Blomberg, Niklas and Burdett, Tony and Mueller, Wolfgang and Conlin, Tom and Conte, Nathalie and Courtot, Melanie and Deck, John and Rc, Jimenez and Dumontier, Michel and Gonzalez-Beltran, Alejandra and Fellows, Donal K. and Gormanns, Philipp and Novère, Nicolas Le and Grethe, Jeffrey and Hastings, Janna and Juty, Navtej and Hermjakob, Henning and Hériché, Jean-Karim and Burdett, Anthony and Ison, Jon C. and Jimenez, Rafael C. and Jupp, Simon and Kunze, John and Laibe, Camille and Ja, McMurry and Morris, Chris and Malone, James Robert and Le Novere, Nicolas and Muilu, Juha and Martin, Maria-Jesus and Müller, Wolfgang and Rocca-Serra, Philippe and Sansone, Susanna-Assunta and Sariyar, Murat and Snoep, Jacky L. and Stanford, Natalie J. and Soiland-Reyes, Stian and Swainston, Neil and Burdett, T. and Washington, Nicole and Williams, Alan R. and Heriche, Jean-Karim and Wimalaratne, Sarala M. and Winfree, Lilly M. and Dk, Fellows and McEntyre, Jo and Jk, Hériché and Mungall, Christopher J. and Goble, Carole and Jc, Ison and Haendel, Melissa A. and Parkinson, Helen}, doi = {10.1371/journal.pbio.2001414}, journal = {PLoS Biology}, month = {mar}, pages = {e2001414}, title = {Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data}, url = {https://doi.org/10.1371/journal.pbio.2001414}, volume = {15}, year = {2017} } @article{Fabregat2018, author = {Fabregat, Antonio and Korninger, Florian and Viteri, Guilherme and Sidiropoulos, Konstantinos and Marin-Garcia, Pablo and Ping, Peipei and Wu, Guanming and Stein, Lincoln and D’Eustachio, Peter and Hermjakob, Henning}, doi = {10.1371/journal.pcbi.1005968}, journal = {PLoS Computational Biology}, month = {jan}, pages = {e1005968}, title = {Reactome graph database: Efficient access to complex pathway data}, url = {https://doi.org/10.1371/journal.pcbi.1005968}, volume = {14}, year = {2018} } @article{Gillespie2021, abstract = {Abstract The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied (‘dark’) proteins from analyzed datasets in the context of Reactome’s manually curated pathways.}, author = {Gillespie, Marc and Jassal, Bijay and Stephan, Ralf and Milacic, Marija and Rothfels, Karen and Senff-Ribeiro, Andrea and Griss, Johannes and Sevilla, Cristoffer and Matthews, Lisa and Gong, Chuqiao and Deng, Chuan and Varusai, Thawfeek and Ragueneau, Eliot and Haider, Yusra and May, Bruce and Shamovsky, Veronica and Weiser, Joel and Brunson, Timothy and Sanati, Nasim and Beckman, Liam and Shao, Xiang and Fabregat, Antonio and Sidiropoulos, Konstantinos and Murillo, Julieth and Viteri, Guilherme and Cook, Justin and Shorser, Solomon and Bader, Gary and Demir, Emek and Sander, Chris and Haw, Robin and Wu, Guanming and Stein, Lincoln and Hermjakob, Henning and D’Eustachio, Peter}, doi = {10.1093/nar/gkab1028}, journal = {Nucleic Acids Research}, month = {nov}, pages = {D687-D692}, title = {The reactome pathway knowledgebase 2022}, url = {https://doi.org/10.1093/nar/gkab1028}, volume = {50}, year = {2021} } @article{Jassal2019, abstract = {Abstract The Reactome Knowledgebase (https://reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations in a single consistent data model, an extended version of a classic metabolic map. Reactome functions both as an archive of biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. To extend our ability to annotate human disease processes, we