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Jackson DB, Racz R, Kim S, Brock S, Burkhart K. Rewiring Drug Research and Development through Human Data-Driven Discovery (HD 3). Pharmaceutics 2023; 15:1673. [PMID: 37376121 DOI: 10.3390/pharmaceutics15061673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.
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Affiliation(s)
| | - Rebecca Racz
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Sarah Kim
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL 32827, USA
| | | | - Keith Burkhart
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993, USA
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2
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Abstract
A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.
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3
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Ricard-Blum S, Miele AE. Omic approaches to decipher the molecular mechanisms of fibrosis, and design new anti-fibrotic strategies. Semin Cell Dev Biol 2020; 101:161-169. [DOI: 10.1016/j.semcdb.2019.12.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 12/16/2019] [Accepted: 12/16/2019] [Indexed: 12/17/2022]
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Paul P, Antonydhason V, Gopal J, Haga SW, Hasan N, Oh JW. Bioinformatics for Renal and Urinary Proteomics: Call for Aggrandization. Int J Mol Sci 2020; 21:E961. [PMID: 32024005 PMCID: PMC7038205 DOI: 10.3390/ijms21030961] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/24/2020] [Accepted: 01/27/2020] [Indexed: 02/07/2023] Open
Abstract
The clinical sampling of urine is noninvasive and unrestricted, whereby huge volumes can be easily obtained. This makes urine a valuable resource for the diagnoses of diseases. Urinary and renal proteomics have resulted in considerable progress in kidney-based disease diagnosis through biomarker discovery and treatment. This review summarizes the bioinformatics tools available for this area of proteomics and the milestones reached using these tools in clinical research. The scant research publications and the even more limited bioinformatic tool options available for urinary and renal proteomics are highlighted in this review. The need for more attention and input from bioinformaticians is highlighted, so that progressive achievements and releases can be made. With just a handful of existing tools for renal and urinary proteomic research available, this review identifies a gap worth targeting by protein chemists and bioinformaticians. The probable causes for the lack of enthusiasm in this area are also speculated upon in this review. This is the first review that consolidates the bioinformatics applications specifically for renal and urinary proteomics.
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Affiliation(s)
- Piby Paul
- St. Jude Childrens Cancer Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA;
| | - Vimala Antonydhason
- Department of Microbiology and Immunology, Institute for Biomedicine, Gothenburg University, 413 90 Gothenburg, Sweden;
| | - Judy Gopal
- Department of Environmental Health Sciences, Konkuk University, Seoul 143-701, Korea;
| | - Steve W. Haga
- Department of Computer Science and Engineering, National Sun Yat Sen University, Kaohsiung 804, Taiwan;
| | - Nazim Hasan
- Department of Chemistry, Faculty of Science, Jazan University, P.O. Box 114, Jazan 45142, Saudi Arabia;
| | - Jae-Wook Oh
- Department of Stem Cell and Regenerative Biotechnology, Konkuk University, Seoul 05029, Korea
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Sirolli V, Pieroni L, Di Liberato L, Urbani A, Bonomini M. Urinary Peptidomic Biomarkers in Kidney Diseases. Int J Mol Sci 2019; 21:E96. [PMID: 31877774 PMCID: PMC6982248 DOI: 10.3390/ijms21010096] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 12/16/2019] [Accepted: 12/19/2019] [Indexed: 12/20/2022] Open
Abstract
In order to effectively develop personalized medicine for kidney diseases we urgently need to develop highly accurate biomarkers for use in the clinic, since current biomarkers of kidney damage (changes in serum creatinine and/or urine albumin excretion) apply to a later stage of disease, lack accuracy, and are not connected with molecular pathophysiology. Analysis of urine peptide content (urinary peptidomics) has emerged as one of the most attractive areas in disease biomarker discovery. Urinary peptidome analysis allows the detection of short and long-term physiological or pathological changes occurring within the kidney. Urinary peptidomics has been applied extensively for several years now in renal patients, and may greatly improve kidney disease management by supporting earlier and more accurate detection, prognostic assessment, and prediction of response to treatment. It also promises better understanding of kidney disease pathophysiology, and has been proposed as a "liquid biopsy" to discriminate various types of renal disorders. Furthermore, proteins being the major drug targets, peptidome analysis may allow one to evaluate the effects of therapies at the protein signaling pathway level. We here review the most recent findings on urinary peptidomics in the setting of the most common kidney diseases.
