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Gene Expression Landscape of Chronic Myeloid Leukemia K562 Cells Overexpressing the Tumor Suppressor Gene PTPRG. Int J Mol Sci 2022; 23:ijms23179899. [PMID: 36077295 PMCID: PMC9456469 DOI: 10.3390/ijms23179899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/09/2022] [Accepted: 08/15/2022] [Indexed: 11/24/2022] Open
Abstract
This study concerns the analysis of the modulation of Chronic Myeloid Leukemia (CML) cell model K562 transcriptome following transfection with the tumor suppressor gene encoding for Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and treatment with the tyrosine kinase inhibitor (TKI) Imatinib. Specifically, we aimed at identifying genes whose level of expression is altered by PTPRG modulation and Imatinib concentration. Statistical tests as differential expression analysis (DEA) supported by gene set enrichment analysis (GSEA) and modern methods of ontological term analysis are presented along with some results of current interest for forthcoming experimental research in the field of the transcriptomic landscape of CML. In particular, we present two methods that differ in the order of the analysis steps. After a gene selection based on fold-change value thresholding, we applied statistical tests to select differentially expressed genes. Therefore, we applied two different methods on the set of differentially expressed genes. With the first method (Method 1), we implemented GSEA, followed by the identification of transcription factors. With the second method (Method 2), we first selected the transcription factors from the set of differentially expressed genes and implemented GSEA on this set. Method 1 is a standard method commonly used in this type of analysis, while Method 2 is unconventional and is motivated by the intention to identify transcription factors more specifically involved in biological processes relevant to the CML condition. Both methods have been equipped in ontological knowledge mining and word cloud analysis, as elements of novelty in our analytical procedure. Data analysis identified RARG and CD36 as a potential PTPRG up-regulated genes, suggesting a possible induction of cell differentiation toward an erithromyeloid phenotype. The prediction was confirmed at the mRNA and protein level, further validating the approach and identifying a new molecular mechanism of tumor suppression governed by PTPRG in a CML context.
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López-López E, Bajorath J, Medina-Franco JL. Informatics for Chemistry, Biology, and Biomedical Sciences. J Chem Inf Model 2020; 61:26-35. [PMID: 33382611 DOI: 10.1021/acs.jcim.0c01301] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Informatics is growing across disciplines, impacting several areas of chemistry, biology, and biomedical sciences. Besides the well-established bioinformatics discipline, other informatics-based interdisciplinary fields have been evolving over time, such as chemoinformatics and biomedical informatics. Other related research areas such as pharmacoinformatics, food informatics, epi-informatics, materials informatics, and neuroinformatics have emerged more recently and continue to develop as independent subdisciplines. The goals and impacts of each of these disciplines have typically been separately reviewed in the literature. Hence, it remains challenging to identify commonalities and key differences. Herein, we discuss in context three major informatics disciplines in the natural and life sciences including bioinformatics, chemoinformatics, and biomedical informatics and briefly comment on related subdisciplines. We focus the discussion on the definitions, historical background, actual impact, main similarities, and differences and evaluate the dissemination and teaching of bioinformatics, chemoinformatics, and biomedical informatics.
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Affiliation(s)
- Edgar López-López
- Department of Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV), Av Instituto Politécnico Nacional 2508, Mexico City 07360, Mexico
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Endenicher Allee 19c, Rheinische Friedrich-Wilhelms-Universität, D-53115 Bonn, Germany
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Av Universidad 3000, Mexico City 04510, Mexico
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Rahman RU, Liebhoff AM, Bansal V, Fiosins M, Rajput A, Sattar A, Magruder DS, Madan S, Sun T, Gautam A, Heins S, Liwinski T, Bethune J, Trenkwalder C, Fluck J, Mollenhauer B, Bonn S. SEAweb: the small RNA Expression Atlas web application. Nucleic Acids Res 2020; 48:D204-D219. [PMID: 31598718 PMCID: PMC6943056 DOI: 10.1093/nar/gkz869] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/14/2019] [Accepted: 10/01/2019] [Indexed: 12/12/2022] Open
Abstract
We present the Small RNA Expression Atlas (SEAweb), a web application that allows for the interactive querying, visualization and analysis of known and novel small RNAs across 10 organisms. It contains sRNA and pathogen expression information for over 4200 published samples with standardized search terms and ontologies. In addition, SEAweb allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEAweb's user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns. We provide evidence for SEAweb's fidelity by (i) generating a set of 591 tissue specific miRNAs across 29 tissues, (ii) finding known and novel bacterial and viral infections across diseases and (iii) determining a Parkinson's disease-specific blood biomarker signature using novel data. We believe that SEAweb's simple semantic search interface, the flexible interactive reports and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases and organisms.