have implemented a new drug class and have used it initially to annotate drugs relevant to cardiovascular disease. Our annotation model depends on external domain experts to identify new areas for annotation and to review new content. New web pages facilitate recruitment of community experts and allow those who have contributed to Reactome to identify their contributions and link them to their ORCID records. To improve visualization of our content, we have implemented a new tool to automatically lay out the components of individual reactions with multiple options for downloading the reaction diagrams and associated data, and a new display of our event hierarchy that will facilitate visual interpretation of pathway analysis results.}, author = {Jassal, Bijay and Matthews, Lisa and Viteri, Guilherme and Gong, Chuqiao and Lorente, Pascual and Fabregat, Antonio and Sidiropoulos, Konstantinos and Cook, Justin and Gillespie, Marc and Haw, Robin and Loney, Fred and May, Bruce and Milacic, Marija and Rothfels, Karen and Sevilla, Cristoffer and Shamovsky, Veronica and Shorser, Solomon and Varusai, Thawfeek and Weiser, Joel and Wu, Guanming and Stein, Lincoln and Hermjakob, Henning and D’Eustachio, Peter}, doi = {10.1093/nar/gkz1031}, journal = {Nucleic Acids Research}, month = {nov}, title = {The reactome pathway knowledgebase}, url = {https://doi.org/10.1093/nar/gkz1031}, year = {2019} } @article{Lieven2018, author = {Lieven, Christian and Beber, Moritz Emanuel and Olivier, Brett G. and Bergmann, Frank T. and Ataman, Meric and Babaei, Parizad and Bartell, Jennifer A. and Blank, Lars M. and Chauhan, Siddharth and Correia, Kevin and Diener, Christian and Dräger, Andreas and Ebert, Birgitta Elisabeth and Edirisinghe, Janaka N. and Faria, Jose P. and Feist, Adam and Fengos, Georgios and Fleming, Ronan M. T. and Garcia-Jimenez, Beatriz and Hatzimanikatis, Vassily and van Helvoirt, Wout and Henry, Christopher S. and Hermjakob, Henning and Herrgard, Markus J. and Kim, Hyun Uk and Novère, Nicolas Le and King, Zachary and Koehorst, Jasper Jan and Klamt, Steffen and Klipp, Edda and Lakshmanan, Meiyappan and Lee, Dong-Yup and Hongwu, and Lee, Sang Yup and Lee, Sunjae Y. and Le Novere, Nicolas and Schaap, Peter J. and Lewis, Nathan E. and Shoaie, Saeed and Machado, Daniel and Mahadevan, Radhakrishnan and Sonnenschein, Nikolaus and Ma, Hongwu and Maia, Paulo and Mardinoglu, Adil and Teusink, Bas and Vilaca, Paulo and Monk, Jonathan M. and Nielsen, Jens and Vik, Jon Olav and Medlock, Greg L. and Nielsen, Lars Keld and Wodke, Judith A. and Nogales, Juan and Nookaew, Intawat and Xavier, Joana C. and Yuan, Qianqian and Palsson, Bernhard O. and Papin, Jason A. and Zakhartsev, Maksim and Patil, Kiran Raosaheb and Zhang, Cheng and Poolman, Mark and Price, Nathan D. and Richelle, Anne and Rocha, Isabel and Sanchez, Benjamin J. and Malik Sheriff, Rahuman S. and Resendis, Osbaldo}, month = {jun}, title = {Memote: A community-driven effort towards a standardized genome-scale metabolic model test suite}, year = {2018} } @article{Lieven2020, abstract = {Nature biotechnology 38(3), 272-276 (2020). doi:10.1038/s41587-020-0446-y}, author = {Lieven, Christian and Beber, Moritz E. and Olivier, Brett G. and Bergmann, Frank T. and Ataman, Meric and Babaei, Parizad and Bartell, Jennifer A. and Blank, Lars Mathias and Chauhan, Siddharth and Correia, Kevin