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Martens M, Stierum R, Schymanski EL, Evelo CT, Aalizadeh R, Aladjov H, Arturi K, Audouze K, Babica P, Berka K, Bessems J, Blaha L, Bolton EE, Cases M, Damalas DΕ, Dave K, Dilger M, Exner T, Geerke DP, Grafström R, Gray A, Hancock JM, Hollert H, Jeliazkova N, Jennen D, Jourdan F, Kahlem P, Klanova J, Kleinjans J, Kondic T, Kone B, Lynch I, Maran U, Martinez Cuesta S, Ménager H, Neumann S, Nymark P, Oberacher H, Ramirez N, Remy S, Rocca-Serra P, Salek RM, Sallach B, Sansone SA, Sanz F, Sarimveis H, Sarntivijai S, Schulze T, Slobodnik J, Spjuth O, Tedds J, Thomaidis N, Weber RJ, van Westen GJ, Wheelock CE, Williams AJ, Witters H, Zdrazil B, Županič A, Willighagen EL. ELIXIR and Toxicology: a community in development. F1000Res 2023; 10:ELIXIR-1129. [PMID: 37842337 PMCID: PMC10568213 DOI: 10.12688/f1000research.74502.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.
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Affiliation(s)
- Marvin Martens
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Rob Stierum
- Risk Analysis for Products In Development (RAPID), Netherlands Organisation for applied scientific research TNO, Utrecht, 3584 CB, The Netherlands
| | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, 4367, Luxembourg
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6229 EN, The Netherlands
| | - Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Hristo Aladjov
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, 1113, Bulgaria
| | - Kasia Arturi
- Department Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, 8600, Switzerland
| | | | - Pavel Babica
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Karel Berka
- Department of Physical Chemistry, Palacky University Olomouc, Olomouc, 77146, Czech Republic
| | | | - Ludek Blaha
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Evan E. Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | | | - Dimitrios Ε. Damalas
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Kirtan Dave
- School of Science, GSFC University, Gujarat, 391750, India
| | - Marco Dilger
- Forschungs- und Beratungsinstitut Gefahrstoffe (FoBiG) GmbH, Freiburg im Breisgau, 79106, Germany
| | | | - Daan P. Geerke
- AIMMS Division of Molecular Toxicology, Vrije Universiteit, Amsterdam, 1081 HZ, The Netherlands
| | - Roland Grafström
- Department of Toxicology, Misvik Biology, Turku, 20520, Finland
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, 17177, Sweden
| | - Alasdair Gray
- Department of Computer Science, Heriot-Watt University, Edinburgh, UK
| | | | - Henner Hollert
- Department Evolutionary Ecology & Environmental Toxicology (E3T), Goethe-University, Frankfurt, D-60438, Germany
| | | | - Danyel Jennen
- Department of Toxicogenomics, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Fabien Jourdan
- MetaboHUB, French metabolomics infrastructure in Metabolomics and Fluxomics, Toulouse, France
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, Toulouse, France
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Jana Klanova
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Jos Kleinjans
- Department of Toxicogenomics, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Todor Kondic
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, 4367, Luxembourg
| | - Boï Kone
- Faculty of Pharmacy, Malaria Research and Training Center, Bamako, BP:1805, Mali
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, UK, Birmingham, B15 2TT, UK
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, 50411, Estonia
| | | | - Hervé Ménager
- Institut Français de Bioinformatique, Evry, F-91000, France
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Paris, F-75015, France
| | - Steffen Neumann
- Research group Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry, Halle, 06120, Germany
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, 17177, Sweden
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Innsbruck, A-6020, Austria
| | - Noelia Ramirez
- Institut d'Investigacio Sanitaria Pere Virgili-Universitat Rovira i Virgili, Tarragona, 43007, Spain
| | | | - Philippe Rocca-Serra
- Data Readiness Group, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Reza M. Salek
- International Agency for Research on Cancer, World Health Organisation, Lyon, 69372, France
| | - Brett Sallach
- Department of Environment and Geography, University of York, UK, York, YO10 5NG, UK
| | | | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, 08003, Spain
| | | | | | - Tobias Schulze
- Helmholtz Centre for Environmental Research - UFZ, Leipzig, 04318, Germany
| | | | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, SE-75124, Sweden
| | - Jonathan Tedds
- ELIXIR Hub, Wellcome Genome Campus, Cambridge, CB10 1SD, UK
| | - Nikolaos Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, 15771, Greece
| | - Ralf J.M. Weber
- School of Biosciences, University of Birmingham, UK, Birmingham, B15 2TT, UK
| | - Gerard J.P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research, Leiden, 2333 CC, The Netherlands
| | - Craig E. Wheelock
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm SE-141-86, Sweden
- Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, 17177, Sweden
| | - Antony J. Williams
- Center for Computational Toxicology and Exposure, United States Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | | | - Barbara Zdrazil
- Department of Pharmaceutical Sciences, University of Vienna, Vienna, 1090, Austria
| | - Anže Županič
- Department Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, Slovenia
| | - Egon L. Willighagen
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands
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Abstract
The present contribution describes how in silico models and methods are applied at different stages of the drug discovery process in the pharmaceutical industry. A description of the most relevant computational methods and tools is given along with an evaluation of their performance in the assessment of potential genotoxic impurities and the prediction of off-target in vitro pharmacology. The challenges of predicting the outcome of highly complex in vivo studies are discussed followed by considerations on how novel ways to manage, store, exchange, and analyze data may advance knowledge and facilitate modeling efforts. In this context, the current status of broad data sharing initiatives, namely, eTOX and eTransafe, will be described along with related projects that could significantly reduce the use of animals in drug discovery in the future.
