@article{De Groot2007, abstract = {Genetic polymorphisms in class I human leukocyte antigen molecules (HLA) have been shown to determine susceptibility to HIV infection as well as the rate of progression to AIDS. In particular, the HLA-B7 supertype has been shown to be associated with high viral loads and rapid progression to disease. Using a multiplatform in silico/in vitro approach, we have prospectively identified 45 highly conserved, putative HLA-B7 restricted HIV CTL epitopes and evaluated them in HLA binding and ELISpot assays. All 45 epitopes (100%) bound to HLA-B7 in cell-based HLA binding assays: 28 (62%) bound with high affinity, 6 (13%) peptides bound with medium affinity and 11 (24%) bound with low affinity. Forty of the 45 peptides (88%) stimulated a IFN-gamma response in PBMC from at least one subject. Eighteen of these 40 epitopes have not been previously described; an additional eight epitopes have not been previously described as restricted by B7. The HLA-B7 restricted epitopes discovered using this in silico screening approach are highly conserved across strains and clades of HIV as well as conserved in the HIV genome over the 20 years since HIV-1 isolates were first sequenced. This study demonstrates that it is possible to select a broad range of HLA-B7 restricted epitopes that comprise stable elements in the rapidly mutating HIV genome. The most immunogenic of these epitopes will be included in the GAIA multi-epitope vaccine.}, author = {De Groot, Anne S. and Rivera, Daniel S. and McMurry, Julie A. and Buus, Soren and Martin, William}, doi = {10.1016/j.vaccine.2007.12.004}, journal = {Vaccine}, month = {dec}, pages = {3059-3071}, title = {Identification of immunogenic HLA-B7 “Achilles' heel” epitopes within highly conserved regions of HIV}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18206276}, volume = {26}, year = {2007} } @article{De Groot2008, abstract = {We have identified at least 2 highly promiscuous major histocompatibility complex class II T-cell epitopes in the Fc fragment of IgG that are capable of specifically activating CD4(+)CD25(Hi)FoxP3(+) natural regulatory T cells (nT(Regs)). Coincubation of these regulatory T-cell epitopes or "Tregitopes" and antigens with peripheral blood mononuclear cells led to a suppression of effector cytokine secretion, reduced proliferation of effector T cells, and caused an increase in cell surface markers associated with T(Regs) such as FoxP3. In vivo administration of the murine homologue of the Fc region Tregitope resulted in suppression of immune response to a known immunogen. These data suggest that one mechanism for the immunosuppressive activity of IgG, such as with IVIG, may be related to the activity of regulatory T cells. In this model, regulatory T-cell epitopes in IgG activate a subset of nT(Regs) that tips the resulting immune response toward tolerance rather than immunogenicity.}, author = {De Groot, Anne S. and Moise, Leonard and McMurry, Julie A. and Wambre, Erik and Van Overtvelt, Laurence and Moingeon, Philippe and Scott, David W. and Martin, William}, doi = {10.1182/blood-2008-02-138073}, journal = {Blood}, month = {oct}, pages = {3303-3311}, title = {Activation of natural regulatory T cells by IgG Fc-derived peptide "Tregitopes"}, url = {http://ashpublications.org/blood/article-pdf/112/8/3303/1308169/zh802008003303.pdf}, volume = {112}, year = {2008} } @article{Degroot2008, author = {Degroot, A. and Mcmurry, J. and Moise, L.}, doi = {10.1016/j.coph.2008.08.002}, journal = {Current Opinion in Pharmacology}, month = {oct}, pages = {620-626}, title = {Prediction of immunogenicity: in silico paradigms, ex vivo and in vivo correlates}, url = {https://oadoi.org/10.1016/j.coph.2008.08.002}, volume = {8}, year = {2008} } @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{Gargano2023, abstract = {Abstract The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.