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Affiliation(s)
- Vittorio Sirolli
- Nephrology and Dialysis Unit, Department of Medicine, G. d’Annunzio University, Chieti-Pescara, SS.Annunziata Hospital, Via dei Vestini, 66013 Chieti, Italy; (V.S.); (L.D.L.)
| | - Luisa Pieroni
- Proteomics and Metabonomics Unit, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy;
| | - Lorenzo Di Liberato
- Nephrology and Dialysis Unit, Department of Medicine, G. d’Annunzio University, Chieti-Pescara, SS.Annunziata Hospital, Via dei Vestini, 66013 Chieti, Italy; (V.S.); (L.D.L.)
| | - Andrea Urbani
- Institute of Biochemistry and Clinical Biochemistry, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Department of Laboratory Diagnostic and Infectious Diseases, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Mario Bonomini
- Nephrology and Dialysis Unit, Department of Medicine, G. d’Annunzio University, Chieti-Pescara, SS.Annunziata Hospital, Via dei Vestini, 66013 Chieti, Italy; (V.S.); (L.D.L.)
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6
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Venkatesan A, Tagny Ngompe G, Hassouni NE, Chentli I, Guignon V, Jonquet C, Ruiz M, Larmande P. Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy. PLoS One 2018; 13:e0198270. [PMID: 30500839 PMCID: PMC6269127 DOI: 10.1371/journal.pone.0198270] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 09/03/2018] [Indexed: 12/22/2022] Open
Abstract
Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD- www.agrold.org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources-such as Gramene.org and TropGeneDB-with 10 ontologies-such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLD's objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.
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Affiliation(s)
- Aravind Venkatesan
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Gildas Tagny Ngompe
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Nordine El Hassouni
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- UMR AGAP, CIRAD, Montpellier, France
- South Green Bioinformatics Platform, Montpellier, France
| | - Imene Chentli
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Valentin Guignon
- South Green Bioinformatics Platform, Montpellier, France
- Bioversity International, Montpellier, France
| | - Clement Jonquet
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
| | - Manuel Ruiz
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- UMR AGAP, CIRAD, Montpellier, France
- South Green Bioinformatics Platform, Montpellier, France
- AGAP, Univ. of Montpellier, CIRAD, INRA, INRIA, SupAgro, Montpellier, France
| | - Pierre Larmande
- Institut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France
- LIRMM, Univ. of Montpellier & CNRS, Montpellier, France
- South Green Bioinformatics Platform, Montpellier, France
- DIADE, IRD, Univ. of Montpellier, Montpellier, France
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7
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Pletz J, Enoch SJ, Jais DM, Mellor CL, Pawar G, Firman JW, Madden JC, Webb SD, Tagliati CA, Cronin MTD. A critical review of adverse effects to the kidney: mechanisms, data sources, and in silico tools to assist prediction. Expert Opin Drug Metab Toxicol 2018; 14:1225-1253. [PMID: 30345815 DOI: 10.1080/17425255.2018.1539076] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION The kidney is a major target for toxicity elicited by pharmaceuticals and environmental pollutants. Standard testing which often does not investigate underlying mechanisms has proven not to be an adequate hazard assessment approach. As such, there is an opportunity for the application of computational approaches that utilize multiscale data based on the Adverse Outcome Pathway (AOP) paradigm, coupled with an understanding of the chemistry underpinning the molecular initiating event (MIE) to provide a deep understanding of how structural fragments of molecules relate to specific mechanisms of nephrotoxicity. Aims covered: The aim of this investigation was to review the current scientific landscape related to computational methods, including mechanistic data, AOPs, publicly available knowledge bases and current in silico models, for the assessment of pharmaceuticals and other chemicals with regard to their potential to elicit nephrotoxicity. A list of over 250 nephrotoxicants enriched with, where possible, mechanistic and AOP-derived understanding was compiled. Expert opinion: Whilst little mechanistic evidence has been translated into AOPs, this review identified a number of data sources of in vitro, in vivo, and human data that may assist in the development of in silico models which in turn may shed light on the interrelationships between nephrotoxicity mechanisms.
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Affiliation(s)
- Julia Pletz
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Steven J Enoch
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Diviya M Jais
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Claire L Mellor
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Gopal Pawar
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - James W Firman
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Judith C Madden
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
| | - Steven D Webb
- b Department of Applied Mathematics , Liverpool John Moores University , Liverpool , UK
| | - Carlos A Tagliati
- c Departamento de Análises Clínicas e Toxicológicas , Universidade Federal de Minas Gerais , Belo Horizonte , Brazil
| | - Mark T D Cronin
- a School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK
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8
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Harpole M, Davis J, Espina V. Current state of the art for enhancing urine biomarker discovery. Expert Rev Proteomics 2017; 13:609-26. [PMID: 27232439 DOI: 10.1080/14789450.2016.1190651] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual's metabolic and pathophysiologic state. AREAS COVERED High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins. Expert commentary: Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals.