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Affiliation(s)
- Alessandro Brigo
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Centre Basel, Basel, Switzerland.
| | - Doha Naga
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Centre Basel, Basel, Switzerland
- Department of Pharmaceutical Chemistry, Group of Pharmacoinformatics, University of Vienna, Wien, Austria
| | - Wolfgang Muster
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Centre Basel, Basel, Switzerland
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3
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Oki NO, Farcal L, Abdelaziz A, Florean O, Doktorova TY, Exner T, Kohonen P, Grafström R, Hardy B. Integrated analysis of in vitro data and the adverse outcome pathway framework for prioritization and regulatory applications: An exploratory case study using publicly available data on piperonyl butoxide and liver models. Toxicol In Vitro 2019; 54:23-32. [DOI: 10.1016/j.tiv.2018.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 08/31/2018] [Accepted: 09/03/2018] [Indexed: 01/30/2023]
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Wu Y, Wang G. Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis. Int J Mol Sci 2018; 19:E2358. [PMID: 30103448 PMCID: PMC6121588 DOI: 10.3390/ijms19082358] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 07/31/2018] [Accepted: 08/08/2018] [Indexed: 02/07/2023] Open
Abstract
Toxicity prediction is very important to public health. Among its many applications, toxicity prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials, because a lot of drug evaluations (cellular, animal, and clinical) can be spared due to the predicted toxicity. In the era of Big Data and artificial intelligence, toxicity prediction can benefit from machine learning, which has been widely used in many fields such as natural language processing, speech recognition, image recognition, computational chemistry, and bioinformatics, with excellent performance. In this article, we review machine learning methods that have been applied to toxicity prediction, including deep learning, random forests, k-nearest neighbors, and support vector machines. We also discuss the input parameter to the machine learning algorithm, especially its shift from chemical structural description only to that combined with human transcriptome data analysis, which can greatly enhance prediction accuracy.
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Affiliation(s)
- Yunyi Wu
- Department of Biology, Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research, Southern University of Science and Technology, Shenzhen 518055, China.
| | - Guanyu Wang
- Department of Biology, Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research, Southern University of Science and Technology, Shenzhen 518055, China.
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Puscheck EE, Bolnick A, Awonuga A, Yang Y, Abdulhasan M, Li Q, Secor E, Louden E, Hüttemann M, Rappolee DA. Why AMPK agonists not known to be stressors may surprisingly contribute to miscarriage or hinder IVF/ART. J Assist Reprod Genet 2018; 35:1359-1366. [PMID: 29882092 PMCID: PMC6086802 DOI: 10.1007/s10815-018-1213-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Accepted: 05/16/2018] [Indexed: 12/20/2022] Open
Abstract
Here we examine recent evidence suggesting that many drugs and diet supplements (DS), experimental AMP-activated protein kinase (AMPK) agonists as well as energy-depleting stress, lead to decreases in anabolism, growth or proliferation, and potency of cultured oocytes, embryos, and stem cells in an AMPK-dependent manner. Surprising data for DS and drugs that have some activity as AMPK agonists in in vitro experiments show possible toxicity. This needs to be balanced against a preponderance of evidence in vivo that these drugs and DS are beneficial for reproduction. We here discuss and analyze data that leads to two possible conclusions: First, although DS and drugs that have some of their therapeutic mechanisms mediated by AMPK activity associated with low ATP levels, some of the associated health problems in vivo and in vitro fertilization/assisted reproductive technologies (IVF/ART) may be better-treated by increasing ATP production using CoQ10 (Ben-Meir et al., Aging Cell 14:887-895, 2015). This enables high developmental trajectories simultaneous with solving stress by energy-requiring responses. In IVF/ART, it is ultimately best to maintain handling and culture of gametes and embryos in the quietest state with low metabolic activity (Leese et al., Mol Hum Reprod 14:667-672, 2008; Leese, Bioessays 24 (9):845-849, 2002) using back-to-nature or simplex algorithms to identify optima (Biggers, Reprod Biomed Online 4 Suppl 1:30-38, 2002). Stress markers, such as checkpoint proteins like TRP53 (aka p53) (Ganeshan et al., Exp Cell Res 358:227-233, 2017); Ganeshan et al., Biol Reprod 83:958-964, 2010) and a small set of kinases from the protein kinome that mediate enzymatic stress responses, can also be used to define optima. But, some gametes or embryos may have been stressed in vivo prior to IVF/ART or IVF/ART optimized for one outcome may be suboptimal for another. Increasing nutrition or adding CoQ10 to increase ATP production (Yang et al., Stem Cell Rev 13:454-464, 2017), managing stress enzyme levels with inhibitors (Xie et al., Mol Hum Reprod 12:217-224, 2006), or adding growth factors such as GM-CSF (Robertson et al., J Reprod Immunol 125:80-88, 2018); Chin et al., Hum Reprod 24:2997-3009, 2009) may increase survival and health of cultured embryos during different stress exposure contexts (Puscheck et al., Adv Exp Med Biol 843:77-128, 2015). We define "stress" as negative stimuli which decrease normal magnitude and speed of development, and these can be stress hormones, reactive oxygen species, inflammatory cytokines, or physical stimuli such as hypoxia. AMPK is normally activated by high AMP, commensurate with low ATP, but it was recently shown that if glucose is present inside the cell, AMPK activation by low ATP/high AMP is suppressed (Zhang et al., Nature 548:112-116, 2017). As we discuss in more detail below, this may also lead to greater AMPK agonist toxicity observed in two-cell embryos that do not import glucose. Stress in embryos and stem cells increases AMPK in large stimulation indexes but also direness indexes; the fastest AMPK activation occurs when stem cells are shifted from optimal oxygen to lower or high levels (Yang et al., J Reprod Dev 63:87-94, 2017). CoQ10 use may be better than risking AMPK-dependent metabolic and developmental toxicity when ATP is depleted and AMPK activated. Second, the use of AMPK agonists, DS, and drugs may best be rationalized when insulin resistance or obesity leads to aberrant hyperglycemia and hypertriglyceridemia, and obesity that negatively affect fertility. Under these conditions, beneficial effects of AMPK on increasing triglyceride and fatty acid and glucose uptake are important, as long as AMPK agonist exposures are not too high or do not occur during developmental windows of sensitivity. During these windows of sensitivity suppression of anabolism, proliferation, and stemness/potency due to AMPK activity, or overexposure may stunt or kill embryos or cause deleterious epigenetic changes.
