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1. Matching disease and phenotype ontologies in the ontology alignment evaluation initiative

Author

Ian Harrow, Ernesto Jiménez-Ruiz, Andrea Splendiani, Martin Romacker, Peter Woollard, Scott Markel, Yasmin Alam-Faruque, Martin Koch, James Malone, and Arild Waaler

Subjects
Biomedical ontology, Ontology alignment, OAE, Evaluation, Phenotype, Disease, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Background The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease. Results Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results. Conclusions Four systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype.

Published
2017
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2. Representing kidney development using the gene ontology.

Author

Yasmin Alam-Faruque, David P Hill, Emily C Dimmer, Midori A Harris, Rebecca E Foulger, Susan Tweedie, Helen Attrill, Douglas G Howe, Stephen Randall Thomas, Duncan Davidson, Adrian S Woolf, Judith A Blake, Christopher J Mungall, Claire O'Donovan, Rolf Apweiler, and Rachael P Huntley

Subjects
Medicine, Science
Abstract

Gene Ontology (GO) provides dynamic controlled vocabularies to aid in the description of the functional biological attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). Here we describe collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development. In the associated annotation activity, the new and revised terms were associated with gene products involved in renal development and function. This project resulted in a total of 522 GO terms being added to the ontology and the creation of approximately 9,600 kidney-related GO term associations to 940 UniProt Knowledgebase (UniProtKB) entries, covering 66 taxonomic groups. We demonstrate the impact of these improvements on the interpretation of GO term analyses performed on genes differentially expressed in kidney glomeruli affected by diabetic nephropathy. In summary, we have produced a resource that can be utilized in the interpretation of data from small- and large-scale experiments investigating molecular mechanisms of kidney function and development and thereby help towards alleviating renal disease.

Published
2014
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3. The impact of focused Gene Ontology curation of specific mammalian systems.

Author

Yasmin Alam-Faruque, Rachael P Huntley, Varsha K Khodiyar, Evelyn B Camon, Emily C Dimmer, Tony Sawford, Maria J Martin, Claire O'Donovan, Philippa J Talmud, Peter Scambler, Rolf Apweiler, and Ruth C Lovering

Subjects
Medicine, Science
Abstract

The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome.GO ANNOTATION DATA IS FREELY AVAILABLE FROM: ftp://ftp.geneontology.org/pub/go/gene-associations/

Published
2011
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6. Introducing the disease and phenotype OAEI track.

Author

Ian Harrow, Ernesto Jiménez-Ruiz, Andrea Splendiani, Martin Romacker, Stefan Negru, Peter Woollard, Scott Markel, Yasmin Alam-Faruque, Martin Koch 0001, Erfan Younesi, and James Malone

Published
2016

7. The UniProt-GO Annotation database in 2011.

Author

Emily C. Dimmer, Rachael P. Huntley, Yasmin Alam-Faruque, Tony Sawford, Claire O'Donovan, Maria Jesus Martin, Benoit Bely, Paul Browne, Wei Mun Chan, Ruth Y. Eberhardt, Michael Gardner, Kati Laiho, Duncan Legge, Michele Magrane, Klemens Pichler, Diego Poggioli, Harminder Sehra, Andrea H. Auchincloss, Kristian B. Axelsen, Marie-Claude Blatter, Emmanuel Boutet, Silvia Braconi-Quintaje, Lionel Breuza, Alan J. Bridge, Elisabeth Coudert, Anne Estreicher, Maria Livia Famiglietti, Serenella Ferro-Rojas, Marc Feuermann, Arnaud Gos, Nadine Gruaz-Gumowski, Ursula Hinz, Chantal Hulo, Janet James, Silvia Jimenez, Florence Jungo, Guillaume Keller, Phillippe Lemercier, Damien Lieberherr, Patrick Masson, Madelaine Moinat, Ivo Pedruzzi, Sylvain Poux, Catherine Rivoire, Bernd Roechert, Michel Schneider, Andre Stutz, Shyamala Sundaram, Michael Tognolli, Lydie Bougueleret, Ghislaine Argoud-Puy, Isabelle Cusin, Paula Duek Roggli, Ioannis Xenarios, and Rolf Apweiler

Published
2012
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8. The IntAct molecular interaction database in 2010.

Author

Bruno Aranda, P. Achuthan, Yasmin Alam-Faruque, Irina M. Armean, Alan J. Bridge, C. Derow, Marc Feuermann, A. T. Ghanbarian, Samuel Kerrien, Jyoti Khadake, Jules Kerssemakers, C. Leroy, Michael P. Menden, Magali Michaut, Luisa Montecchi-Palazzi, S. N. Neuhauser, Sandra E. Orchard, V. Perreau, Bernd Roechert, K. van Eijk, and Henning Hermjakob

Published
2010
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9. IntAct - open source resource for molecular interaction data.