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Affiliation(s)
- Raza-Ur Rahman
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Anna-Maria Liebhoff
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Vikas Bansal
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- German Center for Neurodegenerative Diseases, 72076 Tübingen, Germany
| | - Maksims Fiosins
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- German Center for Neurodegenerative Diseases, 72076 Tübingen, Germany
- Genevention GmbH, 37079 Göttingen, Germany
| | - Ashish Rajput
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Abdul Sattar
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Daniel S Magruder
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Genevention GmbH, 37079 Göttingen, Germany
| | - Sumit Madan
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Rheinische Friedrich-Wilhelms-Universität Bonn, 53113 Bonn, Germany
| | - Ting Sun
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- Department of Neurogenetics, Max Planck Institute of Experimental Medicine, 37075 Göttingen, Germany
| | - Abhivyakti Gautam
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Sven Heins
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Timur Liwinski
- Department of Medicine, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Jörn Bethune
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Claudia Trenkwalder
- Paracelsus-Elena-Klinik, 34128 Kassel, Germany
- Department of Neurosurgery, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Juliane Fluck
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Institute of Geodesy and Geoinformation, University of Bonn, 53115 Bonn, Germany
- German National Library of Medicine (ZB MED) - Information Centre for Life Sciences, 53115 Bonn, Germany
| | - Brit Mollenhauer
- Paracelsus-Elena-Klinik, 34128 Kassel, Germany
- Institute of Neurology, University Medical Center Göttingen, 37075 Göttingen, Germany
| | - Stefan Bonn
- Institute of Medical Systems Biology, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
- German Center for Neurodegenerative Diseases, 72076 Tübingen, Germany
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Takahashi L, Takahashi K. Visualizing Scientists' Cognitive Representation of Materials Data through the Application of Ontology. J Phys Chem Lett 2019; 10:7482-7491. [PMID: 31730356 DOI: 10.1021/acs.jpclett.9b02976] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The introduction of data science as a viable new approach to research has led toward the establishment of materials informatics. However, issues relating to the infrastructure of data collection and organization in materials science have hindered the development of materials informatics. Issues related to data quality, conflicting terminologies between subfields, and inconsistent recording practices make it difficult to share data and implement data science. Furthermore, one can consider that scientific discoveries have occurred via the rules that are unconsciously defined by the scientist's mind, which has made scientific discovery an unintentional process. Here, ontology is proposed as a new way to structure databases as well as model scientific understandings of data. By implementing ontology during the database creation process, it not only becomes possible to define and visualize the experiences and knowledge held by researchers but also provides a way of creating a field-wide standard of defining data, the ability to incorporate data semantics, a method to increase the solid choice of descriptors for determining the materials' properties, and the space to merge databases in a more interactive and coherent manner. Ontology can also help improve database management by providing a way to incorporate new scientific discoveries into existing databases, which can have a positive effect on the search for new materials and material design.
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Affiliation(s)
- Lauren Takahashi
- Department of Chemistry , Hokkaido University , Sapporo 060-8510 , Japan
- Center for Materials research by Information Integration (CMI2) , National Institute for Materials Science (NIMS) , 1-2-1 Sengen , Tsukuba , Ibaraki 305-0047 , Japan
| | - Keisuke Takahashi
- Department of Chemistry , Hokkaido University , Sapporo 060-8510 , Japan
- Center for Materials research by Information Integration (CMI2) , National Institute for Materials Science (NIMS) , 1-2-1 Sengen , Tsukuba , Ibaraki 305-0047 , Japan
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Trellet M, Férey N, Flotyński J, Baaden M, Bourdot P. Semantics for an Integrative and Immersive Pipeline Combining Visualization and Analysis of Molecular Data. J Integr Bioinform 2018; 15:/j/jib.ahead-of-print/jib-2018-0004/jib-2018-0004.xml. [PMID: 29982236 PMCID: PMC6167042 DOI: 10.1515/jib-2018-0004] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 05/11/2018] [Indexed: 12/14/2022] Open
Abstract
The advances made in recent years in the field of structural biology significantly increased the throughput and complexity of data that scientists have to deal with. Combining and analyzing such heterogeneous amounts of data became a crucial time consumer in the daily tasks of scientists. However, only few efforts have been made to offer scientists an alternative to the standard compartmentalized tools they use to explore their data and that involve a regular back and forth between them. We propose here an integrated pipeline especially designed for immersive environments, promoting direct interactions on semantically linked 2D and 3D heterogeneous data, displayed in a common working space. The creation of a semantic definition describing the content and the context of a molecular scene leads to the creation of an intelligent system where data are (1) combined through pre-existing or inferred links present in our hierarchical definition of the concepts, (2) enriched with suitable and adaptive analyses proposed to the user with respect to the current task and (3) interactively presented in a unique working environment to be explored.