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Affiliation(s)
- Elizabeth E Puscheck
- CS Mott Center for Human Growth and Development, Department of Ob/Gyn, Reproductive Endocrinology and Infertility, Wayne State University School of Medicine, 275 East Hancock, Detroit, MI, 48201, USA
| | - Alan Bolnick
- CS Mott Center for Human Growth and Development, Department of Ob/Gyn, Reproductive Endocrinology and Infertility, Wayne State University School of Medicine, 275 East Hancock, Detroit, MI, 48201, USA
- Department of Obstetrics and Gynecology, Kaleida Women's and Children's Hospital Buffalo New York, Buffalo, NY, USA
| | - Awoniyi Awonuga
- CS Mott Center for Human Growth and Development, Department of Ob/Gyn, Reproductive Endocrinology and Infertility, Wayne State University School of Medicine, 275 East Hancock, Detroit, MI, 48201, USA
| | - Yu Yang
- CS Mott Center for Human Growth and Development, Department of Ob/Gyn, Reproductive Endocrinology and Infertility, Wayne State University School of Medicine, 275 East Hancock, Detroit, MI, 48201, USA
- Department of Physiology, Wayne State University School of Medicine, Detroit, MI, 48201, USA
| | - Mohammed Abdulhasan
- CS Mott Center for Human Growth and Development, Department of Ob/Gyn, Reproductive Endocrinology and Infertility, Wayne State University School of Medicine, 275 East Hancock, Detroit, MI, 48201, USA
| | - Quanwen Li
- CS Mott Center for Human Growth and Development, Department of Ob/Gyn, Reproductive Endocrinology and Infertility, Wayne State University School of Medicine, 275 East Hancock, Detroit, MI, 48201, USA
| | - Eric Secor
- Department of Medicine, Integrative Medicine, Hartford Hospital and University of Connecticut, Hartford, CT, 06102, USA
| | - Erica Louden
- Augusta University of Health Sciences, Augusta, GA, USA
| | - Maik Hüttemann
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA
| | - Daniel A Rappolee
- CS Mott Center for Human Growth and Development, Department of Ob/Gyn, Reproductive Endocrinology and Infertility, Wayne State University School of Medicine, 275 East Hancock, Detroit, MI, 48201, USA.
- Department of Physiology, Wayne State University School of Medicine, Detroit, MI, 48201, USA.
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA.
- Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI, USA.
- Institutes for Environmental Health Science, Wayne State University School of Medicine, Detroit, MI, USA.
- Department of Biology, University of Windsor, Windsor, ON, N9B 3P4, Canada.
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Cuykx M, Claes L, Rodrigues RM, Vanhaecke T, Covaci A. Metabolomics profiling of steatosis progression in HepaRG ® cells using sodium valproate. Toxicol Lett 2018; 286:22-30. [PMID: 29355688 DOI: 10.1016/j.toxlet.2017.12.015] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 12/12/2017] [Accepted: 12/20/2017] [Indexed: 02/07/2023]
Abstract
Non-alcoholic Fatty Liver Disease (NAFLD) is a frequently encountered Drug-Induced Liver Injury (DILI). Although this stage of the disease is reversible, it can lead to irreversible damage provoked by non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. Therefore, the assessment of NAFLD is a paramount objective in toxicological screenings of new drug candidates. In this study, a metabolomic fingerprint of NAFLD induced in HepaRG® cells at four dosing schemes by a reference toxicant, sodium valproate (NaVPA), was obtained using liquid-liquid extraction followed by liquid chromatography and accurate mass-mass spectrometry (LC-AM/MS). The combination of a strict design of experiment with a robust detection method, applied on sodium valproate, validated the possibilities of untargeted metabolomics in hepatic toxicological research. Distinctive patterns between exposed and control cells were consistently observed, multivariate analyses selected up to 200 features of interest, revealing hallmark NAFLD-biomarkers, such as diacylglycerol and triglyceride accumulation and carnitine deficiency. Initial toxic responses show increased levels of S-adenosylmethionine and mono-acetylspermidine in combination with only a moderate increase in triglycerides. New specific markers of toxicity have been observed, such as spermidines, creatine, and acetylcholine. The described design of experiment provides a valuable metabolomics platform for mechanistic research of toxicological hazards and identified new markers for steatotic progression.
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Affiliation(s)
- Matthias Cuykx
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium.
| | - Leen Claes
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium
| | - Robim M Rodrigues
- Research group In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Jette, Belgium
| | - Tamara Vanhaecke
- Research group In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Jette, Belgium
| | - Adrian Covaci
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium.
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Abstract
The present contribution describes how in silico models are applied at different stages of the drug discovery process in the pharmaceutical industry. A thorough description of the most relevant computational methods and tools is given along with an in-depth evaluation of their performance in the context of potential genotoxic impurities assessment.The challenges of predicting the outcome of highly complex studies are discussed followed by considerations on how novel ways to manage, store, share and analyze data may advance knowledge and facilitate modeling efforts.
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Affiliation(s)
- Alessandro Brigo
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland.
| | - Wolfgang Muster
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
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8
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Lampa S, Willighagen E, Kohonen P, King A, Vrandečić D, Grafström R, Spjuth O. RDFIO: extending Semantic MediaWiki for interoperable biomedical data management. J Biomed Semantics 2017; 8:35. [PMID: 28870259 PMCID: PMC5584330 DOI: 10.1186/s13326-017-0136-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 08/01/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biological sciences are characterised not only by an increasing amount but also the extreme complexity of its data. This stresses the need for efficient ways of integrating these data in a coherent description of biological systems. In many cases, biological data needs organization before integration. This is not seldom a collaborative effort, and it is thus important that tools for data integration support a collaborative way of working. Wiki systems with support for structured semantic data authoring, such as Semantic MediaWiki, provide a powerful solution for collaborative editing of data combined with machine-readability, so that data can be handled in an automated fashion in any downstream analyses. Semantic MediaWiki lacks a built-in data import function though, which hinders efficient round-tripping of data between interoperable Semantic Web formats such as RDF and the internal wiki format. RESULTS To solve this deficiency, the RDFIO suite of tools is presented, which supports importing of RDF data into Semantic MediaWiki, with metadata needed to export it again in the same RDF format, or ontology. Additionally, the new functionality enables mash-ups of automated data imports combined with manually created data presentations. The application of the suite of tools is demonstrated by importing drug discovery related data about rare diseases from Orphanet and acid dissociation constants from Wikidata. The RDFIO suite of tools is freely available for download via pharmb.io/project/rdfio . CONCLUSIONS Through a set of biomedical demonstrators, it is demonstrated how the new functionality enables a number of usage scenarios where the interoperability of SMW and the wider Semantic Web is leveraged for biomedical data sets, to create an easy to use and flexible platform for exploring and working with biomedical data.