}, author = {Gargano, Michael A. and Matentzoglu, Nicolas and Coleman, Ben and Addo-Lartey, Eunice B. and Anagnostopoulos, Anna V. and Anderton, Joel and Avillach, Paul and Bagley, Anita M. and Bakštein, Eduard and Balhoff, James P. and Baynam, Gareth and Bello, Susan M. and Berk, Michael and Bertram, Holli and Bishop, Somer and Blau, Hannah and Bodenstein, David F. and Botas, Pablo and Boztug, Kaan and Čady, Jolana and Callahan, Tiffany J. and Cameron, Rhiannon and Carbon, Seth J. and Castellanos, Francisco and Caufield, J. Harry and Chan, Lauren E. and Chute, Christopher G. and Cruz-Rojo, Jaime and Dahan-Oliel, Noémi and Davids, Jon R. and de Dieuleveult, Maud and de Souza, Vinicius and de Vries, Bert B. A. and de Vries, Esther and DePaulo, J. Raymond and Derfalvi, Beata and Dhombres, Ferdinand and Diaz-Byrd, Claudia and Dingemans, Alexander J. M. and Donadille, Bruno and Duyzend, Michael and Elfeky, Reem and Essaid, Shahim and Fabrizzi, Carolina and Fico, Giovanna and Firth, Helen V. and Freudenberg-Hua, Yun and Fullerton, Janice M. and Gabriel, Davera L. and Gilmour, Kimberly and Giordano, Jessica and Goes, Fernando S. and Moses, Rachel Gore and Green, Ian and Griese, Matthias and Groza, Tudor and Gu, Weihong and Guthrie, Julia and Gyori, Benjamin and Hamosh, Ada and Hanauer, Marc and Hanušová, Kateřina and He, Yongqun (Oliver) and Hegde, Harshad and Helbig, Ingo and Holasová, Kateřina and Hoyt, Charles Tapley and Huang, Shangzhi and Hurwitz, Eric and Jacobsen, Julius O. B. and Jiang, Xiaofeng and Joseph, Lisa and Keramatian, Kamyar and King, Bryan and Knoflach, Katrin and Koolen, David A. and Kraus, Megan L. and Kroll, Carlo and Kusters, Maaike and Ladewig, Markus S. and Lagorce, David and Lai, Meng-Chuan and Lapunzina, Pablo and Laraway, Bryan and Lewis-Smith, David and Li, Xiarong and Lucano, Caterina and Majd, Marzieh and Marazita, Mary L. and Martinez-Glez, Victor and McHenry, Toby H. and McInnis, Melvin G. and McMurry, Julie A. and Mihulová, Michaela and Millett, Caitlin E. and Mitchell, Philip B. and Moslerová, Veronika and Narutomi, Kenji and Nematollahi, Shahrzad and Nevado, Julian and Nierenberg, Andrew A. and Čajbiková, Nikola Novák and Nurnberger, John I. and Ogishima, Soichi and Olson, Daniel and Ortiz, Abigail and Pachajoa, Harry and Perez de Nanclares, Guiomar and Peters, Amy and Putman, Tim and Rapp, Christina K. and Rath, Ana and Reese, Justin and Rekerle, Lauren and Roberts, Angharad M. and Roy, Suzy and Sanders, Stephan J. and Schuetz, Catharina and Schulte, Eva C. and Schulze, Thomas G. and Schwarz, Martin and Scott, Katie and Seelow, Dominik and Seitz, Berthold and Shen, Yiping and Similuk, Morgan N. and Simon, Eric S. and Singh, Balwinder and Smedley, Damian and Smith, Cynthia L. and Smolinsky, Jake T. and Sperry, Sarah and Stafford, Elizabeth and Stefancsik, Ray and Steinhaus, Robin and Strawbridge, Rebecca and Sundaramurthi, Jagadish Chandrabose and Talapova, Polina and Tenorio Castano, Jair A. and Tesner, Pavel and Thomas, Rhys H. and Thurm, Audrey and Turnovec, Marek and van Gijn, Marielle E. and Vasilevsky, Nicole A. and Vlčková, Markéta and Walden, Anita and Wang, Kai and Wapner, Ron and Ware, James S. and Wiafe, Addo A. and Wiafe, Samuel A. and Wiggins, Lisa D. and Williams, Andrew E. and Wu, Chen and Wyrwoll, Margot J. and Xiong, Hui and Yalin, Nefize and Yamamoto, Yasunori and Yatham, Lakshmi N. and Yocum, Anastasia K. and Young, Allan H. and Yüksel, Zafer and Zandi, Peter P. and Zankl, Andreas and Zarante, Ignacio and Zvolský, Miroslav and Toro, Sabrina and Carmody, Leigh C. and Harris, Nomi L. and Munoz-Torres, Monica C. and Danis, Daniel and Mungall, Christopher J. and Köhler, Sebastian and Haendel, Melissa A. and Robinson, Peter N.