Author

Samuel Kerrien, Yasmin Alam-Faruque, Bruno Aranda, I. Bancarz, Alan J. Bridge, C. Derow, Emily Dimmer, Marc Feuermann, A. Friedrichsen, Rachael P. Huntley, C. Kohler, Jyoti Khadake, C. Leroy, A. Liban, C. Lieftink, Luisa Montecchi-Palazzi, Sandra E. Orchard, Judith E. Risse, Karine Robbe, Bernd Roechert, David Thorneycroft, Y. Zhang, Rolf Apweiler, and Henning Hermjakob

Published
2007
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11. Representing kidney development using the gene ontology

Author

Emily Dimmer, Judith A. Blake, Claire O'Donovan, Rachael P. Huntley, Helen Attrill, David P. Hill, Duncan Davidson, Adrian S. Woolf, Rebecca E. Foulger, Rolf Apweiler, Christopher J. Mungall, Yasmin Alam-Faruque, Stephen Randall Thomas, Midori A. Harris, Douglas G. Howe, Susan Tweedie, Harris, Midori [0000-0003-4148-4606], and Apollo - University of Cambridge Repository

Subjects
Proteomics, Organogenesis, Statistics as Topic, Gene Expression, lcsh:Medicine, Ontology (information science), Bioinformatics, Kidney, Biochemistry, Computer Applications, Database and Informatics Methods, Mice, Databases, Genetic, Taxonomic rank, Database Searching, lcsh:Science, Databases, Protein, Data Management, Multidisciplinary, Proteomic Databases, Controlled Vocabularies, Molecular Sequence Annotation, medicine.anatomical_structure, Anatomy, UniProt, Sequence Analysis, Research Article, Computer and Information Sciences, Sequence Databases, Computational biology, Biology, Research and Analysis Methods, Kidney Development, Annotation, Species Specificity, Controlled vocabulary, Genetics, medicine, Animals, Humans, Molecular Biology Techniques, Sequencing Techniques, Molecular Biology, Taxonomy, lcsh:R, Biology and Life Sciences, Kidney metabolism, Renal System, Biological Databases, Gene Ontology, lcsh:Q, Gene Function, Organism Development, Developmental Biology
Abstract

Gene Ontology (GO) provides dynamic controlled vocabularies to aid in the description of the functional biological attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). Here we describe collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development. In the associated annotation activity, the new and revised terms were associated with gene products involved in renal development and function. This project resulted in a total of 522 GO terms being added to the ontology and the creation of approximately 9,600 kidney-related GO term associations to 940 UniProt Knowledgebase (UniProtKB) entries, covering 66 taxonomic groups. We demonstrate the impact of these improvements on the interpretation of GO term analyses performed on genes differentially expressed in kidney glomeruli affected by diabetic nephropathy. In summary, we have produced a resource that can be utilized in the interpretation of data from small- and large-scale experiments investigating molecular mechanisms of kidney function and development and thereby help towards alleviating renal disease.

Published
2020
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13. Matching disease and phenotype ontologies in the ontology alignment evaluation initiative

Author

Yasmin Alam-Faruque, Martin Koch, Arild Waaler, Ernesto Jiménez-Ruiz, Martin Romacker, Andrea Splendiani, Scott Markel, James Malone, Ian Harrow, and Peter Woollard

Subjects
0301 basic medicine, Matching (statistics), Consensus, Ontology alignment, Computer Networks and Communications, Computer science, Health Informatics, 02 engineering and technology, Disease, lcsh:Computer applications to medicine. Medical informatics, computer.software_genre, QH301, 03 medical and health sciences, Semantic similarity, 0202 electrical engineering, electronic engineering, information engineering, Humans, Semantic integration, Evaluation, Equivalence (measure theory), business.industry, Research, OAE, Computer Science Applications, Biomedical ontology, 030104 developmental biology, Workflow, Phenotype, Biological Ontologies, lcsh:R858-859.7, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Natural language processing, Information Systems, Data integration
Abstract

Background The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease. Results Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results. Conclusions Four systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype.