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Affiliation(s)
- Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, SE-751 24, Sweden.
| | - Egon Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, P.O. Box 616, UNS50 Box 19, Maastricht, NL-6200, MD, The Netherlands
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, SE-171 77, Sweden.,Division of Toxicology, Misvik Biology Oy, Turku, Finland
| | | | | | - Roland Grafström
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, SE-171 77, Sweden.,Division of Toxicology, Misvik Biology Oy, Turku, Finland
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, SE-751 24, Sweden
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Boué S, Exner T, Ghosh S, Belcastro V, Dokler J, Page D, Boda A, Bonjour F, Hardy B, Vanscheeuwijck P, Hoeng J, Peitsch M. Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure. F1000Res 2017; 6:12. [PMID: 29123642 PMCID: PMC5657032 DOI: 10.12688/f1000research.10493.2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/25/2017] [Indexed: 01/24/2023] Open
Abstract
The US FDA defines modified risk tobacco products (MRTPs) as products that aim to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products. Establishing a product’s potential as an MRTP requires scientific substantiation including toxicity studies and measures of disease risk relative to those of cigarette smoking. Best practices encourage verification of the data from such studies through sharing and open standards. Building on the experience gained from the OpenTox project, a proof-of-concept database and website (
INTERVALS) has been developed to share results from both
in vivo inhalation studies and
in vitro studies conducted by Philip Morris International R&D to assess candidate MRTPs. As datasets are often generated by diverse methods and standards, they need to be traceable, curated, and the methods used well described so that knowledge can be gained using data science principles and tools. The data-management framework described here accounts for the latest standards of data sharing and research reproducibility. Curated data and methods descriptions have been prepared in ISA-Tab format and stored in a database accessible via a search portal on the INTERVALS website. The portal allows users to browse the data by study or mechanism (e.g., inflammation, oxidative stress) and obtain information relevant to study design, methods, and the most important results. Given the successful development of the initial infrastructure, the goal is to grow this initiative and establish a public repository for 21
st-century preclinical systems toxicology MRTP assessment data and results that supports open data principles.
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Affiliation(s)
- Stéphanie Boué
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | | | | | | | - Joh Dokler
- Douglas Connect GmbH, Zeiningen, Switzerland
| | - David Page
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Akash Boda
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Filipe Bonjour
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Barry Hardy
- Douglas Connect GmbH, Zeiningen, Switzerland
| | | | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Manuel Peitsch
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
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Boué S, Exner T, Ghosh S, Belcastro V, Dokler J, Page D, Boda A, Bonjour F, Hardy B, Vanscheeuwijck P, Hoeng J, Peitsch M. Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure. F1000Res 2017; 6:12. [PMID: 29123642 DOI: 10.12688/f1000research.10493.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/03/2017] [Indexed: 12/11/2022] Open
Abstract
The US FDA defines modified risk tobacco products (MRTPs) as products that aim to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products. Establishing a product's potential as an MRTP requires scientific substantiation including toxicity studies and measures of disease risk relative to those of cigarette smoking. Best practices encourage verification of the data from such studies through sharing and open standards. Building on the experience gained from the OpenTox project, a proof-of-concept database and website ( INTERVALS) has been developed to share results from both in vivo inhalation studies and in vitro studies conducted by Philip Morris International R&D to assess candidate MRTPs. As datasets are often generated by diverse methods and standards, they need to be traceable, curated, and the methods used well described so that knowledge can be gained using data science principles and tools. The data-management framework described here accounts for the latest standards of data sharing and research reproducibility. Curated data and methods descriptions have been prepared in ISA-Tab format and stored in a database accessible via a search portal on the INTERVALS website. The portal allows users to browse the data by study or mechanism (e.g., inflammation, oxidative stress) and obtain information relevant to study design, methods, and the most important results. Given the successful development of the initial infrastructure, the goal is to grow this initiative and establish a public repository for 21 st-century preclinical systems toxicology MRTP assessment data and results that supports open data principles.
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Affiliation(s)
- Stéphanie Boué
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | | | | | | | - Joh Dokler
- Douglas Connect GmbH, Zeiningen, Switzerland
| | - David Page
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Akash Boda
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Filipe Bonjour
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Barry Hardy
- Douglas Connect GmbH, Zeiningen, Switzerland
| | | | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Manuel Peitsch
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
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11
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Abstract
The adverse outcome pathway (AOP) framework provides an alternative to traditional in vivo experiments for the risk assessment of chemicals. AOPs consist of a number of key events (KEs) linked by key event relationships across a range of biological organization backed by scientific evidence. The first KE in the pathway is the molecular initiating event (MIE)-the initial chemical trigger that starts an AOP. Over the past 3 years the AOP conceptual framework has gained a large amount of momentum in toxicology as an alternative to animal methods, and so the MIE has come into the spotlight. What is an MIE? How can MIEs be measured or predicted? What research is currently contributing to our understanding of MIEs? In this Perspective we outline answers to these key questions.
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Affiliation(s)
- Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Steve Gutsell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Paul J Russell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
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12
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Bandrowski A, Brinkman R, Brochhausen M, Brush MH, Bug B, Chibucos MC, Clancy K, Courtot M, Derom D, Dumontier M, Fan L, Fostel J, Fragoso G, Gibson F, Gonzalez-Beltran A, Haendel MA, He Y, Heiskanen M, Hernandez-Boussard T, Jensen M, Lin Y, Lister AL, Lord P, Malone J, Manduchi E, McGee M, Morrison N, Overton JA, Parkinson H, Peters B, Rocca-Serra P, Ruttenberg A, Sansone SA, Scheuermann RH, Schober D, Smith B, Soldatova LN, Stoeckert CJ, Taylor CF, Torniai C, Turner JA, Vita R, Whetzel PL, Zheng J. The Ontology for Biomedical Investigations. PLoS One 2016; 11:e0154556. [PMID: 27128319 PMCID: PMC4851331 DOI: 10.1371/journal.pone.0154556] [Citation(s) in RCA: 133] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2016] [Accepted: 04/17/2016] [Indexed: 12/18/2022] Open
Abstract
The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.