}, doi = {10.1093/nar/gkad1005}, journal = {Nucleic Acids Research}, month = {nov}, pages = {D1333-D1346}, title = {The Human Phenotype Ontology in 2024: phenotypes around the world}, url = {https://doi.org/10.1093/nar/gkad1005}, volume = {52}, year = {2023} } @article{Gregory2009, abstract = {Francisella tularensis, the etiological agent of tularemia, is one of the most infectious bacterial pathogens known. No vaccine is currently approved for public use. Previously, we identified epitopes recognized specifically by T cells obtained from individuals following infection with F. tularensis. Here, we report that a subunit vaccine constructed based upon these epitopes elicited protective immunity in “humanized” HLA class II (DRB1*0401) transgenic mice. Vaccinated mice challenged intratracheally with a lethal dose of F. tularensis (Live Vaccine Strain) exhibited a rapid increase in pro-inflammatory cytokine production and diminished number of organisms in the lungs, and a concurrent increased rate of survival. These results demonstrate the efficacy of an epitope-based tularemia vaccine and suggest that such an approach might be widely applicable to the development of vaccines specific for intracellular bacterial pathogens.}, author = {Gregory, Stephen H. and Mott, Stephanie and Phung, Jennifer and Lee, Jinhee and Moise, Leonard and McMurry, Julie A. and Martin, William and De Groot, Anne S.}, doi = {10.1016/j.vaccine.2009.06.101}, journal = {Vaccine}, month = {jul}, pages = {5299-5306}, title = {Epitope-based Vaccination against Pneumonic Tularemia}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19616492}, volume = {27}, year = {2009} } @article{Knopf2008, abstract = {The C3d fragment of complement component C3 has been shown to enhance immune responses to antigens that lack T-cell epitopes such as bacterial polysaccharides. C3d binds to the B-cell complement receptor 2 (CR2 or CD21); this binding serves as a co-activation signal to the B cell when the polysaccharide antigen portion binds simultaneously to the B-cell receptor (surface Ig). Bringing together receptor-associated signal transduction molecules CD19 and Igalpha/beta, respectively, results in a lower threshold of activation. Paradoxically, C3d has also been shown to enhance antibody titers in the CD21 knockout (KO) mouse model as well as increase Th1 and Th2 cytokine secretion, suggesting that that an auxiliary CR2-independent pathway of immune activation may exist. We hypothesized that in addition to its molecular adjuvant property that enhances signal 1 during B-cell activation (co-signal 1), C3d also contains T-cell epitopes that are able to stimulate autoreactive C3d peptide-specific helper T cells which we term 'co-signal 2'. Using the EpiMatrix T-cell epitope-mapping algorithm, we identified 11 putative T-cell epitopes in C3d, a very high epitope density for a 302 amino-acid sequence. Eight of these epitope candidates were synthesized and shown to bind a variety of class II HLA-DR molecules of different haplotypes, and to stimulate C3d peptide-specific T cells to secrete pro-inflammatory cytokines in vitro. Further, we demonstrate a C3d-peptide specific increase in CD4(+) intracellular IFN-gamma(+) T cells in peripheral blood mononuclear cells (PBMCs) exposed to C3d peptides in vitro. We believe that the discovery of these autologous T cells autoreactive for C3d provides evidence supporting the 'co-signal 2' hypothesis and may offer a novel explanation of the CD21 KO paradox.}, author = {Knopf, Paul M. and Rivera, Daniel S. and Hai, Si-Han and McMurry, Julie and Martin, William and De Groot, Anne S.}, doi = {10.1038/sj.icb.7100147}, journal = {Immunology & Cell Biology}, month = {jan}, pages = {221-225}, title = {Novel function of complement C3d as an autologous helper T-cell target}, url = {https://www.researchgate.net/profile/Anne_De_Groot/publication/5669143_Novel_function_of_complement_C3d_as_an_autologous_helper_T-cell_target/links/00b49532a4dc7b0bff000000.pdf}, volume = {86}, year = {2008} } @article{Köhler2016, abstract = {Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology. Nucleic Acids Res 2017 Jan 4; 45(D1):D865-D876.