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Affiliation(s)
- Michael Harpole
- a Center for Applied Proteomics and Molecular Medicine , George Mason University , Manassas , VA , USA
| | - Justin Davis
- b Department of Chemistry/Biochemistry , George Mason University , Manassas , VA , USA
| | - Virginia Espina
- a Center for Applied Proteomics and Molecular Medicine , George Mason University , Manassas , VA , USA
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9
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Krochmal M, Fernandes M, Filip S, Pontillo C, Husi H, Zoidakis J, Mischak H, Vlahou A, Jankowski J. PeptiCKDdb-peptide- and protein-centric database for the investigation of genesis and progression of chronic kidney disease. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw128. [PMID: 27589965 PMCID: PMC5009324 DOI: 10.1093/database/baw128] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Accepted: 08/17/2016] [Indexed: 01/11/2023]
Abstract
The peptiCKDdb is a publicly available database platform dedicated to support research in the field of chronic kidney disease (CKD) through identification of novel biomarkers and molecular features of this complex pathology. PeptiCKDdb collects peptidomics and proteomics datasets manually extracted from published studies related to CKD. Datasets from peptidomics or proteomics, human case/control studies on CKD and kidney or urine profiling were included. Data from 114 publications (studies of body fluids and kidney tissue: 26 peptidomics and 76 proteomics manuscripts on human CKD, and 12 focusing on healthy proteome profiling) are currently deposited and the content is quarterly updated. Extracted datasets include information about the experimental setup, clinical study design, discovery-validation sample sizes and list of differentially expressed proteins (P-value < 0.05). A dedicated interactive web interface, equipped with multiparametric search engine, data export and visualization tools, enables easy browsing of the data and comprehensive analysis. In conclusion, this repository might serve as a source of data for integrative analysis or a knowledgebase for scientists seeking confirmation of their findings and as such, is expected to facilitate the modeling of molecular mechanisms underlying CKD and identification of biologically relevant biomarkers. Database URL:www.peptickddb.com
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Affiliation(s)
- Magdalena Krochmal
- Biomedical Research Foundation Academy of Athens, Center of Basic Research, Athens, Greece University Hospital RWTH Aachen University, Institute for Molecular Cardiovascular Research, Aachen, Germany
| | - Marco Fernandes
- University of Glasgow, BHF Glasgow Cardiovascular Research Centre, Glasgow, United Kingdom
| | - Szymon Filip
- Biomedical Research Foundation Academy of Athens, Center of Basic Research, Athens, Greece Experimental Nephrology and Hypertension, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia Pontillo
- Experimental Nephrology and Hypertension, Charité-Universitätsmedizin Berlin, Berlin, Germany Mosaiques Diagnostics GmbH, Hannover, Germany
| | - Holger Husi
- University of Glasgow, BHF Glasgow Cardiovascular Research Centre, Glasgow, United Kingdom
| | - Jerome Zoidakis
- Biomedical Research Foundation Academy of Athens, Center of Basic Research, Athens, Greece
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, Hannover, Germany University of Glasgow, Institute of Cardiovascular and Medical Sciences, Glasgow, United Kingdom
| | - Antonia Vlahou
- Biomedical Research Foundation Academy of Athens, Center of Basic Research, Athens, Greece
| | - Joachim Jankowski
- University Hospital RWTH Aachen University, Institute for Molecular Cardiovascular Research, Aachen, Germany University of Maastricht, CARIM School for Cardiovascular Diseases, Maastricht, The Netherlands
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Diehl AD, Meehan TF, Bradford YM, Brush MH, Dahdul WM, Dougall DS, He Y, Osumi-Sutherland D, Ruttenberg A, Sarntivijai S, Van Slyke CE, Vasilevsky NA, Haendel MA, Blake JA, Mungall CJ. The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability. J Biomed Semantics 2016; 7:44. [PMID: 27377652 PMCID: PMC4932724 DOI: 10.1186/s13326-016-0088-7] [Citation(s) in RCA: 145] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 06/23/2016] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND The Cell Ontology (CL) is an OBO Foundry candidate ontology covering the domain of canonical, natural biological cell types. Since its inception in 2005, the CL has undergone multiple rounds of revision and expansion, most notably in its representation of hematopoietic cells. For in vivo cells, the CL focuses on vertebrates but provides general classes that can be used for other metazoans, which can be subtyped in species-specific ontologies. CONSTRUCTION AND CONTENT Recent work on the CL has focused on extending the representation of various cell types, and developing new modules in the CL itself, and in related ontologies in coordination with the CL. For example, the Kidney and Urinary Pathway Ontology was used as a template to populate the CL with additional cell types. In addition, subtypes of the class 'cell in vitro' have received improved definitions and labels to provide for modularity with the representation of cells in the Cell Line Ontology and Reagent Ontology. Recent changes in the ontology development methodology for CL include a switch from OBO to OWL for the primary encoding of the ontology, and an increasing reliance on logical definitions for improved reasoning. UTILITY AND DISCUSSION The CL is now mandated as a metadata standard for large functional genomics and transcriptomics projects, and is used extensively for annotation, querying, and analyses of cell type specific data in sequencing consortia such as FANTOM5 and ENCODE, as well as for the NIAID ImmPort database and the Cell Image Library. The CL is also a vital component used in the modular construction of other biomedical ontologies-for example, the Gene Ontology and the cross-species anatomy ontology, Uberon, use CL to support the consistent representation of cell types across different levels of anatomical granularity, such as tissues and organs. CONCLUSIONS The ongoing improvements to the CL make it a valuable resource to both the OBO Foundry community and the wider scientific community, and we continue to experience increased interest in the CL both among developers and within the user community.