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Affiliation(s)
- Anita Bandrowski
- University of California San Diego, La Jolla, California, United States of America
| | - Ryan Brinkman
- British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Mathias Brochhausen
- University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Matthew H. Brush
- Oregon Health and Science University, Portland, Oregon, United States of America
| | - Bill Bug
- Drexel University College of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Marcus C. Chibucos
- University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Kevin Clancy
- Thermo Fisher Scientific, Carlsbad, California, United States of America
| | | | - Dirk Derom
- The Vrije Universiteit Brussel, Ixelles, Brussels, Belgium
| | - Michel Dumontier
- Stanford University, Stanford, California, United States of America
| | - Liju Fan
- Ontology Workshop, LLC, Columbia, Maryland, United States of America
| | - Jennifer Fostel
- National Toxicology Program, NIEHS, National Institutes of Health, Research Triangle Park, North Carolina, United States of America
| | - Gilberto Fragoso
- Center for Biomedical Informatics and Information Technology, National Institutes of Health, Rockville, Maryland, United States of America
| | - Frank Gibson
- Royal Society of Chemistry, Cambridge, Cambridgeshire, United Kingdom
| | | | - Melissa A. Haendel
- Oregon Health and Science University, Portland, Oregon, United States of America
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Mervi Heiskanen
- National Cancer Institute, Rockville, Maryland, United States of America
| | | | - Mark Jensen
- University at Buffalo, Buffalo, New York, United States of America
| | - Yu Lin
- University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | | | - Phillip Lord
- Newcastle University, Newcastle-upon-Tyne, Tyne and Wear, United Kingdom
| | - James Malone
- European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Elisabetta Manduchi
- University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Monnie McGee
- Southern Methodist University, Dallas, Texas, United States of America
| | - Norman Morrison
- The University of Manchester, Manchester, Greater Manchester, United Kingdom
| | - James A. Overton
- La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America
| | - Helen Parkinson
- European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America
| | | | - Alan Ruttenberg
- University at Buffalo, Buffalo, New York, United States of America
| | | | | | - Daniel Schober
- Leibniz Institute of Plant Biochemistry, Halle, Saxony-Anhalt, Germany
| | - Barry Smith
- University at Buffalo, Buffalo, New York, United States of America
| | | | | | - Chris F. Taylor
- European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
| | - Carlo Torniai
- Oregon Health and Science University, Portland, Oregon, United States of America
| | - Jessica A. Turner
- Georgia State University, Atlanta, Georgia, United States of America
| | - Randi Vita
- La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America
| | - Patricia L. Whetzel
- University of California San Diego, La Jolla, California, United States of America
| | - Jie Zheng
- University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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13
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Zhu H, Bouhifd M, Donley E, Egnash L, Kleinstreuer N, Kroese ED, Liu Z, Luechtefeld T, Palmer J, Pamies D, Shen J, Strauss V, Wu S, Hartung T. Supporting read-across using biological data. ALTEX 2016; 33:167-82. [PMID: 26863516 PMCID: PMC4834201 DOI: 10.14573/altex.1601252] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 02/09/2016] [Indexed: 01/08/2023]
Abstract
Read-across, i.e. filling toxicological data gaps by relating to similar chemicals, for which test data are available, is usually done based on chemical similarity. Besides structure and physico-chemical properties, however, biological similarity based on biological data adds extra strength to this process. In the context of developing Good Read-Across Practice guidance, a number of case studies were evaluated to demonstrate the use of biological data to enrich read-across. In the simplest case, chemically similar substances also show similar test results in relevant in vitro assays. This is a well-established method for the read-across of e.g. genotoxicity assays. Larger datasets of biological and toxicological properties of hundreds and thousands of substances become increasingly available enabling big data approaches in read-across studies. Several case studies using various big data sources are described in this paper. An example is given for the US EPA's ToxCast dataset allowing read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example for REACH registration data enhancing read-across for acute toxicity studies is given. A different approach is taken using omics data to establish biological similarity: Examples are given for stem cell models in vitro and short-term repeated dose studies in rats in vivo to support read-across and category formation. These preliminary biological data-driven read-across studies highlight the road to the new generation of read-across approaches that can be applied in chemical safety assessment.
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Affiliation(s)
- Hao Zhu
- Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Mounir Bouhifd
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | | | - Laura Egnash
- Stemina Biomarker Discovery Inc., Madison, WI, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - E Dinant Kroese
- Risk Analysis for Products in Development, TNO Zeist, The Netherlands
| | | | - Thomas Luechtefeld
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | | | - David Pamies
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Jie Shen
- Research Institute for Fragrance Materials, Inc. Woodcliff Lake, New Jersey, USA
| | - Volker Strauss
- BASF Aktiengesellschaft, Experimental Toxicology and Ecology, Ludwigshafen, Germany
| | | | - Thomas Hartung
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
- University of Konstanz, CAAT-Europe, Konstanz, Germany
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14
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Grafström RC, Nymark P, Hongisto V, Spjuth O, Ceder R, Willighagen E, Hardy B, Kaski S, Kohonen P. Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures. Altern Lab Anim 2016; 43:325-32. [PMID: 26551289 DOI: 10.1177/026119291504300506] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
This paper outlines the work for which Roland Grafström and Pekka Kohonen were awarded the 2014 Lush Science Prize. The research activities of the Grafström laboratory have, for many years, covered cancer biology studies, as well as the development and application of toxicity-predictive in vitro models to determine chemical safety. Through the integration of in silico analyses of diverse types of genomics data (transcriptomic and proteomic), their efforts have proved to fit well into the recently-developed Adverse Outcome Pathway paradigm. Genomics analysis within state-of-the-art cancer biology research and Toxicology in the 21st Century concepts share many technological tools. A key category within the Three Rs paradigm is the Replacement of animals in toxicity testing with alternative methods, such as bioinformatics-driven analyses of data obtained from human cell cultures exposed to diverse toxicants. This work was recently expanded within the pan-European SEURAT-1 project (Safety Evaluation Ultimately Replacing Animal Testing), to replace repeat-dose toxicity testing with data-rich analyses of sophisticated cell culture models. The aims and objectives of the SEURAT project have been to guide the application, analysis, interpretation and storage of 'omics' technology-derived data within the service-oriented sub-project, ToxBank. Particularly addressing the Lush Science Prize focus on the relevance of toxicity pathways, a 'data warehouse' that is under continuous expansion, coupled with the development of novel data storage and management methods for toxicology, serve to address data integration across multiple 'omics' technologies. The prize winners' guiding principles and concepts for modern knowledge management of toxicological data are summarised. The translation of basic discovery results ranged from chemical-testing and material-testing data, to information relevant to human health and environmental safety.