}, author = {Köhler, Sebastian and Vasilevsky, Nicole A. and de Vries, Bert B. A. and Engelstad, Mark and Foster, Erin and McMurry, Julie and Aymé, Ségolène and Baynam, Gareth and Bello, Susan M. and Boerkoel, Cornelius F. and Boycott, Kym M. and Brudno, Michael and Schaefer, Franz and Buske, Orion J. and Scott, Richard H. and Chinnery, Patrick F. and Segal, Michael and Cipriani, Valentina and Sergouniotis, Panagiotis I. and Connell, Laureen E. and Sever, Richard and Dawkins, Hugh J. S. and Smith, Cynthia L. and DeMare, Laura E. and Straub, Volker and Devereau, Andrew D. and Thompson, Rachel and Turner, Catherine and Firth, Helen V. and Turro, Ernest and Freson, Kathleen and Veltman, Marijcke W. M. and Greene, Daniel and Vulliamy, Tom and Hamosh, Ada and Yu, Jing and Helbig, Ingo and von Ziegenweidt, Julie and Hum, Courtney and Zankl, Andreas and Jähn, Johanna A. and Züchner, Stephan and James, Roger and Zemojtel, Tomasz and Krause, Roland and F. Laulederkind, Stanley J. and Smedley, Damian and Lochmüller, Hanns and Lyon, Gholson J. and Ogishima, Soichi and Olry, Annie and Ouwehand, Willem H. and Pontikos, Nikolas and Rath, Ana and Jacobsen, Julius O. B. and Groza, Tudor and Mungall, Christopher J. and Haendel, Melissa and Mundlos, S. and Robinson, Peter N.}, doi = {10.1111/j.1399-0004.2010.01436.x}, journal = {Nucleic Acids Research}, month = {nov}, pages = {D865-D876}, title = {The Human Phenotype Ontology in 2017.}, url = {https://doi.org/10.1093/nar/gkw1039}, volume = {45}, year = {2016} } @article{Köhler2018, author = {Köhler, Sebastian and Carmody, Leigh and Vasilevsky, Nicole and Jacobsen, Julius O. B. and Danis, Daniel and Gourdine, Jean-Philippe and Gargano, Michael and Harris, Nomi L. and Matentzoglu, Nicolas and McMurry, Julie A. and Osumi-Sutherland, David and Cipriani, Valentina and Balhoff, James P. and Conlin, Tom and Blau, Hannah and Baynam, Gareth and Palmer, Richard and Gratian, Dylan and Dawkins, Hugh and Segal, Michael and Jansen, Anna C. and Muaz, Ahmed and Chang, Willie H. and Bergerson, Jenna and Laulederkind, Stanley J. F. and Yüksel, Zafer and Beltran, Sergi and Freeman, Alexandra F. and Sergouniotis, Panagiotis I. and Durkin, Daniel and Storm, Andrea L. and Hanauer, Marc and Brudno, Michael and Bello, Susan M. and Sincan, Murat and Rageth, Kayli and Wheeler, Matthew T. and Oegema, Renske and Lourghi, Halima and Della Rocca, Maria G. and Thompson, Rachel and Castellanos, Francisco and Priest, James and Cunningham-Rundles, Charlotte and Hegde, Ayushi and Lovering, Ruth C. and Hajek, Catherine and Olry, Annie and Notarangelo, Luigi and Similuk, Morgan and Zhang, Xingmin A. and Gómez-Andrés, David and Lochmüller, Hanns and Dollfus, Hélène and Rosenzweig, Sergio and Marwaha, Shruti and Rath, Ana and Sullivan, Kathleen and Smith, Cynthia and Milner, Joshua D. and Leroux, Dorothée and Boerkoel, Cornelius F. and Klion, Amy and Carter, Melody C. and Groza, Tudor and Smedley, Damian and Haendel, Melissa A. and Mungall, Chris and Robinson, Peter N.}, doi = {10.1093/nar/gky1105}, journal = {Nucleic Acids Research}, month = {nov}, pages = {D1018-D1027}, title = {Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources}, url = {https://doi.org/10.1093/nar/gky1105}, volume = {47}, year = {2018} } @article{McMurry2007, author = {McMurry, Julie and Kimball, Sarah and Hee Lee, Jin and Rivera, Daniel and Martin, William and Weiner, David and Kutzler, Michele and Sherman, David and Kornfeld, Hardy and De Groot, Anne}, doi = {10.2174/156652407780831584}, journal = {Current Molecular Medicine}, month = {jun}, pages = {351-363}, title = {Epitope-Driven TB Vaccine Development: A Streamlined Approach Using Immuno-Informatics, ELISpot Assays, and HLA Transgenic Mice}, url = {https://oadoi.org/10.2174/156652407780831584}, volume = {7}, year = {2007} } @article{Mcmurry2007, author = {Mcmurry, Julie}, month = {oct}, title = {Tularemia vaccines - an overview.