Published
2017

14. The UniProt-GO Annotation database in 2011

Author

Rachael P. Huntley, Florence Jungo, Tony Sawford, Harminder Sehra, Shyamala Sundaram, Phillippe Lemercier, Bernd Roechert, Silvia Braconi-Quintaje, Guillaume Keller, Madelaine Moinat, Sylvain Poux, Paul Browne, Rolf Apweiler, Kristian B. Axelsen, Kati Laiho, Andre Stutz, Janet James, Catherine Rivoire, Diego Poggioli, Wei Mun Chan, Ivo Pedruzzi, Anne Estreicher, Silvia Jimenez, Paula Duek Roggli, Michael Gardner, Serenella Ferro-Rojas, Claire O'Donovan, Patrick Masson, Maria Jesus Martin, Michele Magrane, Elizabeth Coudert, Benoit Bely, Michael Tognolli, Emmanuel Boutet, Isabelle Cusin, Andrea H. Auchincloss, Yasmin Alam-Faruque, Klemens Pichler, Nadine Gruaz-Gumowski, Chantal Hulo, Ursula Hinz, Ruth Y. Eberhardt, Duncan Legge, L Famiglietti, E. Dimmer, Michael Schneider, Marc Feuermann, Lionel Breuza, Arnaud Gos, Damien Lieberherr, Ghislaine Argoud-Puy, Marie-Claude Blatter, Lydie Bougueleret, Ioannis Xenarios, and Alan Bridge

Subjects
0303 health sciences, business.industry, Databases, Protein, Molecular Sequence Annotation/standards, Vocabulary, Controlled, Molecular Sequence Annotation, Articles, Biology, File format, Bioinformatics, World Wide Web, 03 medical and health sciences, Consistency (database systems), Annotation, 0302 clinical medicine, 030220 oncology & carcinogenesis, Controlled vocabulary, Genetics, Data set (IBM mainframe), The Internet, UniProt, business, 030304 developmental biology
Abstract

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360,000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set.

Published
2017

15. Activities at the Universal Protein Resource (UniProt)

Author

Borisas Bursteinas, L-S Yeh, Klemens Pichler, Guillaume Keller, C Casal-Casas, Gerritsen, A Da Silva, Prudence Mutowo, Xavier D. Martin, Peter B. McGarvey, Volynkin, J Lew, K Sonesson, M. C. Blatter, L Pureza, Rodrigo Lopez, Anne Estreicher, Alex Bateman, Ramona Britto, A-L Veuthey, Penelope Garmiri, Patrick Masson, Baris E. Suzek, Cathy H. Wu, Paul Gane, Maria Jesus Martin, Shyamala Sundaram, Yasmin Alam-Faruque, Ioannis Xenarios, Alan Bridge, WM Chan, Guoying Qi, L Famiglietti, Christian J. A. Sigrist, Nadine Gruaz-Gumowski, Chantal Hulo, Hermann Zellner, Matthew Corbett, Monica Pozzato, Thierry Lombardot, S Staehli, Jerven Bolleman, Sylvain Poux, Séverine Duvaud, Tony Sawford, Chuming Chen, Xavier Watkins, Yuqi Wang, Elisabeth Gasteiger, Kati Laiho, Qinghua Wang, M Bingley, Lydie Bougueleret, Kristian B. Axelsen, Yongxing Chen, Salvo Paesano, Andrew Nightingale, Nicole Redaschi, Emma Hatton-Ellis, John S. Garavelli, Delphine Baratin, Darren A. Natale, Gayatri Chavali, E de Castro, Leslie Arminski, Damien Lieberherr, Elena Cibrian-Uhalte, Catherine Rivoire, Bernd Roechert, Yerramalla, Hongzhan Huang, Alistair MacDougall, Edd Turner, Maria Victoria Schneider, Aleksandra Shypitsyna, Jie Zhang, Michele Magrane, Dolnide Dornevil, Lara, M Donnelly, F. Fazzini, Andrea H. Auchincloss, Emanuele Alpi, Benoit Bely, Brigitte Boeckmann, Rolf Apweiler, Rachael P. Huntley, Lionel Breuza, Sangya Pundir, Lorenzo Cerutti, Tunca Doğan, EB Casanova, P van Rensburg, Rabie Saidi, Jie Luo, Mickael Goujon, Laure Verbregue, Tony Wardell, W Liu, Andre Stutz, P Lemercier, Hamish McWilliam, Ivo Pedruzzi, P-A Binz, Sebastien Gehant, Béatrice A. Cuche, Carlos Bonilla, Duncan Legge, C. R. Vinayaka, Anne Morgat, Diego Poggioli, J Arganiska, Teresa Batista Neto, Emmanuel Boutet, Florence Jungo, Ursula Hinz, Parit Bansal, Sandra Orchard, Ricardo Antunes, Manuela Pruess, M Feuermann, S. Rosanoff, Claire O'Donovan, Cecilia N. Arighi, J. James, M Doche, Elisabeth Coudert, M De Giorgi, Reija Hieta, Sandrine Pilbout, Arnaud Gos, Michael Tognolli, Leyla Jael Garcia Castro, and Lucila Aimo

Subjects
Proteomics, 0303 health sciences, Internet, Representation (systemics), Genome database, Proteins, Molecular Sequence Annotation, Computational biology, Genomics, Biology, Ontology (information science), Universal Protein Resource, II. Protein sequence and structure, motifs and domains, 03 medical and health sciences, Annotation, 0302 clinical medicine, Gene Ontology, Sequence Analysis, Protein, 030220 oncology & carcinogenesis, Genetics, UniProt, Databases, Protein, 030304 developmental biology
Abstract

The mission of the Universal Protein Resource (UniProt) (http://www.uniprot.org) is to provide the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation. It integrates, interprets and standardizes data from literature and numerous resources to achieve the most comprehensive catalog possible of protein information. The central activities are the biocuration of the UniProt Knowledgebase and the dissemination of these data through our Web site and web services. UniProt is produced by the UniProt Consortium, which consists of groups from the European Bioinformatics Institute (EBI), the SIB Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is updated and distributed every 4 weeks and can be accessed online for searches or downloads.