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Affiliation(s)
- Roland C Grafström
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Vesa Hongisto
- Toxicology Department, Misvik Biology Corporation, Turku, Finland
| | - Ola Spjuth
- Department of Medical Epidemiology and Biostatistics, Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden and Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Rebecca Ceder
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Egon Willighagen
- Department of Bioinformatics-BiGCat, Maastricht University, Maastricht, The Netherlands
| | - Barry Hardy
- Douglas Connect GmbH, Zeiningen, Switzerland
| | - Samuel Kaski
- Helsinki Institute for Information Technology, Aalto University, Department of Computer Science, Helsinki, Finland
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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15
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González-Beltrán A, Li P, Zhao J, Avila-Garcia MS, Roos M, Thompson M, van der Horst E, Kaliyaperumal R, Luo R, Lee TL, Lam TW, Edmunds SC, Sansone SA, Rocca-Serra P. From Peer-Reviewed to Peer-Reproduced in Scholarly Publishing: The Complementary Roles of Data Models and Workflows in Bioinformatics. PLoS One 2015; 10:e0127612. [PMID: 26154165 PMCID: PMC4495984 DOI: 10.1371/journal.pone.0127612] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Accepted: 04/16/2015] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Reproducing the results from a scientific paper can be challenging due to the absence of data and the computational tools required for their analysis. In addition, details relating to the procedures used to obtain the published results can be difficult to discern due to the use of natural language when reporting how experiments have been performed. The Investigation/Study/Assay (ISA), Nanopublications (NP), and Research Objects (RO) models are conceptual data modelling frameworks that can structure such information from scientific papers. Computational workflow platforms can also be used to reproduce analyses of data in a principled manner. We assessed the extent by which ISA, NP, and RO models, together with the Galaxy workflow system, can capture the experimental processes and reproduce the findings of a previously published paper reporting on the development of SOAPdenovo2, a de novo genome assembler. RESULTS Executable workflows were developed using Galaxy, which reproduced results that were consistent with the published findings. A structured representation of the information in the SOAPdenovo2 paper was produced by combining the use of ISA, NP, and RO models. By structuring the information in the published paper using these data and scientific workflow modelling frameworks, it was possible to explicitly declare elements of experimental design, variables, and findings. The models served as guides in the curation of scientific information and this led to the identification of inconsistencies in the original published paper, thereby allowing its authors to publish corrections in the form of an errata. AVAILABILITY SOAPdenovo2 scripts, data, and results are available through the GigaScience Database: http://dx.doi.org/10.5524/100044; the workflows are available from GigaGalaxy: http://galaxy.cbiit.cuhk.edu.hk; and the representations using the ISA, NP, and RO models are available through the SOAPdenovo2 case study website http://isa-tools.github.io/soapdenovo2/. CONTACT philippe.rocca-serra@oerc.ox.ac.uk and susanna-assunta.sansone@oerc.ox.ac.uk.
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Affiliation(s)
| | - Peter Li
- GigaScience, BGI HK Research Institute, 16 Dai Fu Street, Tai Po Industrial Estate, Hong Kong, People’s Republic of China
| | - Jun Zhao
- InfoLab21, Lancaster University, Bailrigg, Lancaster, LA1 4WA, United Kingdom
| | - Maria Susana Avila-Garcia
- Nuffield Department of Medicine, Experimental Medicine Division, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, United Kingdom
| | - Marco Roos
- Department of Human Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Mark Thompson
- Department of Human Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Eelke van der Horst
- Department of Human Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Rajaram Kaliyaperumal
- Department of Human Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Ruibang Luo
- HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory & Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong, People’s Republic of China
| | - Tin-Lap Lee
- School of Biomedical Sciences and CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, Hong Kong, People’s Republic of China
| | - Tak-wah Lam
- HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory & Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong, People’s Republic of China
| | - Scott C. Edmunds
- GigaScience, BGI HK Research Institute, 16 Dai Fu Street, Tai Po Industrial Estate, Hong Kong, People’s Republic of China
| | | | - Philippe Rocca-Serra
- Oxford e-Research Centre, University of Oxford, 7 Keble Road, OX1 3QG, United Kingdom
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16
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Marchese Robinson RL, Cronin MTD, Richarz AN, Rallo R. An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology. Beilstein J Nanotechnol 2015; 6:1978-99. [PMID: 26665069 PMCID: PMC4660926 DOI: 10.3762/bjnano.6.202] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 08/27/2015] [Indexed: 05/20/2023]
Abstract
Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed. These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a "Toy Dataset" presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.
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Affiliation(s)
- Richard L Marchese Robinson
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom
| | - Andrea-Nicole Richarz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom
| | - Robert Rallo
- Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Paisos Catalans 26, 43007 Tarragona, Catalunya, Spain
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17
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Jeliazkova N, Chomenidis C, Doganis P, Fadeel B, Grafström R, Hardy B, Hastings J, Hegi M, Jeliazkov V, Kochev N, Kohonen P, Munteanu CR, Sarimveis H, Smeets B, Sopasakis P, Tsiliki G, Vorgrimmler D, Willighagen E. The eNanoMapper database for nanomaterial safety information. Beilstein J Nanotechnol 2015; 6:1609-34. [PMID: 26425413 PMCID: PMC4578352 DOI: 10.3762/bjnano.6.165] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 07/03/2015] [Indexed: 05/20/2023]
Abstract
BACKGROUND The NanoSafety Cluster, a cluster of projects funded by the European Commision, identified the need for a computational infrastructure for toxicological data management of engineered nanomaterials (ENMs). Ontologies, open standards, and interoperable designs were envisioned to empower a harmonized approach to European research in nanotechnology. This setting provides a number of opportunities and challenges in the representation of nanomaterials data and the integration of ENM information originating from diverse systems. Within this cluster, eNanoMapper works towards supporting the collaborative safety assessment for ENMs by creating a modular and extensible infrastructure for data sharing, data analysis, and building computational toxicology models for ENMs. RESULTS The eNanoMapper database solution builds on the previous experience of the consortium partners in supporting diverse data through flexible data storage, open source components and web services. We have recently described the design of the eNanoMapper prototype database along with a summary of challenges in the representation of ENM data and an extensive review of existing nano-related data models, databases, and nanomaterials-related entries in chemical and toxicogenomic databases. This paper continues with a focus on the database functionality exposed through its application programming interface (API), and its use in visualisation and modelling. Considering the preferred community practice of using spreadsheet templates, we developed a configurable spreadsheet parser facilitating user friendly data preparation and data upload. We further present a web application able to retrieve the experimental data via the API and analyze it with multiple data preprocessing and machine learning algorithms. CONCLUSION We demonstrate how the eNanoMapper database is used to import and publish online ENM and assay data from several data sources, how the "representational state transfer" (REST) API enables building user friendly interfaces and graphical summaries of the data, and how these resources facilitate the modelling of reproducible quantitative structure-activity relationships for nanomaterials (NanoQSAR).