}, year = {2007} } @article{McMurry2008, abstract = {Influenza A is an important cause of morbidity and mortality worldwide. In the United States alone influenza kills 30,000 to 50,000 people in a non-epidemic year and significantly more in an acute epidemic.(1) An emerging pandemic influenza virus, such as H5N1, could have a devastating economic and social impact. The Surgeon General estimates that at least 43 million Americans, especially those younger than 1 and older than 60, are at risk of death from influenza. Antigenically distinct influenza virus strains emerge regularly, mandating changes in influenza vaccine antigenic composition. Consequently, the immunity engendered by the conventional influenza vaccines is relevant only for a short time. However, by incorporating conserved influenza T cell epitopes, it may be possible to develop more immunogenic, broader-spectrum vaccines that may be efficacious over a longer period. This review summarizes the critical components of effective influenza vaccines, a rational vaccine design approach, and the pertinent influenza immunology.}, author = {McMurry, Julie A. and Johansson, Bert E. and De Groot, Anne S.}, doi = {10.4161/hv.4.2.5169}, journal = {Human Vaccines}, month = {mar}, pages = {148-157}, title = {A call to cellular & humoral arms: Enlisting cognate T cell help to develop broad-spectrum vaccines against influenza A}, url = {https://www.researchgate.net/profile/Anne_De_Groot/publication/5470822_A_call_to_cellular_humoral_arms_Enlisting_cognate_T_cell_help_to_develop_broad-spectrum_vaccines_against_influenza_A/links/0deec52c569348dd0b000000.pdf}, volume = {4}, year = {2008} } @article{Moise2008, abstract = {Computational methods accelerate vaccine development by rapid identification of potential vaccine candidates. We screened the Helicobacter pylori J99 and 26695 genomes for T-cell epitopes using the epitope mapping algorithm EpiMatrix and selected 150 sequences for experimental validation in a pre-clinical mouse model. Because strains of H. pylori that infect humans do not generally infect mice, and the sequence of the mouse-adapted "Sydney" strain (SS1) is not publicly available, we used targeted PCR to confirm that the epitopes we computationally predicted from the human H. pylori isolates J99 and 26695 are conserved in SS1. Epitopes conserved in SS1 were further analyzed for binding to MHC in vitro and for antigenicity in infected mice to select candidates for an epitope-based vaccine.}, author = {Moise, Leonard and McMurry, Julie A. and Pappo, Jacques and Lee, Dong-Soo and Moss, Steven F. and Martin, William D. and De Groot, Anne S.}, doi = {10.4161/hv.4.3.5394}, journal = {Human Vaccines}, month = {may}, pages = {219-223}, title = {Identification of genome-derived vaccine candidates conserved between human and mouse-adapted strains ofH. pylori}, url = {http://www.tandfonline.com/doi/pdf/10.4161/hv.4.3.5394?needAccess=true}, volume = {4}, year = {2008} } @article{Moise2009, abstract = {Epitopes shared by the vaccinia and variola viruses underlie the protective effect of vaccinia immunization against variola infection. We set out to identify a subset of cross-reactive epitopes using bioinformatics and immunological methods. Putative T-cell epitopes were computationally predicted from highly conserved open reading frames from seven complete vaccinia and variola genomes using EpiMatrix. Over 100 epitopes bearing low human sequence homology were selected and assessed in HLA binding assays and in T-cell antigenicity assays using PBMCs isolated from Dryvax-immunized subjects. This experimental validation of computational predictions illustrates the potential for immunoinformatics methods to identify candidate immunogens for a new, safer smallpox vaccine.