and Diener, Christian and van Helvoirt, Wout and Dräger, Andreas and Ebert, Birgitta E. and Edirisinghe, Janaka N. and Faria, José P. and Feist, Adam M. and Fengos, Georgios and Fleming, Ronan M. T. and García-Jiménez, Beatriz and Hatzimanikatis, Vassily and Henry, Christopher S. and Hermjakob, Henning and Herrgård, Markus J. and Kaafarani, Ali and Kim, Hyun Uk and King, Zachary and Klamt, Steffen and Klipp, Edda and Koehorst, Jasper J. and König, Matthias and Lakshmanan, Meiyappan and Lee, Dong-Yup and Lee, Sang Yup and Lee, Sunjae and Lewis, Nathan E. and Liu, Filipe and Hongwu, and Ma, Hongwu and Machado, Daniel and Mahadevan, Radhakrishnan and Maia, Paulo and Mardinoglu, Adil and Medlock, Gregory L. and Monk, Jonathan M. and Nielsen, Jens and Nielsen, Lars Keld and Nogales, Juan and Nookaew, Intawat and Palsson, Bernhard O. and Papin, Jason A. and Patil, Kiran R. and Poolman, Mark and Price, Nathan D. and Resendis-Antonio, Osbaldo and Richelle, Anne and Rocha, Isabel and Sánchez, Benjamín J. and Sánchez, Benjamín J. and Schaap, Peter J. and Malik Sheriff, Rahuman S. and Shoaie, Saeed and Sonnenschein, Nikolaus and Teusink, Bas and Vilaça, Paulo and Vik, Jon Olav and Wodke, Judith A. H. and Xavier, Joana C. and Yuan, Qianqian and Zakhartsev, Maksim and Zhang, Cheng}, doi = {10.18154/rwth-2020-03668}, journal = {Nature Biotechnology}, month = {mar}, pages = {272-276}, title = {MEMOTE for standardized genome-scale metabolic model testing}, url = {http://www.nature.com/articles/s41587-020-0446-y.pdf}, volume = {38}, year = {2020} } @article{Lieven2020_2, abstract = {An amendment to this paper has been published and can be accessed via a link at the top of the paper.}, author = {Lieven, Christian and Beber, Moritz E. and Olivier, Brett G. and Bergmann, Frank T. and Ataman, Meric and Babaei, Parizad and Bartell, Jennifer A. and Blank, Lars Mathias and Chauhan, Siddharth and Correia, Kevin and Diener, Christian and Dräger, Andreas and Ebert, Birgitta E. and Edirisinghe, Janaka N. and Faria, José P. and Feist, Adam M. and Fengos, Georgios and Fleming, Ronan M. T. and García-Jiménez, Beatriz and Hatzimanikatis, Vassily and van Helvoirt, Wout and Henry, Christopher S. and Hermjakob, Henning and Herrgård, Markus J. and Kaafarani, Ali and Kim, Hyun Uk and King, Zachary and Klamt, Steffen and Klipp, Edda and Koehorst, Jasper J. and König, Matthias and Lakshmanan, Meiyappan and Lee, Dong-Yup and Lee, Sang Yup and Lee, Sunjae and Lewis, Nathan E. and Liu, Filipe and Hongwu, and Ma, Hongwu and Machado, Daniel and Mahadevan, Radhakrishnan and Maia, Paulo and Mardinoglu, Adil and Medlock, Gregory L. and Monk, Jonathan M. and Nielsen, Jens and Nielsen, Lars Keld and Nogales, Juan and Nookaew, Intawat and Palsson, Bernhard O. and Papin, Jason A. and Patil, Kiran R. and Poolman, Mark and Price, Nathan D. and Resendis-Antonio, Osbaldo and Richelle, Anne and Rocha, Isabel and Sánchez, Benjamín J. and Sánchez, Benjamín J. and Schaap, Peter J. and Malik Sheriff, Rahuman S. and Sheriff, Rahuman S. Malik and Shoaie, Saeed and Sonnenschein, Nikolaus and Teusink, Bas and Vilaça, Paulo and Vik, Jon Olav and Wodke, Judith A. H. and Xavier, Joana C. and Yuan, Qianqian and Zakhartsev, Maksim and Zhang, Cheng}, doi = {10.18154/rwth-2021-01434}, journal = {Nature Biotechnology}, month = {mar}, pages = {504-504}, title = {Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing}, url = {http://www.nature.com/articles/s41587-020-0477-4.pdf}, volume = {38}, year = {2020} } @article{Meldal2021, abstract = {Abstract The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.