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Affiliation(s)
- Mikael Trellet
- Faculty of Science, Chemistry, Computational Structural Biology group, Bijvoet Center for Biomolecular Research, Padualaan 9, 3584CH Utrecht, Netherlands
| | - Nicolas Férey
- VENISE Group, LIMSI, CNRS, Université Paris Sud, Orsay, France
| | | | - Marc Baaden
- Laboratoire de Biochimie Théorique, CNRS, UPR9080, Institut de Biologie Physico-Chimique, Univ Paris Diderot, Sorbonne Paris Cité, PSL Research University, Paris, France
| | - Patrick Bourdot
- VENISE Group, LIMSI, CNRS, Université Paris Sud, Orsay, France
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Pacifici M, Attorre F, Martellos S, Bego F, De Sanctis M, Hoda P, Meço M, Rondinini C, Saçdanaku E, Salihaj E, Scepi E, Shuka L, Ghiurghi A. BioNNA: the Biodiversity National Network of Albania. NATURE CONSERVATION 2018. [DOI: 10.3897/natureconservation.25.22387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Recently, the Albanian Government started the process to join the European Union. This process also involves matching the EU parameters in protecting its biodiversity. In order to support the Albanian authorities, the Italian Ministry of Foreign Affairs, General Directorate for Development Cooperation (DGCS) and the International Union for Conservation of Nature (IUCN) joined efforts in the project “Institutional Support to the Albanian Ministry of Environment, Forest and Water Administration for Sustainable Biodiversity Conservation and Use in Protected Areas”. This project aims at identifying priority needs in safeguarding ecosystem services and biodiversity conservation. Another project funded by the EU – “Strengthening capacity in National Nature Protection – preparation for Natura 2000 network” – started in 2015 with the aim to raise awareness for assisting local and national Albanian institutions to better exploit the potential of protected areas. One of the main issues encountered during these projects was the need for a national biodiversity data repository. The Biodiversity National Network of Albania (BioNNA) has been created to aggregate occurrence records of plants and animals and aims at becoming the most relevant source of information for biodiversity data as far as Albania is concerned. In this paper, the authors detail structure and data of BioNNA, including the process of data gathering and aggregation, taxonomic coverage, software details and WebGIS development. BioNNA is a milestone on the path towards Albania’s inclusion in the EU and has also a relevant potential social relevance for improving people’s awareness on the importance of biodiversity in the country.
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Kraus JM, Lausser L, Kuhn P, Jobst F, Bock M, Halanke C, Hummel M, Heuschmann P, Kestler HA. Big data and precision medicine: challenges and strategies with healthcare data. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2018. [DOI: 10.1007/s41060-018-0095-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Panneerselvam S, Choi S. Nanoinformatics: emerging databases and available tools. Int J Mol Sci 2014; 15:7158-82. [PMID: 24776761 PMCID: PMC4057665 DOI: 10.3390/ijms15057158] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2014] [Revised: 03/28/2014] [Accepted: 04/09/2014] [Indexed: 01/20/2023] Open
Abstract
Nanotechnology has arisen as a key player in the field of nanomedicine. Although the use of engineered nanoparticles is rapidly increasing, safety assessment is also important for the beneficial use of new nanomaterials. Considering that the experimental assessment of new nanomaterials is costly and laborious, in silico approaches hold promise. Several major challenges in nanotechnology indicate a need for nanoinformatics. New database initiatives such as ISA-TAB-Nano, caNanoLab, and Nanomaterial Registry will help in data sharing and developing data standards, and, as the amount of nanomaterials data grows, will provide a way to develop methods and tools specific to the nanolevel. In this review, we describe emerging databases and tools that should aid in the progress of nanotechnology research.