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Affiliation(s)
- Alexander D. Diehl
- />Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY 14203 USA
| | - Terrence F. Meehan
- />European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Yvonne M. Bradford
- />ZFIN, the Zebrafish Model Organism Database, 5291 University of Oregon, Eugene, OR 97403 USA
| | - Matthew H. Brush
- />Ontology Development Group, Library, Oregon Health and Science University, Portland, Oregon 97239 USA
| | - Wasila M. Dahdul
- />Department of Biology, University of South Dakota, Vermillion, SD 57069 USA
- />National Evolutionary Synthesis Center, Durham, NC 27705 USA
| | - David S. Dougall
- />Southwestern Medical Center, University of Texas, Dallas, TX 75235 USA
| | - Yongqun He
- />Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - David Osumi-Sutherland
- />European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Alan Ruttenberg
- />Oral Diagnostics Sciences, University at Buffalo School of Dental Medicine, Buffalo, NY 14210 USA
| | - Sirarat Sarntivijai
- />European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD UK
| | - Ceri E. Van Slyke
- />ZFIN, the Zebrafish Model Organism Database, 5291 University of Oregon, Eugene, OR 97403 USA
| | - Nicole A. Vasilevsky
- />Ontology Development Group, Library, Oregon Health and Science University, Portland, Oregon 97239 USA
| | - Melissa A. Haendel
- />Ontology Development Group, Library, Oregon Health and Science University, Portland, Oregon 97239 USA
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Jupp S, Burdett T, Welter D, Sarntivijai S, Parkinson H, Malone J. Webulous and the Webulous Google Add-On--a web service and application for ontology building from templates. J Biomed Semantics 2016; 7:17. [PMID: 27042287 PMCID: PMC4818523 DOI: 10.1186/s13326-016-0055-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 03/11/2016] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Authoring bio-ontologies is a task that has traditionally been undertaken by skilled experts trained in understanding complex languages such as the Web Ontology Language (OWL), in tools designed for such experts. As requests for new terms are made, the need for expert ontologists represents a bottleneck in the development process. Furthermore, the ability to rigorously enforce ontology design patterns in large, collaboratively developed ontologies is difficult with existing ontology authoring software. DESCRIPTION We present Webulous, an application suite for supporting ontology creation by design patterns. Webulous provides infrastructure to specify templates for populating ontology design patterns that get transformed into OWL assertions in a target ontology. Webulous provides programmatic access to the template server and a client application has been developed for Google Sheets that allows templates to be loaded, populated and resubmitted to the Webulous server for processing. CONCLUSIONS The development and delivery of ontologies to the community requires software support that goes beyond the ontology editor. Building ontologies by design patterns and providing simple mechanisms for the addition of new content helps reduce the overall cost and effort required to develop an ontology. The Webulous system provides support for this process and is used as part of the development of several ontologies at the European Bioinformatics Institute.
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Affiliation(s)
- Simon Jupp
- European Bioinformatics Institute (EMBL-EBI),European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Tony Burdett
- European Bioinformatics Institute (EMBL-EBI),European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Danielle Welter
- European Bioinformatics Institute (EMBL-EBI),European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Sirarat Sarntivijai
- European Bioinformatics Institute (EMBL-EBI),European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Bioinformatics Institute (EMBL-EBI),European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - James Malone
- European Bioinformatics Institute (EMBL-EBI),European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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12
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Venkatesan A, Tripathi S, Sanz de Galdeano A, Blondé W, Lægreid A, Mironov V, Kuiper M. Finding gene regulatory network candidates using the gene expression knowledge base. BMC Bioinformatics 2014; 15:386. [PMID: 25490885 PMCID: PMC4279962 DOI: 10.1186/s12859-014-0386-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Accepted: 11/14/2014] [Indexed: 12/17/2022] Open
Abstract
Background Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ‘omics’ data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. Results We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Conclusions Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0386-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Aravind Venkatesan
- Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway.
| | - Sushil Tripathi
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
| | | | - Ward Blondé
- Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway.
| | - Astrid Lægreid
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
| | - Vladimir Mironov
- Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway.
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway.