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Affiliation(s)
| | | | - Philip Doganis
- National Technical University of Athens, School of Chemical Engineering, Athens, Greece
| | | | | | - Barry Hardy
- Douglas Connect GmbH, Zeiningen, Switzerland
| | - Janna Hastings
- European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
| | - Markus Hegi
- Douglas Connect GmbH, Zeiningen, Switzerland
| | | | - Nikolay Kochev
- Ideaconsult Ltd., Sofia, Bulgaria
- Department of Analytical Chemistry and Computer Chemistry, University of Plovdiv, Plovdiv, Bulgaria
| | | | - Cristian R Munteanu
- Department of Bioinformatics, NUTRIM, Maastricht University, Maastricht, The Netherlands
- Computer Science Faculty, University of A Coruna, A Coruña, Spain
| | - Haralambos Sarimveis
- National Technical University of Athens, School of Chemical Engineering, Athens, Greece
| | - Bart Smeets
- Department of Bioinformatics, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Pantelis Sopasakis
- National Technical University of Athens, School of Chemical Engineering, Athens, Greece
- IMT Institute for Advanced Studies Lucca, Lucca, Italy
| | - Georgia Tsiliki
- National Technical University of Athens, School of Chemical Engineering, Athens, Greece
| | | | - Egon Willighagen
- Department of Bioinformatics, NUTRIM, Maastricht University, Maastricht, The Netherlands
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18
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Abstract
BACKGROUND Reporting and sharing experimental metadata- such as the experimental design, characteristics of the samples, and procedures applied, along with the analysis results, in a standardised manner ensures that datasets are comprehensible and, in principle, reproducible, comparable and reusable. Furthermore, sharing datasets in formats designed for consumption by humans and machines will also maximize their use. The Investigation/Study/Assay (ISA) open source metadata tracking framework facilitates standards-compliant collection, curation, visualization, storage and sharing of datasets, leveraging on other platforms to enable analysis and publication. The ISA software suite includes several components used in increasingly diverse set of life science and biomedical domains; it is underpinned by a general-purpose format, ISA-Tab, and conversions exist into formats required by public repositories. While ISA-Tab works well mainly as a human readable format, we have also implemented a linked data approach to semantically define the ISA-Tab syntax. RESULTS We present a semantic web representation of the ISA-Tab syntax that complements ISA-Tab's syntactic interoperability with semantic interoperability. We introduce the linkedISA conversion tool from ISA-Tab to the Resource Description Framework (RDF), supporting mappings from the ISA syntax to multiple community-defined, open ontologies and capitalising on user-provided ontology annotations in the experimental metadata. We describe insights of the implementation and how annotations can be expanded driven by the metadata. We applied the conversion tool as part of Bio-GraphIIn, a web-based application supporting integration of the semantically-rich experimental descriptions. Designed in a user-friendly manner, the Bio-GraphIIn interface hides most of the complexities to the users, exposing a familiar tabular view of the experimental description to allow seamless interaction with the RDF representation, and visualising descriptors to drive the query over the semantic representation of the experimental design. In addition, we defined queries over the linkedISA RDF representation and demonstrated its use over the linkedISA conversion of datasets from Nature' Scientific Data online publication. CONCLUSIONS Our linked data approach has allowed us to: 1) make the ISA-Tab semantics explicit and machine-processable, 2) exploit the existing ontology-based annotations in the ISA-Tab experimental descriptions, 3) augment the ISA-Tab syntax with new descriptive elements, 4) visualise and query elements related to the experimental design. Reasoning over ISA-Tab metadata and associated data will facilitate data integration and knowledge discovery.
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Affiliation(s)
| | - Eamonn Maguire
- Oxford e-Research Centre, University of Oxford, Oxford, OX1 3QG, UK
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Cases M, Briggs K, Steger-Hartmann T, Pognan F, Marc P, Kleinöder T, Schwab CH, Pastor M, Wichard J, Sanz F. The eTOX data-sharing project to advance in silico drug-induced toxicity prediction. Int J Mol Sci 2014; 15:21136-54. [PMID: 25405742 DOI: 10.3390/ijms151121136] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Accepted: 10/20/2014] [Indexed: 11/16/2022] Open
Abstract
The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage.
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Zhu H, Zhang J, Kim MT, Boison A, Sedykh A, Moran K. Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants. Chem Res Toxicol 2014; 27:1643-51. [PMID: 25195622 PMCID: PMC4203392 DOI: 10.1021/tx500145h] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2014] [Indexed: 12/17/2022]
Abstract
High-throughput screening (HTS) assays that measure the in vitro toxicity of environmental compounds have been widely applied as an alternative to in vivo animal tests of chemical toxicity. Current HTS studies provide the community with rich toxicology information that has the potential to be integrated into toxicity research. The available in vitro toxicity data is updated daily in structured formats (e.g., deposited into PubChem and other data-sharing web portals) or in an unstructured way (papers, laboratory reports, toxicity Web site updates, etc.). The information derived from the current toxicity data is so large and complex that it becomes difficult to process using available database management tools or traditional data processing applications. For this reason, it is necessary to develop a big data approach when conducting modern chemical toxicity research. In vitro data for a compound, obtained from meaningful bioassays, can be viewed as a response profile that gives detailed information about the compound's ability to affect relevant biological proteins/receptors. This information is critical for the evaluation of complex bioactivities (e.g., animal toxicities) and grows rapidly as big data in toxicology communities. This review focuses mainly on the existing structured in vitro data (e.g., PubChem data sets) as response profiles for compounds of environmental interest (e.g., potential human/animal toxicants). Potential modeling and mining tools to use the current big data pool in chemical toxicity research are also described.