}, author = {Meldal, Birgit H. M. and Pons, Carles and Perfetto, Livia and Del-Toro, Noemi and Wong, Edith and Aloy, Patrick and Hermjakob, Henning and Orchard, Sandra and Porras, Pablo}, doi = {10.1093/nar/gkab077}, journal = {Nucleic Acids Research}, month = {mar}, pages = {3156-3167}, title = {Analysing the yeast complexome—the Complex Portal rising to the challenge}, url = {https://doi.org/10.1093/nar/gkab077}, volume = {49}, year = {2021} } @article{Meldal2021_2, abstract = {Abstract The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database of macromolecular complexes with known function from a range of model organisms. It summarizes complex composition, topology and function along with links to a large range of domain-specific resources (i.e. wwPDB, EMDB and Reactome). Since the last update in 2019, we have produced a first draft complexome for Escherichia coli, maintained and updated that of Saccharomyces cerevisiae, added over 40 coronavirus complexes and increased the human complexome to over 1100 complexes that include approximately 200 complexes that act as targets for viral proteins or are part of the immune system. The display of protein features in ComplexViewer has been improved and the participant table is now colour-coordinated with the nodes in ComplexViewer. Community collaboration has expanded, for example by contributing to an analysis of putative transcription cofactors and providing data accessible to semantic web tools through Wikidata which is now populated with manually curated Complex Portal content through a new bot. Our data license is now CC0 to encourage data reuse. Users are encouraged to get in touch, provide us with feedback and send curation requests through the ‘Support’ link.}, author = {Meldal, Birgit H. M. and Perfetto, Livia and Combe, Colin and Lubiana, Tiago and Ferreira Cavalcante, João Vitor and Bye-A.-Jee, Hema and Waagmeester, Andra and del-Toro, Noemi and Shrivastava, Anjali and Barrera, Elisabeth and Wong, Edith and Mlecnik, Bernhard and Bindea, Gabriela and Panneerselvam, Kalpana and Willighagen, Egon and Rappsilber, Juri and Porras, Pablo and Hermjakob, Henning and Orchard, Sandra}, doi = {10.1093/nar/gkab991}, journal = {Nucleic Acids Research}, month = {oct}, pages = {D578-D586}, title = {Complex Portal 2022: new curation frontiers}, url = {https://doi.org/10.1093/nar/gkab991}, volume = {50}, year = {2021} } @article{Perez-Riverol2017, author = {Perez-Riverol, Yasset and Bai, Mingze and da Veiga Leprevost, Felipe and Squizzato, Silvano and Park, Young Mi and Haug, Kenneth and Carroll, Adam J. and Spalding, Dylan and Paschall, Justin and Wang, Mingxun and del-Toro, Noemi and Ternent, Tobias and Zhang, Peng and Buso, Nicola and Bandeira, Nuno and Deutsch, Eric W. and Campbell, David S. and Beavis, Ronald C. and Salek, Reza M. and Sarkans, Ugis and Petryszak, Robert and Keays, Maria and Fahy, Eoin and Sud, Manish and Subramaniam, Shankar and Barbera, Ariana and Jiménez, Rafael C. and Nesvizhskii, Alexey I. and Sansone, Susanna-Assunta and Steinbeck, Christoph and Lopez, Rodrigo and Vizcaíno, Juan A. and Ping, Peipei and Hermjakob, Henning}, doi = {10.1038/nbt.3790}, journal = {Nature Biotechnology}, month = {may}, pages = {406-409}, title = {Discovering and linking public omics data sets using the Omics Discovery Index}, url = {http://europepmc.org/articles/pmc5831141?pdf=render}, volume = {35}, year = {2017} } @article{Perfetto2020, abstract = {Abstract The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (https://www.ebi.ac.uk/intact) and will be continuously updated as research on COVID-19 progresses.