Published
2013

16. The Gene Ontology: enhancements for 2011

Author

P D'Eustachio, Benjamin C. Hitz, Julie Park, Paul Browne, Douglas G. Howe, Cynthia J. Krieger, Kalpana Karra, Stan Laulederkind, Karen R. Christie, Susan Tweedie, Eurie L. Hong, Lydie Bougueleret, Michele Magrane, Cathy R. Gresham, Rolf Apweiler, Lisa Matthews, Dong Li, Philippa J. Talmud, Ioannis Xenarios, J. M. Cherry, Tanya Z. Berardini, Deborah A. Siegele, Rama Balakrishnan, D. Sitnikov, A. Auchinchloss, Selina S. Dwight, Tony Sawford, Paul J. Kersey, Ruth C. Lovering, Ruth Y. Eberhardt, Ursula Hinz, Lakshmi Pillai, Sylvain Poux, Edith D. Wong, Klemens Pichler, Kati Laiho, Malcolm J. Gardner, Stephen G. Oliver, Lionel Breuza, Kara Dolinski, P Lemercier, Kristian B. Axelsen, Midori A. Harris, Adrienne E. Zweifel, H. Drabkin, Guillaume Keller, Marek S. Skrzypek, Daniel M. Staines, Fiona M. McCarthy, Nicholas H. Brown, Mark D. McDowall, Antonia Lock, Mary Shimoyama, Maria C. Costanzo, Teresia Buza, S. Jimenez, Rex L. Chisholm, Paul W. Sternberg, Hui Wang, Nadine Gruaz-Gumowski, Chantal Hulo, Rebecca E. Foulger, Melinda R. Dwinell, Judith A. Blake, Marcus C. Chibucos, B. K. McIntosh, C. D. Amundsen, Jane Lomax, L Famiglietti, Tom Hayman, Michael Tognolli, Eva Huala, James C. Hu, Patrick Masson, Maria Jesus Martin, Benoit Bely, Shuai Weng, Heather C. Wick, E. Dimmer, L. Ni, Catherine Rivoire, Christopher J. Mungall, H. Sehra, P. Duek-Roggli, Maria Victoria Schneider, Dianna G. Fisk, Michael S. Livstone, Ivo Pedruzzi, Shyamala Sundaram, Donna K. Slonim, Isabelle Cusin, Stuart R. Miyasato, Timothy F. Lowry, Varsha K. Khodiyar, Seth Carbon, Elisabeth Coudert, Jürg Bähler, Juancarlos Chan, Evelyn Camon, Daniel P. Renfro, Anne Estreicher, M. C. Blatter, Robert S. Nash, P Gaudet, Sven Heinicke, K. Van Auken, Stacia R. Engel, Alan Bridge, Ralf Stephan, Mary E. Dolan, Shane C. Burgess, Petra Fey, Shur-Jen Wang, Damien Lieberherr, Duncan Legge, P. Porras Millán, Andre Stutz, Yasmin Alam-Faruque, Gail Binkley, Bernd Roechert, S. Branconi-Quintaje, Ghislaine Argoud-Puy, S. Basu, Kim Rutherford, M. Moinat, Monte Westerfield, Arnaud Gos, Eleanor J Stanley, Valerie Wood, Ranjana Kishore, Diego Poggioli, S. Ferro-Rojas, Victoria Petri, Florence Jungo, Suzanna E. Lewis, Emmanuel Boutet, Warren A. Kibbe, M Feuermann, Claire O'Donovan, W. M. Chan, J. James, David P. Hill, Rachael P. Huntley, M. Gwinn Giglio, Paul Thomas, Jodi E. Hirschman, Paola Roncaglia, Gene Ontology Consortium, Blake, JA., Dolan, M., Drabkin, H., Hill, DP., Ni, L., Sitnikov, D., Burgess, S., Buza, T., Gresham, C., McCarthy, F., Pillai, L., Wang, H., Carbon, S., Lewis, SE., Mungall, CJ., Gaudet, P., Chisholm, RL., Fey, P., Kibbe, WA., Basu, S., Siegele, DA., McIntosh, BK., Renfro, DP., Zweifel, AE., Hu, JC., Brown, NH., Tweedie, S., Alam-Faruque, Y., Apweiler, R., Auchinchloss, A., Axelsen, K., Argoud-Puy, G., Bely, B., Blatter, M-., Bougueleret, L., Boutet, E., Branconi, S., Breuza, L., Bridge, A., Browne, P., Chan, WM., Coudert, E., Cusin, I., Dimmer, E., Duek-Roggli, P., Eberhardt, R., Estreicher, A., Famiglietti, L., Ferro-Rojas, S., Feuermann, M., Gardner, M., Gos, A., Gruaz-Gumowski, N., Hinz, U., Hulo, C., Huntley, R., James, J., Jimenez, S., Jungo, F., Keller, G., Laiho, K., Legge, D., Lemercier, P., Lieberherr, D., Magrane, M., Martin, MJ., Masson, P., Moinat, M., O'Donovan, C., Pedruzzi, I., Pichler, K., Poggioli, D., Porras Millán, P., Poux, S., Rivoire, C., Roechert, B., Sawford, T., Schneider, M., Sehra, H., Stanley, E., Stutz, A., Sundaram, S., Tognolli, M., Xenarios, I., Foulger, R., Lomax, J., Roncaglia, P., Camon, E., Khodiyar, VK., Lovering, RC., Talmud, PJ., Chibucos, M., Gwinn Giglio, M., Dolinski, K., Heinicke, S., Livstone, MS., Stephan, R., Harris, MA., Oliver, SG., Rutherford, K., Wood, V., Bahler, J., Lock, A., Kersey, PJ., McDowall, MD., Staines, DM., Dwinell, M., Shimoyama, M., Laulederkind, S., Hayman, T., Wang, S-., Petri, V., Lowry, T., D'Eustachio, P., Matthews, L., Amundsen, CD., Balakrishnan, R., Binkley, G., Cherry, JM., Christie, KR., Costanzo, MC., Dwight, SS., Engel, SR., Fisk, DG., Hirschman, JE., Hitz, BC., Hong, EL., Karra, K., Krieger, CJ., Miyasato, SR., Nash, RS., Park, J., Skrzypek, MS., Weng, S., Wong, ED., Berardini, TZ., Li, D., Huala, E., Slonim, D., Wick, H., Thomas, P., Chan, J., Kishore, R., Sternberg, P., Van Auken, K., Howe, D., and Westerfield, M.