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Affiliation(s)
- Hao Zhu
- Department
of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
- The
Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Jun Zhang
- Department
of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
- The
Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Marlene T. Kim
- Department
of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
- The
Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Abena Boison
- Department
of Chemistry, Rutgers University, Camden, New Jersey 08102, United States
| | - Alexander Sedykh
- The
Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102, United States
| | - Kimberlee Moran
- Center
for Forensic Science Research and Education, Willow Grove, Pennsylvania 19090, United States
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Kohonen P, Ceder R, Smit I, Hongisto V, Myatt G, Hardy B, Spjuth O, Grafström R. Cancer biology, toxicology and alternative methods development go hand-in-hand. Basic Clin Pharmacol Toxicol 2014; 115:50-8. [PMID: 24779563 DOI: 10.1111/bcpt.12257] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Accepted: 04/21/2014] [Indexed: 12/13/2022]
Abstract
Toxicological research faces the challenge of integrating knowledge from diverse fields and novel technological developments generally in the biological and medical sciences. We discuss herein the fact that the multiple facets of cancer research, including discovery related to mechanisms, treatment and diagnosis, overlap many up and coming interest areas in toxicology, including the need for improved methods and analysis tools. Common to both disciplines, in vitro and in silico methods serve as alternative investigation routes to animal studies. Knowledge on cancer development helps in understanding the relevance of chemical toxicity studies in cell models, and many bioinformatics-based cancer biomarker discovery tools are also applicable to computational toxicology. Robotics-aided, cell-based, high-throughput screening, microscale immunostaining techniques and gene expression profiling analyses are common tools in cancer research, and when sequentially combined, form a tiered approach to structured safety evaluation of thousands of environmental agents, novel chemicals or engineered nanomaterials. Comprehensive tumour data collections in databases have been translated into clinically useful data, and this concept serves as template for computer-driven evaluation of toxicity data into meaningful results. Future 'cancer research-inspired knowledge management' of toxicological data will aid the translation of basic discovery results and chemicals- and materials-testing data to information relevant to human health and environmental safety.
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Affiliation(s)
- Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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22
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González-Beltrán A, Maguire E, Sansone SA, Rocca-Serra P. linkedISA: semantic representation of ISA-Tab experimental metadata. BMC Bioinformatics 2014; 15. [PMID: 25472428 PMCID: PMC4255742 DOI: 10.1186/1471-2105-15-s14-s4,] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND Reporting and sharing experimental metadata- such as the experimental design, characteristics of the samples, and procedures applied, along with the analysis results, in a standardised manner ensures that datasets are comprehensible and, in principle, reproducible, comparable and reusable. Furthermore, sharing datasets in formats designed for consumption by humans and machines will also maximize their use. The Investigation/Study/Assay (ISA) open source metadata tracking framework facilitates standards-compliant collection, curation, visualization, storage and sharing of datasets, leveraging on other platforms to enable analysis and publication. The ISA software suite includes several components used in increasingly diverse set of life science and biomedical domains; it is underpinned by a general-purpose format, ISA-Tab, and conversions exist into formats required by public repositories. While ISA-Tab works well mainly as a human readable format, we have also implemented a linked data approach to semantically define the ISA-Tab syntax. RESULTS We present a semantic web representation of the ISA-Tab syntax that complements ISA-Tab's syntactic interoperability with semantic interoperability. We introduce the linkedISA conversion tool from ISA-Tab to the Resource Description Framework (RDF), supporting mappings from the ISA syntax to multiple community-defined, open ontologies and capitalising on user-provided ontology annotations in the experimental metadata. We describe insights of the implementation and how annotations can be expanded driven by the metadata. We applied the conversion tool as part of Bio-GraphIIn, a web-based application supporting integration of the semantically-rich experimental descriptions. Designed in a user-friendly manner, the Bio-GraphIIn interface hides most of the complexities to the users, exposing a familiar tabular view of the experimental description to allow seamless interaction with the RDF representation, and visualising descriptors to drive the query over the semantic representation of the experimental design. In addition, we defined queries over the linkedISA RDF representation and demonstrated its use over the linkedISA conversion of datasets from Nature' Scientific Data online publication. CONCLUSIONS Our linked data approach has allowed us to: 1) make the ISA-Tab semantics explicit and machine-processable, 2) exploit the existing ontology-based annotations in the ISA-Tab experimental descriptions, 3) augment the ISA-Tab syntax with new descriptive elements, 4) visualise and query elements related to the experimental design. Reasoning over ISA-Tab metadata and associated data will facilitate data integration and knowledge discovery.
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Affiliation(s)
| | - Eamonn Maguire
- Oxford e-Research Centre, University of Oxford, Oxford, OX1 3QG, UK
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23
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Scholz S, Sela E, Blaha L, Braunbeck T, Galay-Burgos M, García-Franco M, Guinea J, Klüver N, Schirmer K, Tanneberger K, Tobor-Kapłon M, Witters H, Belanger S, Benfenati E, Creton S, Cronin MT, Eggen RI, Embry M, Ekman D, Gourmelon A, Halder M, Hardy B, Hartung T, Hubesch B, Jungmann D, Lampi MA, Lee L, Léonard M, Küster E, Lillicrap A, Luckenbach T, Murk AJ, Navas JM, Peijnenburg W, Repetto G, Salinas E, Schüürmann G, Spielmann H, Tollefsen KE, Walter-Rohde S, Whale G, Wheeler JR, Winter MJ. A European perspective on alternatives to animal testing for environmental hazard identification and risk assessment. Regul Toxicol Pharmacol 2013; 67:506-30. [DOI: 10.1016/j.yrtph.2013.10.003] [Citation(s) in RCA: 127] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Revised: 10/02/2013] [Accepted: 10/16/2013] [Indexed: 12/20/2022]
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Nyström-Persson J, Igarashi Y, Ito M, Morita M, Nakatsu N, Yamada H, Mizuguchi K. Toxygates: interactive toxicity analysis on a hybrid microarray and linked data platform. Bioinformatics 2013; 29:3080-6. [DOI: 10.1093/bioinformatics/btt531] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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