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Farrah T, Deutsch EW, Omenn GS, Sun Z, Watts JD, Yamamoto T, Shteynberg D, Harris MM, Moritz RL. State of the human proteome in 2013 as viewed through PeptideAtlas: comparing the kidney, urine, and plasma proteomes for the biology- and disease-driven Human Proteome Project. J Proteome Res 2013; 13:60-75. [PMID: 24261998 DOI: 10.1021/pr4010037] [Citation(s) in RCA: 99] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The kidney, urine, and plasma proteomes are intimately related: proteins and metabolic waste products are filtered from the plasma by the kidney and excreted via the urine, while kidney proteins may be secreted into the circulation or released into the urine. Shotgun proteomics data sets derived from human kidney, urine, and plasma samples were collated and processed using a uniform software pipeline, and relative protein abundances were estimated by spectral counting. The resulting PeptideAtlas builds yielded 4005, 2491, and 3553 nonredundant proteins at 1% FDR for the kidney, urine, and plasma proteomes, respectively - for kidney and plasma, the largest high-confidence protein sets to date. The same pipeline applied to all available human data yielded a 2013 Human PeptideAtlas build containing 12,644 nonredundant proteins and at least one peptide for each of ∼14,000 Swiss-Prot entries, an increase over 2012 of ∼7.5% of the predicted human proteome. We demonstrate that abundances are correlated between plasma and urine, examine the most abundant urine proteins not derived from either plasma or kidney, and consider the biomarker potential of proteins associated with renal decline. This analysis forms part of the Biology and Disease-driven Human Proteome Project (B/D-HPP) and is a contribution to the Chromosome-centric Human Proteome Project (C-HPP) special issue.
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Brennan E, McEvoy C, Sadlier D, Godson C, Martin F. The genetics of diabetic nephropathy. Genes (Basel) 2013; 4:596-619. [PMID: 24705265 PMCID: PMC3927570 DOI: 10.3390/genes4040596] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Revised: 10/08/2013] [Accepted: 10/30/2013] [Indexed: 12/18/2022] Open
Abstract
Up to 40% of patients with type 1 and type 2 diabetes will develop diabetic nephropathy (DN), resulting in chronic kidney disease and potential organ failure. There is evidence for a heritable genetic susceptibility to DN, but despite intensive research efforts the causative genes remain elusive. Recently, genome-wide association studies have discovered several novel genetic variants associated with DN. The identification of such variants may potentially allow for early identification of at risk patients. Here we review the current understanding of the key molecular mechanisms and genetic architecture of DN, and discuss the merits of employing an integrative approach to incorporate datasets from multiple sources (genetics, transcriptomics, epigenetic, proteomic) in order to fully elucidate the genetic elements contributing to this serious complication of diabetes.
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Affiliation(s)
- Eoin Brennan
- Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland.
| | - Caitríona McEvoy
- Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland.
| | | | - Catherine Godson
- Diabetes Complications Research Centre, Conway Institute of Biomolecular and Biomedical Research, School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland.
| | - Finian Martin
- Conway Institute of Biomolecular and Biomedical Research, School of Biomolecular and Biomedical Sciences, University College Dublin, Dublin, Ireland.
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The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases. BMC Bioinformatics 2013; 14:235. [PMID: 23883183 PMCID: PMC3725151 DOI: 10.1186/1471-2105-14-235] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2012] [Accepted: 07/21/2013] [Indexed: 02/08/2023] Open
Abstract
Background Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases. Results In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease. Conclusions The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner.
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Bai JP, Abernethy DR. Systems Pharmacology to Predict Drug Toxicity: Integration Across Levels of Biological Organization. Annu Rev Pharmacol Toxicol 2013; 53:451-73. [DOI: 10.1146/annurev-pharmtox-011112-140248] [Citation(s) in RCA: 102] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jane P.F. Bai
- Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland 20993;
| | - Darrell R. Abernethy
- Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland 20993;
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Mikroyannidi E, Stevens R, Iannone L, Rector A. Analysing Syntactic Regularities and Irregularities in SNOMED-CT. J Biomed Semantics 2012; 3:8. [PMID: 23244503 PMCID: PMC3637289 DOI: 10.1186/2041-1480-3-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2012] [Accepted: 11/13/2012] [Indexed: 11/28/2022] Open
Abstract