Subjects
Quality Control, 0303 health sciences, media_common.quotation_subject, Databases, Genetic, Molecular Sequence Annotation/standards, Vocabulary, Controlled, Inference, Molecular Sequence Annotation, Articles, Biology, Ontology (information science), World Wide Web, Open Biomedical Ontologies, 03 medical and health sciences, Annotation, 0302 clinical medicine, Resource (project management), Controlled vocabulary, Genetics, Social media, Function (engineering), 030217 neurology & neurosurgery, 030304 developmental biology, media_common
Abstract

The Gene Ontology (GO) (http://www.geneontology.org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources.

Published
2011

17. IntAct--open source resource for molecular interaction data

Author

Judith Risse, Y. Zhang, Yasmin Alam-Faruque, A. Liban, Samuel Kerrien, Jyoti Khadake, Karine Robbe, Rolf Apweiler, Emily Dimmer, Bruno Aranda, David Thorneycroft, Rachael P. Huntley, Catherine Leroy, A. Friedrichsen, Bernd Roechert, Henning Hermjakob, Marc Feuermann, Alan Bridge, I. Bancarz, C. Derow, C. Kohler, Luisa Montecchi-Palazzi, C. Lieftink, and Sandra Orchard

Subjects
Quality Control, Source code, media_common.quotation_subject, Biology, Data submission, Bioinformatics, User-Computer Interface, 03 medical and health sciences, Software, Databases, Genetic, Protein Interaction Mapping, Controlled vocabulary, Genetics, 030304 developmental biology, media_common, Internet, 0303 health sciences, Information retrieval, Software suite, business.industry, 030302 biochemistry & molecular biology, Proteins, Articles, DNA, Resource (Windows), Protein Structure, Tertiary, Open source, Vocabulary, Controlled, RNA, business, Level of detail
Abstract

IntAct is an open source database and software suite for modeling, storing and analyzing molecular interaction data. The data available in the database originates entirely from published literature and is manually annotated by expert biologists to a high level of detail, including experimental methods, conditions and interacting domains. The database features over 126,000 binary interactions extracted from over 2100 scientific publications and makes extensive use of controlled vocabularies. The web site provides tools allowing users to search, visualize and download data from the repository. IntAct supports and encourages local installations as well as direct data submission and curation collaborations. IntAct source code and data are freely available from http://www.ebi.ac.uk/intact.