}, author = {Moise, Leonard and McMurry, Julie A. and Buus, Soren and Frey, Sharon and Martin, William D. and De Groot, Anne S.}, doi = {10.1016/j.vaccine.2009.06.018}, journal = {Vaccine}, month = {jul}, pages = {6471-6479}, title = {In silico-accelerated identification of conserved and immunogenic variola/vaccinia T-cell epitopes}, url = {http://europepmc.org/articles/pmc2838212?pdf=render}, volume = {27}, year = {2009} } @article{Mungall2016, abstract = {The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype-phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype-phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species. Nucleic Acids Res 2017 Jan 4; 45(D1):D712-D722.}, author = {Mungall, Christopher J. and McMurry, Julie A. and Köhler, Sebastian and Balhoff, James P. and Borromeo, Charles and Brush, Matthew and Carbon, Seth and Conlin, Tom and Dunn, Nathan and Engelstad, Mark and Foster, Erin and Gourdine, J. P. and Jacobsen, Julius O. B. and Keith, Dan and Laraway, Bryan and Lewis, Suzanna E. and NguyenXuan, Jeremy and Shefchek, Kent and Vasilevsky, Nicole and Yuan, Zhou and Washington, Nicole and Hochheiser, Harry and Groza, Tudor and Smedley, Damian and Robinson, Peter N. and Haendel, Melissa A.}, doi = {10.1093/nar/gkw1128}, journal = {Nucleic Acids Research}, month = {nov}, pages = {D712-D722}, title = {The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species.}, url = {http://dx.doi.org/10.1093/nar/gkw1128}, volume = {45}, year = {2016} } @article{Petryszak2013, abstract = {Expression Atlas (http://www.ebi.ac.uk/gxa) is a value-added database providing information about gene, protein and splice variant expression in different cell types, organism parts, developmental stages, diseases and other biological and experimental conditions. The database consists of selected high-quality microarray and RNA-sequencing experiments from ArrayExpress that have been manually curated, annotated with Experimental Factor Ontology terms and processed using standardized microarray and RNA-sequencing analysis methods. The new version of Expression Atlas introduces the concept of ‘baseline’ expression, i.e. gene and splice variant abundance levels in healthy or untreated conditions, such as tissues or cell types. Differential gene expression data benefit from an in-depth curation of experimental intent, resulting in biologically meaningful ‘contrasts’, i.e. instances of differential pairwise comparisons between two sets of biological replicates. Other novel aspects of Expression Atlas are its strict quality control of raw experimental data, up-to-date RNA-sequencing analysis methods, expression data at the level of gene sets, as well as genes and a more powerful search interface designed to maximize the biological value provided to the user.}, author = {Petryszak, Robert and Burdett, Tony and Fiorelli, Benedetto and Fonseca, Nuno A. and Gonzalez-Porta, Mar and Hastings, Emma and Huber, Wolfgang and Jupp, Simon and Keays, Maria and Kryvych, Nataliya and McMurry, Julie and Marioni, John C. and Malone, James and Megy, Karine and Rustici, Gabriella and Tang, Amy Y. and Taubert, Jan and Williams, Eleanor and Mannion, Oliver and Parkinson, Helen E. and Brazma, Alvis}, doi = {10.1093/nar/gkt1270}, journal = {Nucleic Acids Research}, month = {dec}, pages = {D926-D932}, title = {Expression Atlas update—a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments}, url = {https://doi.org/10.1093/nar/gkt1270}, volume = {42}, year = {2013} } @article{Putman2023, abstract = {Abstract Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI’s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.