Motivation In this paper we demonstrate the usage of RIO; a framework for detecting syntactic regularities using cluster analysis of the entities in the signature of an ontology. Quality assurance in ontologies is vital for their use in real applications, as well as a complex and difficult task. It is also important to have such methods and tools when the ontology lacks documentation and the user cannot consult the ontology developers to understand its construction. One aspect of quality assurance is checking how well an ontology complies with established ‘coding standards’; is the ontology regular in how descriptions of different types of entities are axiomatised? Is there a similar way to describe them and are there any corner cases that are not covered by a pattern? Detection of regularities and irregularities in axiom patterns should provide ontology authors and quality inspectors with a level of abstraction such that compliance to coding standards can be automated. However, there is a lack of such reverse ontology engineering methods and tools. Results RIO framework allows regularities to be detected in an OWL ontology, i.e. repetitive structures in the axioms of an ontology. We describe the use of standard machine learning approaches to make clusters of similar entities and generalise over their axioms to find regularities. This abstraction allows matches to, and deviations from, an ontology’s patterns to be shown. We demonstrate its usage with the inspection of three modules from SNOMED-CT, a large medical terminology, that cover “Present” and “Absent” findings, as well as “Chronic” and “Acute” findings. The module sizes are 5 065, 20 688 and 19 812 asserted axioms. They are analysed in terms of their types and number of regularities and irregularities in the asserted axioms of the ontology. The analysis showed that some modules of the terminology, which were expected to instantiate a pattern described in the SNOMED-CT technical guide, were found to have a high number of regularity deviations. A subset of these were categorised as “design defects” by verifying them with past work on the quality assurance of SNOMED-CT. These were mainly incomplete descriptions. In the worst case, the expected patterns described in the technical guide were followed by only 5% of the axioms in the module. Conclusion It is possible to automatically detect regularities and then inspect irregularities in an ontology. We argue that RIO is a tool to find and report such matches and mismatches, for evaluations by the domain experts. We have demonstrated that standard clustering techniques from machine learning can offer a tool in the drive for quality assurance in ontologies. Availability http://riotool.sourceforge.net/ Contact http://eleni.mikroyannidi@manchester.ac.uk, http://robert.stevens@manchehster.ac.uk
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Affiliation(s)
- Eleni Mikroyannidi
- School of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL UK.
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Stevens R, Jupp S, Klein J, Schanstra J. Using semantic web technologies to manage complexity and change in biomedical data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:3708-11. [PMID: 22255145 DOI: 10.1109/iembs.2011.6090629] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Data in biomedicine are characterised by their complexity, volatility and heterogeneity. It is these characteristics, rather than size of the data, that make managing these data an issue for their analysis. Any significant data analysis task requires gathering data from many places, organising the relationships between the data's entities and overcoming the issues of recognising the nature of each entity such that this organisation can take place. It is the inter-relationship of these data and the semantic confusion inherent in the data that make the data complex. On top of this we have volatility in the domain's data, knowledge and experimental techniques that make the processing of data from the domain a distinct challenge, even before those data are organised. In this article we describe these challenges with respect to a project that is using data mining techniques to analyse data from the kidney and urinary pathway (KUP) domain. We are using Semantic Web technologies to manage the complexity and change in our data and we report on our experiences in this project.
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Affiliation(s)
- Robert Stevens
- School of Computer Science, University of Manchester, Oxford Road, Manchester, United Kingdom
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Samwald M, Coulet A, Huerga I, Powers RL, Luciano JS, Freimuth RR, Whipple F, Pichler E, Prud'hommeaux E, Dumontier M, Marshall MS. Semantically enabling pharmacogenomic data for the realization of personalized medicine. Pharmacogenomics 2012; 13:201-12. [PMID: 22256869 DOI: 10.2217/pgs.11.179] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Understanding how each individual's genetics and physiology influences pharmaceutical response is crucial to the realization of personalized medicine and the discovery and validation of pharmacogenomic biomarkers is key to its success. However, integration of genotype and phenotype knowledge in medical information systems remains a critical challenge. The inability to easily and accurately integrate the results of biomolecular studies with patients' medical records and clinical reports prevents us from realizing the full potential of pharmacogenomic knowledge for both drug development and clinical practice. Herein, we describe approaches using Semantic Web technologies, in which pharmacogenomic knowledge relevant to drug development and medical decision support is represented in such a way that it can be efficiently accessed both by software and human experts. We suggest that this approach increases the utility of data, and that such computational technologies will become an essential part of personalized medicine, alongside diagnostics and pharmaceutical products.