Published
2007

18. A method for increasing expressivity of Gene Ontology annotations using a compositional approach

Author

Tony Sawford, Midori A. Harris, Judith A. Blake, Ruth C. Lovering, Emily Dimmer, Christopher J. Mungall, David P. Hill, Seth Carbon, Prudence Mutowo-Meullenet, Yasmin Alam-Faruque, Valerie Wood, Antonia Lock, Jane Lomax, Kimberly Van Auken, Rebecca E. Foulger, Heiko Dietze, Varsha K. Khodiyar, Rachael P. Huntley, Harris, Midori [0000-0003-4148-4606], Wood, Valerie [0000-0001-6330-7526], and Apollo - University of Cambridge Repository

Subjects
Computer science, Association (object-oriented programming), media_common.quotation_subject, Context (language use), Ontology (information science), Manual curation, Biochemistry, Annotation, Structural Biology, Annotation extension, Humans, Function (engineering), Molecular Biology, media_common, Structure (mathematical logic), Information retrieval, Applied Mathematics, Methodology Article, Computational Biology, Proteins, Functional annotation, Molecular Sequence Annotation, Data structure, Computer Science Applications, Gene Ontology, DNA microarray
Abstract

BACKGROUND: The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations. RESULTS: The GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector-target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions. CONCLUSIONS: The additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism's gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction.

Published
2014

19. Use of Gene Ontology Annotation to understand the peroxisome proteome in humans

Author

Rachael P. Huntley, Tony Sawford, Maria Jesus Martin, Prudence Mutowo-Meullenet, Yasmin Alam-Faruque, E. Dimmer, Claire O'Donovan, and Rolf Apweiler

Subjects
Saccharomyces cerevisiae Proteins, Proteome, Saccharomyces cerevisiae, Computational biology, Biology, General Biochemistry, Genetics and Molecular Biology, Field (computer science), Terminology, Set (abstract data type), 03 medical and health sciences, Annotation, Species Specificity, Protein Interaction Mapping, Peroxisomes, Humans, Protein Isoforms, Databases, Protein, 030304 developmental biology, Genetics, 0303 health sciences, 030302 biochemistry & molecular biology, Molecular Sequence Annotation, Protein Transport, Organ Specificity, Original Article, UniProt, General Agricultural and Biological Sciences, Protein Binding, Information Systems, De facto standard
Abstract

The Gene Ontology (GO) is the de facto standard for the functional description of gene products, providing a consistent, information-rich terminology applicable across species and information repositories. The UniProt Consortium uses both manual and automatic GO annotation approaches to curate UniProt Knowledgebase (UniProtKB) entries. The selection of a protein set prioritized for manual annotation has implications for the characteristics of the information provided to users working in a specific field or interested in particular pathways or processes. In this article, we describe an organelle-focused, manual curation initiative targeting proteins from the human peroxisome. We discuss the steps taken to define the peroxisome proteome and the challenges encountered in defining the boundaries of this protein set. We illustrate with the use of examples how GO annotations now capture cell and tissue type information and the advantages that such an annotation approach provides to users. Database URL: http://www.ebi.ac.uk/GOA/ and http://www.uniprot.org.

Published
2013

20. The impact of focused Gene Ontology curation of specific mammalian systems

Author

Maria Jesus Martin, Emily Dimmer, Tony Sawford, Varsha K. Khodiyar, Evelyn Camon, Peter J. Scambler, Ruth C. Lovering, Philippa J. Talmud, Yasmin Alam-Faruque, Claire O'Donovan, Rachael P. Huntley, and Rolf Apweiler

Subjects
Proteomics, Microarrays, media_common.quotation_subject, Statistics as Topic, lcsh:Medicine, Computational biology, Biology, Bioinformatics, Kidney, 03 medical and health sciences, Annotation, 0302 clinical medicine, Resource (project management), Genome Analysis Tools, Controlled vocabulary, Databases, Genetic, Human proteome project, Animals, Humans, Function (engineering), lcsh:Science, 030304 developmental biology, media_common, Oligonucleotide Array Sequence Analysis, Mammals, 0303 health sciences, Multidisciplinary, Gene ontology, Proteomic Databases, Macrophages, Gene Ontologies, Bio-Ontologies, lcsh:R, High-Throughput Nucleotide Sequencing, Computational Biology, Molecular Sequence Annotation, Genomics, Mammalogy, ComputingMethodologies_PATTERNRECOGNITION, lcsh:Q, DNA microarray, Genome Expression Analysis, Zoology, 030217 neurology & neurosurgery, Research Article
Abstract

The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome. Availability GO annotation data is freely available from: ftp://ftp.geneontology.org/pub/go/gene-associations/