}, author = {Putman, Tim E. and Schaper, Kevin and Matentzoglu, Nicolas and Rubinetti, Vincent P. and Alquaddoomi, Faisal S. and Cox, Corey and Caufield, J. Harry and Elsarboukh, Glass and Gehrke, Sarah and Hegde, Harshad and Reese, Justin T. and Braun, Ian and Bruskiewich, Richard M. and Cappelletti, Luca and Carbon, Seth and Caron, Anita R. and Chan, Lauren E. and Chute, Christopher G. and Cortes, Katherina G. and De Souza, Vinícius and Fontana, Tommaso and Harris, Nomi L. and Hartley, Emily L. and Hurwitz, Eric and Jacobsen, Julius O. B. and Krishnamurthy, Madan and Laraway, Bryan J. and McLaughlin, James A. and McMurry, Julie A. and Moxon, Sierra A. T. and Mullen, Kathleen R. and O’Neil, Shawn T. and Shefchek, Kent A. and Stefancsik, Ray and Toro, Sabrina and Vasilevsky, Nicole A. and Walls, Ramona L. and Whetzel, Patricia L. and Osumi-Sutherland, David and Smedley, Damian and Robinson, Peter N. and Mungall, Christopher J. and Haendel, Melissa A. and Munoz-Torres, Monica C.}, doi = {10.1093/nar/gkad1082}, journal = {Nucleic Acids Research}, month = {nov}, title = {The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species}, url = {https://doi.org/10.1093/nar/gkad1082}, year = {2023} } @article{Smedley2016, abstract = {The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.}, author = {Smedley, Damian and Schubach, Max and Jacobsen, Julius O. B. and Köhler, Sebastian and Zemojtel, Tomasz and Spielmann, Malte and Jäger, Marten and Hochheiser, Harry and Washington, Nicole L. and Mcmurry, Julie A. and Haendel, Melissa A. and Mungall, Christopher J. and Lewis, Suzanna E. and Groza, Tudor and Valentini, Giorgio and Robinson, Peter N.}, doi = {10.1016/j.ajhg.2016.07.005}, journal = {American Journal of Human Genetics}, month = {sep}, pages = {595-606}, title = {A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease}, url = {https://doi.org/10.1016/j.ajhg.2016.07.005}, volume = {99}, year = {2016} } @article{Wd2007, author = {Wd, Martin and Ds, Rivera and Ej, Carter and Moise, Lenny and Lee, J. and Mcmurry, Julie and Kornfeld, H. and As, De Groot}, month = {oct}, title = {Progress towards a genome-derived, epitope-driven vaccine for latent TB infection.}, year = {2007} } @article{Wimalaratne2017, abstract = {AbstractMost biomedical data repositories issue locally-unique accessions numbers, but do not provide globally unique, machine-resolvable, persistent identifiers for their datasets, as required by publishers wishing to implement data citation in accordance with widely accepted principles. Local accessions may however be prefixed with a namespace identifier, providing global uniqueness. Such “compact identifiers” have been widely used in biomedical informatics to support global resource identification with local identifier assignment. We report here on our project to provide robust support for machine-resolvable, persistent compact identifiers in biomedical data citation, by harmonizing the Identifiers.org and N2T.net (Name-To-Thing) meta-resolvers and extending their capabilities. Identifiers.org services hosted at the European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), and N2T.net services hosted at the California Digital Library (CDL), can now resolve any given identifier from over 600 source databases to its original source on the Web, using a common registry of prefix-based redirection rules. We believe these services will be of significant help to publishers and others implementing persistent, machine-resolvable citation of research data.}, author = {Wimalaratne, Sarala M. and Juty, Nick and Kunze, John and Janée, Greg and McMurry, Julie A. and Beard, Niall and Jimenez, Rafael and Grethe, Jeffrey S. and Hermjakob, Henning and Martone, Maryann E. and Clark, Tim}, doi = {10.1038/sdata.2018.29}, journal = {Scientific Data}, month = {jan}, title = {Uniform Resolution of Compact Identifiers for Biomedical Data}, url = {https://www.nature.com/articles/sdata201829.pdf}, volume = {5}, year = {2017} }