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Affiliation(s)
- Matthias Samwald
- Department of Medical Statistics & Bioinformatics, Leiden University Medical Center/Informatics Institute, University of Amsterdam, Einthovenweg 20, 2333 ZC Leiden, The Netherlands
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20
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Jupp S, Stevens R, Hoehndorf R. Logical Gene Ontology Annotations (GOAL): exploring gene ontology annotations with OWL. J Biomed Semantics 2012; 3 Suppl 1:S3. [PMID: 22541594 PMCID: PMC3337258 DOI: 10.1186/2041-1480-3-s1-s3] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
MOTIVATION Ontologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products, there is a possibility to explore gene products through multiple ontological perspectives at the same time. Questions could be asked that link a gene product's function, process, cellular location, phenotype and disease. Current tools, such as AmiGO, allow exploration of genes based on their GO annotations, but not through multiple ontological perspectives. In addition, the semantics of these ontology's representations should be able to, through automated reasoning, afford richer query opportunities of the gene product annotations than is currently possible. RESULTS To do this multi-perspective, richer querying of gene product annotations, we have created the Logical Gene Ontology, or GOAL ontology, in OWL that combines the Gene Ontology, Human Disease Ontology and the Mammalian Phenotype Ontology, together with classes that represent the annotations with these ontologies for mouse gene products. Each mouse gene product is represented as a class, with the appropriate relationships to the GO aspects, phenotype and disease with which it has been annotated. We then use defined classes to query these protein classes through automated reasoning, and to build a complex hierarchy of gene products. We have presented this through a Web interface that allows arbitrary queries to be constructed and the results displayed. CONCLUSION This standard use of OWL affords a rich interaction with Gene Ontology, Human Disease Ontology and Mammalian Phenotype Ontology annotations for the mouse, to give a fine partitioning of the gene products in the GOAL ontology. OWL in combination with automated reasoning can be effectively used to query across ontologies to ask biologically rich questions. We have demonstrated that automated reasoning can be used to deliver practical on-line querying support for the ontology annotations available for the mouse. AVAILABILITY The GOAL Web page is to be found at http://owl.cs.manchester.ac.uk/goal.
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Affiliation(s)
- Simon Jupp
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK
| | - Robert Stevens
- School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Robert Hoehndorf
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK
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Klein J, Jupp S, Moulos P, Fernandez M, Buffin‐Meyer B, Casemayou A, Chaaya R, Charonis A, Bascands J, Stevens R, Schanstra JP. The KUPKB: a novel Web application to access multiomics data on kidney disease. FASEB J 2012; 26:2145-53. [DOI: 10.1096/fj.11-194381] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Julie Klein
- Institut National de la Santé et de la Recherche Médicale, U1048Institut of Cardiovascular and Metabolic DiseaseToulouseFrance
- Université Toulouse III Paul‐SabatierToulouseFrance
| | - Simon Jupp
- School of Computer ScienceUniversity of ManchesterManchesterUK
| | - Panagiotis Moulos
- Institut National de la Santé et de la Recherche Médicale, U1048Institut of Cardiovascular and Metabolic DiseaseToulouseFrance
- Université Toulouse III Paul‐SabatierToulouseFrance
| | - Myriem Fernandez
- Institut National de la Santé et de la Recherche Médicale, U1048Institut of Cardiovascular and Metabolic DiseaseToulouseFrance
- Université Toulouse III Paul‐SabatierToulouseFrance
| | - Bénédicte Buffin‐Meyer
- Institut National de la Santé et de la Recherche Médicale, U1048Institut of Cardiovascular and Metabolic DiseaseToulouseFrance
- Université Toulouse III Paul‐SabatierToulouseFrance
| | - Audrey Casemayou
- Institut National de la Santé et de la Recherche Médicale, U1048Institut of Cardiovascular and Metabolic DiseaseToulouseFrance
- Université Toulouse III Paul‐SabatierToulouseFrance
| | - Rana Chaaya
- Institut National de la Santé et de la Recherche Médicale, U1048Institut of Cardiovascular and Metabolic DiseaseToulouseFrance
- Université Toulouse III Paul‐SabatierToulouseFrance
| | - Aristidis Charonis
- Section of HistologyCenter for Basic Research I, Biomedical Research Foundation of the Academy of AthensAthensGreece
| | - Jean‐Loup Bascands
- Institut National de la Santé et de la Recherche Médicale, U1048Institut of Cardiovascular and Metabolic DiseaseToulouseFrance
- Université Toulouse III Paul‐SabatierToulouseFrance
| | - Robert Stevens
- School of Computer ScienceUniversity of ManchesterManchesterUK
| | - Joost P. Schanstra
- Institut National de la Santé et de la Recherche Médicale, U1048Institut of Cardiovascular and Metabolic DiseaseToulouseFrance
- Université Toulouse III Paul‐SabatierToulouseFrance
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Jupp S, Horridge M, Iannone L, Klein J, Owen S, Schanstra J, Wolstencroft K, Stevens R. Populous: a tool for building OWL ontologies from templates. BMC Bioinformatics 2012; 13 Suppl 1:S5. [PMID: 22373396 PMCID: PMC3471341 DOI: 10.1186/1471-2105-13-s1-s5] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Ontologies are being developed for the life sciences to standardise the way we describe and interpret the wealth of data currently being generated. As more ontology based applications begin to emerge, tools are required that enable domain experts to contribute their knowledge to the growing pool of ontologies. There are many barriers that prevent domain experts engaging in the ontology development process and novel tools are needed to break down these barriers to engage a wider community of scientists. RESULTS We present Populous, a tool for gathering content with which to construct an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies. Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. These forms can be exported as spreadsheets, providing an interface that is much more familiar to many biologists. CONCLUSIONS Populous's contribution is in the knowledge gathering stage of ontology development; it separates knowledge gathering from the conceptualisation and axiomatisation, as well as separating the user from the standard ontology authoring environments. Populous is by no means a replacement for standard ontology editing tools, but instead provides a useful platform for engaging a wider community of scientists in the mass production of ontology content.