Published
2011

21. Ongoing and future developments at the Universal Protein Resource

Author

Vicente Lara, Arnaud Gos, Nikolas Pontikos, L. Bollondi, L Famiglietti, Barker Wc, Chuming Chen, Michael Tognolli, Patrick Masson, Salvo Paesano, Tony Sawford, Sandrine Pilbout, Jerven Bolleman, Jian Zhang, S Staehli, Elisabeth Gasteiger, David Binns, Ursula Hinz, Nadine Gruaz-Gumowski, Chantal Hulo, Maria Jesus Martin, M. Corbett, Veuthey Al, Paul Browne, Guillaume Keller, S. Jimenez, Roberts Nv, Brigitte Boeckmann, Natale Da, Suzek Be, Edward Turner, T. Kappler, Steven Rosanoff, Leslie Arminski, Florence Jungo, M Donnelly, P. Dubey, Ruth Y. Eberhardt, Lydie Bougueleret, Vivienne Baillie Gerritsen, Maria Victoria Schneider, Lionel Breuza, Isabelle Cusin, Delphine Baratin, E. Dimmer, Rachael P. Huntley, Julius O.B. Jacobsen, Arighi Cn, Quintaje Sb, Alan Bridge, Nicole Redaschi, Shyamala Sundaram, Peter B. McGarvey, Rebecca E. Foulger, Duncan Legge, Andre Stutz, Monica Pozzato, Raja Mazumder, Anne Morgat, M Doche, K Sonesson, Anne Estreicher, Blatter Mc, Manuela Pruess, Serenella Ferro, Séverine Duvaud, F. Fazzini, Rolf Apweiler, Natarajan Tg, E. Stanley, M Feuermann, Claire O'Donovan, J. James, Ivo Pedruzzi, Castro Lg, Wu Ch, M Bingley, W Liu, He Huang, Bernd Roechert, Laure Verbregue, Elisabeth Coudert, Ioannis Xenarios, M. Moinat, Sandra Orchard, P Lemercier, Damien Lieberherr, Ricardo Antunes, Ghislaine Argoud-Puy, Sebastien Gehant, Qiang Wang, Klemens Pichler, S. Patient, Nicolas Hulo, Diego Poggioli, J. Nchoutmboube, Emmanuel Boutet, H. Sehra, Chan Wm, Yasmin Alam-Faruque, M. Kleen, Van Rensburg P, Kati Laiho, Kristian B. Axelsen, John S. Garavelli, Christian J. A. Sigrist, Amos Marc Bairoch, Yongxing Chen, A. Fedotov, Yeh Ls, Sylvain Poux, Lynette Bower, Quan Lin, Daniel Barrell, Benoit Bely, U. Ugochukwu, Xavier D. Martin, Catherine Rivoire, E. Decastro, Jie Luo, Alain Gateau, Michele Magrane, Dolnide Dornevil, Vinayaka Cr, Andrea H. Auchincloss, Yuqi Wang, An algorithmic view on genomes, cells, and environments (BAMBOO), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), Institut de Microélectronique, Electromagnétisme et Photonique - Laboratoire d'Hyperfréquences et Caractérisation (IMEP-LAHC), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Institut de minéralogie et de physique des milieux condensés (IMPMC), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Lausanne (UNIL), Université Pierre et Marie Curie - Paris 6 (UPMC)-IPG PARIS-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), UniProt Consortium, Apweiler, R., Martin, MJ., O'Donovan, C., Magrane, M., Alam-Faruque, Y., Antunes, R., Barrell, D., Bely, B., Bingley, M., Binns, D., Bower, L., Browne, P., Chan, WM., Dimmer, E., Eberhardt, R., Fazzini, F., Fedotov, A., Foulger, R., Garavelli, J., Castro, LG., Huntley, R., Jacobsen, J., Kleen, M., Laiho, K., Legge, D., Lin, Q., Liu, W., Luo, J., Orchard, S., Patient, S., Pichler, K., Poggioli, D., Pontikos, N., Pruess, M., Rosanoff, S., Sawford, T., Sehra, H., Turner, E., Corbett, M., Donnelly, M., van Rensburg, P., Xenarios, I., Bougueleret, L., Auchincloss, A., Argoud-Puy, G., Axelsen, K., Bairoch, A., Baratin, D., Blatter, MC., Boeckmann, B., Bolleman, J., Bollondi, L., Boutet, E., Quintaje, SB., Breuza, L., Bridge, A., deCastro, E., Coudert, E., Cusin, I., Doche, M., Dornevil, D., Duvaud, S., Estreicher, A., Famiglietti, L., Feuermann, M., Gehant, S., Ferro, S., Gasteiger, E., Gateau, A., Gerritsen, V., Gos, A., Gruaz-Gumowski, N., Hinz, U., Hulo, C., Hulo, N., James, J., Jimenez, S., Jungo, F., Kappler, T., Keller, G., Lara, V., Lemercier, P., Lieberherr, D., Martin, X., Masson, P., Moinat, M., Morgat, A., Paesano, S., Pedruzzi, I., Pilbout, S., Poux, S., Pozzato, M., Redaschi, N., Rivoire, C., Roechert, B., Schneider, M., Sigrist, C., Sonesson, K., Staehli, S., Stanley, E., Stutz, A., Sundaram, S., Tognolli, M., Verbregue, L., Veuthey, AL., Wu, CH., Arighi, CN., Arminski, L., Barker, WC., Chen, C., Chen, Y., Dubey, P., Huang, H., Mazumder, R., McGarvey, P., Natale, DA., Natarajan, TG., Nchoutmboube, J., Roberts, NV., Suzek, BE., Ugochukwu, U., Vinayaka, CR., Wang, Q., Wang, Y., Yeh, LS., and Zhang, J.