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Affiliation(s)
- Suresh Panneerselvam
- Department of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea.
| | - Sangdun Choi
- Department of Molecular Science and Technology, Ajou University, Suwon 443-749, Korea.
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Tudose I, Hastings J, Muthukrishnan V, Owen G, Turner S, Dekker A, Kale N, Ennis M, Steinbeck C. OntoQuery: easy-to-use web-based OWL querying. Bioinformatics 2013; 29:2955-7. [PMID: 24008420 PMCID: PMC3810857 DOI: 10.1093/bioinformatics/btt514] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Summary: The Web Ontology Language (OWL) provides a sophisticated language for building complex domain ontologies and is widely used in bio-ontologies such as the Gene Ontology. The Protégé-OWL ontology editing tool provides a query facility that allows composition and execution of queries with the human-readable Manchester OWL syntax, with syntax checking and entity label lookup. No equivalent query facility such as the Protégé Description Logics (DL) query yet exists in web form. However, many users interact with bio-ontologies such as chemical entities of biological interest and the Gene Ontology using their online Web sites, within which DL-based querying functionality is not available. To address this gap, we introduce the OntoQuery web-based query utility. Availability and implementation: The source code for this implementation together with instructions for installation is available at http://github.com/IlincaTudose/OntoQuery. OntoQuery software is fully compatible with all OWL-based ontologies and is available for download (CC-0 license). The ChEBI installation, ChEBI OntoQuery, is available at http://www.ebi.ac.uk/chebi/tools/ontoquery. Contact: hastings@ebi.ac.uk
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Affiliation(s)
- Ilinca Tudose
- Cheminformatics and Metabolism, European Bioinformatics Institute, Cambridge, UK, Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland and Evolutionary Bioinformatics, Swiss Institute of Bioinformatics, Lausanne, Romandy, Switzerland
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Elsayed I, Ludescher T, King J, Ager C, Trosin M, Senocak U, Brezany P, Feilhauer T, Amann A. ABA-Cloud: support for collaborative breath research. J Breath Res 2013; 7:026007. [PMID: 23619467 PMCID: PMC4913868 DOI: 10.1088/1752-7155/7/2/026007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This paper introduces the advanced breath analysis (ABA) platform, an innovative scientific research platform for the entire breath research domain. Within the ABA project, we are investigating novel data management concepts and semantic web technologies to document breath analysis studies for the long run as well as to enable their full automatic reproducibility. We propose several concept taxonomies (a hierarchical order of terms from a glossary of terms), which can be seen as a first step toward the definition of conceptualized terms commonly used by the international community of breath researchers. They build the basis for the development of an ontology (a concept from computer science used for communication between machines and/or humans and representation and reuse of knowledge) dedicated to breath research.
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Affiliation(s)
- Ibrahim Elsayed
- Research Group Scientific Computing, University of Vienna, Währinger Strasse 29, A-1090 Vienna, Austria
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AllerML: markup language for allergens. Regul Toxicol Pharmacol 2011; 60:151-60. [PMID: 21420460 DOI: 10.1016/j.yrtph.2011.03.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2011] [Revised: 03/14/2011] [Accepted: 03/16/2011] [Indexed: 02/01/2023]
Abstract
Many concerns have been raised about the potential allergenicity of novel, recombinant proteins into food crops. Guidelines, proposed by WHO/FAO and EFSA, include the use of bioinformatics screening to assess the risk of potential allergenicity or cross-reactivities of all proteins introduced, for example, to improve nutritional value or promote crop resistance. However, there are no universally accepted standards that can be used to encode data on the biology of allergens to facilitate using data from multiple databases in this screening. Therefore, we developed AllerML a markup language for allergens to assist in the automated exchange of information between databases and in the integration of the bioinformatics tools that are used to investigate allergenicity and cross-reactivity. As proof of concept, AllerML was implemented using the Structural Database of Allergenic Proteins (SDAP; http://fermi.utmb.edu/SDAP/) database. General implementation of AllerML will promote automatic flow of validated data that will aid in allergy research and regulatory analysis.
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