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Affiliation(s)
- Simon Jupp
- Bio-Health Informatics Group, School of Computer Science, Kilburn Building, Oxford Road, Manchester, UK, M13 9PL
| | - Matthew Horridge
- Bio-Health Informatics Group, School of Computer Science, Kilburn Building, Oxford Road, Manchester, UK, M13 9PL
| | - Luigi Iannone
- Bio-Health Informatics Group, School of Computer Science, Kilburn Building, Oxford Road, Manchester, UK, M13 9PL
| | - Julie Klein
- Inserm U1048, Institute of Metabolic and Cardiovascular Diseases - I2MC, 1 avenue Jean Poulhés, B.P. 84225, 31432 Toulouse Cedex 4, France
| | - Stuart Owen
- Bio-Health Informatics Group, School of Computer Science, Kilburn Building, Oxford Road, Manchester, UK, M13 9PL
| | - Joost Schanstra
- Inserm U1048, Institute of Metabolic and Cardiovascular Diseases - I2MC, 1 avenue Jean Poulhés, B.P. 84225, 31432 Toulouse Cedex 4, France
| | - Katy Wolstencroft
- Bio-Health Informatics Group, School of Computer Science, Kilburn Building, Oxford Road, Manchester, UK, M13 9PL
| | - Robert Stevens
- Bio-Health Informatics Group, School of Computer Science, Kilburn Building, Oxford Road, Manchester, UK, M13 9PL
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Soldatova LN, Sansone SA, Stephens SM, Shah NH. Selected papers from the 13th Annual Bio-Ontologies Special Interest Group Meeting. J Biomed Semantics 2011; 2 Suppl 2:I1. [PMID: 21624154 PMCID: PMC3102888 DOI: 10.1186/2041-1480-2-s2-i1] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Over the years, the Bio-Ontologies SIG at ISMB has provided a forum for discussion of the latest and most innovative research in the application of ontologies and more generally the organisation, presentation and dissemination of knowledge in biomedicine and the life sciences. The ten papers selected for this supplement are extended versions of the original papers presented at the 2010 SIG. The papers span a wide range of topics including practical solutions for data and knowledge integration for translational medicine, hypothesis based querying , understanding kidney and urinary pathways, mining the pharmacogenomics literature; theoretical research into the orthogonality of biomedical ontologies, the representation of diseases, the representation of research hypotheses, the combination of ontologies and natural language processing for an annotation framework, the generation of textual definitions, and the discovery of gene interaction networks.
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Jupp S, Klein J, Schanstra J, Stevens R. Developing a kidney and urinary pathway knowledge base. J Biomed Semantics 2011; 2 Suppl 2:S7. [PMID: 21624162 PMCID: PMC3102896 DOI: 10.1186/2041-1480-2-s2-s7] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Chronic renal disease is a global health problem. The identification of suitable biomarkers could facilitate early detection and diagnosis and allow better understanding of the underlying pathology. One of the challenges in meeting this goal is the necessary integration of experimental results from multiple biological levels for further analysis by data mining. Data integration in the life science is still a struggle, and many groups are looking to the benefits promised by the Semantic Web for data integration. RESULTS We present a Semantic Web approach to developing a knowledge base that integrates data from high-throughput experiments on kidney and urine. A specialised KUP ontology is used to tie the various layers together, whilst background knowledge from external databases is incorporated by conversion into RDF. Using SPARQL as a query mechanism, we are able to query for proteins expressed in urine and place these back into the context of genes expressed in regions of the kidney. CONCLUSIONS The KUPKB gives KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. The Semantic Web technologies we use, together with the background knowledge from the domain's ontologies, allows both rapid conversion and integration of this knowledge base. The KUPKB is still relatively small, but questions remain about scalability, maintenance and availability of the knowledge itself. AVAILABILITY The KUPKB may be accessed via http://www.e-lico.eu/kupkb.
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Affiliation(s)
- Simon Jupp
- School of Computer Science, University of Manchester, UK
| | - Julie Klein
- Institut National de la Santé et de la Recherche Médicale (INSERM), U858, Toulouse, France
- Université Toulouse III Paul-Sabatier, I2MR, IFR150, Toulouse, France
| | - Joost Schanstra
- Institut National de la Santé et de la Recherche Médicale (INSERM), U858, Toulouse, France
- Université Toulouse III Paul-Sabatier, I2MR, IFR150, Toulouse, France
| | - Robert Stevens
- School of Computer Science, University of Manchester, UK
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