Subjects
Proteomics, 0303 health sciences, Sequence database, 030302 biochemistry & molecular biology, Proteins, Articles, Computational biology, Biology, Bioinformatics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Systems Integration, Universal Protein Resource, 03 medical and health sciences, Information resource, Protein sequencing, Sequence Analysis, Protein, Metagenomics, UniProt Knowledgebase, Genetics, Databases, Protein/trends, Proteins/chemistry, Proteins/genetics, UniProt, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Databases, Protein, 030304 developmental biology
Abstract

International audience; The primary mission of Universal Protein Resource (UniProt) is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 4 weeks and can be accessed online for searches or download at http://www.uniprot.org.

Published
2011

22. The Renal Gene Ontology Annotation Initiative

Author

Rolf Apweiler, Peter J. Scambler, Claire O'Donovan, Emily Dimmer, Yasmin Alam-Faruque, and Rachael P. Huntley

Subjects
Transplantation, Embryology, Knowledge management, Views and Commentary, Gene ontology, business.industry, media_common.quotation_subject, Biomedical Engineering, Proteins, Molecular Sequence Annotation, Biology, Bioinformatics, Kidney, Annotation, Resource (project management), Controlled vocabulary, Animals, Humans, business, Function (engineering), Developmental Biology, Gene ontology annotation, media_common
Abstract

The Gene Ontology (GO) resource provides dynamic controlled vocabularies to aid in the description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). A renal-focused curation initiative, funded by Kidney Research UK and supported by the GO Consortium, has started at the European Bioinformatics Institute and aims to provide a detailed GO resource for mammalian proteins implicated in renal development and function. This report outlines the aims of this initiative and explains how the renal community can become involved to help improve the availability, quality and quantity of GO terms and their association to specific proteins.

Published
2010

23. The IntAct molecular interaction database in 2010

Author

Sandra Orchard, Henning Hermjakob, M Feuermann, P. Achuthan, Jyoti Khadake, K. van Eijk, Luisa Montecchi-Palazzi, Michael P. Menden, Avazeh T. Ghanbarian, Alan Bridge, Yasmin Alam-Faruque, Bernd Roechert, Victoria M. Perreau, Bruno Aranda, Irina M. Armean, C. Leroy, C. Derow, Samuel Kerrien, J. Kerssemakers, S. N. Neuhauser, and Magali Michaut

Subjects
Chemical and physical biology [NCMLS 7], Source code, Genetics and epigenetic pathways of disease [NCMLS 6], media_common.quotation_subject, Information Storage and Retrieval, Biology, computer.software_genre, User-Computer Interface, 03 medical and health sciences, Annotation, 0302 clinical medicine, Software, Databases, Genetic, Protein Interaction Mapping, Controlled vocabulary, Genetics, Animals, Humans, False Positive Reactions, Zoom, Databases, Protein, 030304 developmental biology, media_common, Internet, 0303 health sciences, Database, business.industry, Computational Biology, Proteins, Articles, Protein Structure, Tertiary, Open data, Vocabulary, Controlled, 030220 oncology & carcinogenesis, Programming Languages, The Internet, business, computer, Level of detail
Abstract

Contains fulltext : 185558.pdf (Publisher’s version ) (Open Access) IntAct is an open-source, open data molecular interaction database and toolkit. Data is abstracted from the literature or from direct data depositions by expert curators following a deep annotation model providing a high level of detail. As of September 2009, IntAct contains over 200.000 curated binary interaction evidences. In response to the growing data volume and user requests, IntAct now provides a two-tiered view of the interaction data. The search interface allows the user to iteratively develop complex queries, exploiting the detailed annotation with hierarchical controlled vocabularies. Results are provided at any stage in a simplified, tabular view. Specialized views then allows 'zooming in' on the full annotation of interactions, interactors and their properties. IntAct source code and data are freely available at http://www.ebi.ac.uk/intact. 01 januari 2010

Published
2010

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