}, author = {Perfetto, L. and del-Toro, N. and Pastrello, C. and Del Toro, N. and Duesbury, M. and Iannuccelli, M. and Kotlyar, M. and Licata, L. and Meldal, B. and Panneerselvam, K. and Panni, S. and Rahimzadeh, N. and Ricard-Blum, S. and Salwinski, L. and Shrivastava, A. and Cesareni, G. and Pellegrini, M. and Orchard, S. and Jurisica, I. and Hermjakob, H. and Hh, Hermjakob and Porras, P.}, doi = {10.1093/database/baaa096}, journal = {Database}, month = {jan}, title = {The IMEx coronavirus interactome: an evolving map of Coronaviridae–host molecular interactions}, url = {https://doi.org/10.1093/database/baaa096}, volume = {2020}, year = {2020} } @article{Porras2020, abstract = {AbstractThe International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.}, author = {Porras, Pablo and Barrera, Elisabet and Bridge, Alan and del-Toro, Noemi and Cesareni, Gianni and Duesbury, Margaret and Hermjakob, Henning and Iannuccelli, Marta and Jurisica, Igor and Kotlyar, Max and Licata, Luana and Lovering, Ruth C. and Lynn, David J. and Meldal, Birgit and Nanduri, Bindu and Paneerselvam, Kalpana and Panni, Simona and Pastrello, Chiara and Pellegrini, Matteo and Perfetto, Livia and Rahimzadeh, Negin and Ratan, Prashansa and Ricard-Blum, Sylvie and Salwinski, Lukasz and Shirodkar, Gautam and Shrivastava, Anjalia and Orchard, Sandra}, doi = {10.1038/s41467-020-19942-z}, journal = {Nature Communications}, month = {dec}, title = {Towards a unified open access dataset of molecular interactions}, url = {https://doi.org/10.1038/s41467-020-19942-z}, volume = {11}, year = {2020} } @article{Stefanucci2023, abstract = {Abstract Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.}, author = {Stefanucci, Luca and Collins, Janine and Sims, Matthew C. and Barrio-Hernandez, Inigo and Sun, Luanluan and Burren, Oliver S. and Perfetto, Livia and Bender, Isobel and Callahan, Tiffany J. and Fleming, Kathryn and Guerrero, Jose A. and Hermjakob, Henning and Martin, Maria J. and Stephenson, James and Paneerselvam, Kalpana and Petrovski, Slavé and Porras, Pablo and Robinson, Peter N. and Wang, Quanli and Watkins, Xavier and Frontini, Mattia and Laskowski, Roman A. and Beltrao, Pedro and Di Angelantonio, Emanuele and Gomez, Keith and Laffan, Mike and Ouwehand, Willem H. and Mumford, Andrew D. and Freson, Kathleen and Carss, Keren and Downes, Kate and Gleadall, Nick and Megy, Karyn and Bruford, Elspeth and Vuckovic, Dragana}, doi = {10.1182/blood.2023020118}, journal = {Blood}, month = {dec}, pages = {2055-2068}, title = {The effects of pathogenic and likely pathogenic variants for inherited hemostasis disorders in 140 214 UK Biobank participants}, url = {https://oadoi.org/10.1182/blood.2023020118}, volume = {142}, year = {2023} } @article{Sánchez2019, author = {Sánchez, Luis Francisco Hernández and Burger, Bram and Horro, Carlos and Fabregat, Antonio and Johansson, Stefan and Njølstad, Pål Rasmus and Barsnes, Harald and Hermjakob, Henning and Vaudel, Marc}, doi = {10.1093/gigascience/giz088}, journal = {GigaScience}, month = {jul}, title = {PathwayMatcher: proteoform-centric network construction enables fine-granularity multiomics pathway mapping}, url = {https://doi.org/10.1093/gigascience/giz088}, volume = {8}, year = {2019} } @article{Varusai2020, author = {Varusai, Thawfeek Mohamed and Jupe, Steven and Sevilla, Cristoffer and Matthews, Lisa and Gillespie, Marc and Stein, Lincoln and Wu, Guanming and D’Eustachio, Peter and Metzakopian, Emmanouil and Hermjakob, Henning}, doi = {10.1080/15548627.2020.1761659}, journal = {Autophagy}, month = {jun}, pages = {1543-1554}, title = {Using Reactome to build an autophagy mechanism knowledgebase}, url = {https://oadoi.org/10.1080/15548627.2020.1761659}, volume = {17}, year = {2020} } @article{Waagmeester2020, abstract = {Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.}, author = {Waagmeester, Andra and Stupp, Gregory and Burgstaller-Muehlbacher, Sebastian and Good, Benjamin M. and Griffith, Malachi and Griffith, Obi L. and Hanspers, Kristina and Hermjakob, Henning and Hudson, Toby S. and Hybiske, Kevin and Keating, Sarah M. and Manske, Magnus and Mayers, Michael and Mietchen, Daniel and Mitraka, Elvira and Pico, Alexander R. and Putman, Timothy and Riutta, Anders and Queralt-Rosinach, Nuria and Schriml, Lynn M. and Shafee, Thomas and Slenter, Denise and Stephan, Ralf and Thornton, Katherine and Tsueng, Ginger and Tu, Roger and Ul-Hasan, Sabah and Willighagen, Egon and Wu, Chunlei and Su, Andrew I.}, doi = {10.7554/elife.52614}, journal = {eLife}, month = {mar}, title = {Wikidata as a knowledge graph for the life sciences}, url = {https://doi.org/10.7554/elife.52614}, volume = {9}, year = {2020} } @article{Waltemath2020, abstract = {Abstract This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.}, author = {Waltemath, Dagmar and Golebiewski, Martin and Blinov, Michael L. and Gleeson, Padraig and Hermjakob, Henning and Hucka, Michael and Inau, Esther Thea and Keating, Sarah M. and König, Matthias and Krebs, Olga and Malik-Sheriff, Rahuman S. and Nickerson, David and Oberortner, Ernst and Sauro, Herbert M. and Schreiber, Falk and Smith, Lucian and Stefan, Melanie I. and Wittig, Ulrike and Myers, Chris J.}, doi = {10.1515/jib-2020-0005}, journal = {Journal of Integrative Bioinformatics}, month = {jun}, title = {The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)}, url = {https://doi.org/10.1515/jib-2020-0005}, volume = {17}, year = {2020} } @article{Wolkenhauer2020, author = {Wolkenhauer, Olaf and Ostaszewski, Marek and Mazein, Alexander and Gillespie, Marc E. and Kuperstein, Inna and Niarakis, Anna and Hermjakob, Henning and Schreiber, Falk and Dräger, Andreas and Pico, Alexander R. and Demir, Emek and Willighagen, Egon L. and Furlong, Laura I. and Evelo, Chris T. and Barillot, Emmanuel and Dopazo, Joaquin and Hasenauer, Jan and Orta-Resendiz, Aurelio and Messina, Francesco and Valencia, Alfonso and Funahashi, Akira and Kitano, Hiroaki and Auffray, Charles and Balling, Rudi and Schneider, Reinhard}, doi = {10.1038/s41597-020-0477-8}, journal = {Scientific Data}, month = {may}, title = {COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms}, url = {https://doi.org/10.1038/s41597-020-0477-8}, volume = {7}, year = {2020} }