1
|
Mah N, Kurtz A, Fuhr A, Seltmann S, Chen Y, Bultjer N, Dewender J, Lual A, Steeg R, Mueller SC. The Management of Data for the Banking, Qualification, and Distribution of Induced Pluripotent Stem Cells: Lessons Learned from the European Bank for Induced Pluripotent Stem Cells. Cells 2023; 12:2756. [PMID: 38067184 PMCID: PMC10705942 DOI: 10.3390/cells12232756] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/17/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
The European Bank for induced pluripotent Stem Cells (EBiSC) was established in 2014 as a non-profit project for the banking, quality control, and distribution of human iPSC lines for research around the world. EBiSC iPSCs are deposited from diverse laboratories internationally and, hence, a key activity for EBiSC is standardising not only the iPSC lines themselves but also the data associated with them. This includes enabling unique nomenclature for the cells, as well as applying uniformity to the data provided by the cell line generator versus quality control data generated by EBiSC, and providing mechanisms to share personal data in a secure and GDPR-compliant manner. A joint approach implemented by EBiSC and the human pluripotent stem cell registry (hPSCreg®) has provided a solution that enabled hPSCreg® to improve its registration platform for iPSCs and EBiSC to have a pipeline for the import, standardisation, storage, and management of data associated with EBiSC iPSCs. In this work, we describe the experience of cell line data management for iPSC banking throughout the course of EBiSC's development as a central European banking infrastructure and present a model for how this could be implemented by other iPSC repositories to increase the FAIRness of iPSC research globally.
Collapse
Affiliation(s)
- Nancy Mah
- Fraunhofer-Institute für Biomedizinische Technik (IBMT), Joseph-von-Fraunhofer Weg 1, 66280 Sulzbach, Germany; (N.M.)
| | - Andreas Kurtz
- Fraunhofer-Institute für Biomedizinische Technik (IBMT), Joseph-von-Fraunhofer Weg 1, 66280 Sulzbach, Germany; (N.M.)
- Berlin Institute of Health Center for Regenerative Therapies, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Antonie Fuhr
- Fraunhofer-Institute für Biomedizinische Technik (IBMT), Joseph-von-Fraunhofer Weg 1, 66280 Sulzbach, Germany; (N.M.)
| | - Stefanie Seltmann
- Fraunhofer-Institute für Biomedizinische Technik (IBMT), Joseph-von-Fraunhofer Weg 1, 66280 Sulzbach, Germany; (N.M.)
| | - Ying Chen
- Fraunhofer-Institute für Biomedizinische Technik (IBMT), Joseph-von-Fraunhofer Weg 1, 66280 Sulzbach, Germany; (N.M.)
| | - Nils Bultjer
- Fraunhofer-Institute für Biomedizinische Technik (IBMT), Joseph-von-Fraunhofer Weg 1, 66280 Sulzbach, Germany; (N.M.)
| | - Johannes Dewender
- Fraunhofer-Institute für Biomedizinische Technik (IBMT), Joseph-von-Fraunhofer Weg 1, 66280 Sulzbach, Germany; (N.M.)
| | - Ayuen Lual
- European Collection of Authenticated Cell Cultures (ECACC), UK Health Security Agency, Porton Down, Salisbury SP4 0JG, UK;
| | - Rachel Steeg
- Fraunhofer UK Research Ltd., Technology and Innovation Centre, 99 George St., Glasgow G1 1RD, UK
| | - Sabine C. Mueller
- Fraunhofer-Institute für Biomedizinische Technik (IBMT), Joseph-von-Fraunhofer Weg 1, 66280 Sulzbach, Germany; (N.M.)
| |
Collapse
|
2
|
Chen Y, Sakurai K, Maeda S, Masui T, Okano H, Dewender J, Seltmann S, Kurtz A, Masuya H, Nakamura Y, Sheldon M, Schneider J, Stacey GN, Panina Y, Fujibuchi W. Integrated Collection of Stem Cell Bank Data, a Data Portal for Standardized Stem Cell Information. Stem Cell Reports 2021; 16:997-1005. [PMID: 33740463 PMCID: PMC8072026 DOI: 10.1016/j.stemcr.2021.02.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 02/16/2021] [Accepted: 02/18/2021] [Indexed: 12/30/2022] Open
Abstract
The past decade has witnessed an extremely rapid increase in the number of newly established stem cell lines. However, due to the lack of a standardized format, data exchange among stem cell line resources has been challenging, and no system can search all stem cell lines across resources worldwide. To solve this problem, we have developed the Integrated Collection of Stem Cell Bank data (ICSCB) (http://icscb.stemcellinformatics.org/), the largest database search portal for stem cell line information, based on the standardized data items and terms of the MIACARM framework. Currently, ICSCB can retrieve >16,000 cell lines from four major data resources in Europe, Japan, and the United States. ICSCB is automatically updated to provide the latest cell line information, and its integrative search helps users collect cell line information for over 1,000 diseases, including many rare diseases worldwide, which has been a formidable task, thereby distinguishing itself from other database search portals. Searches >16,000 stem cell lines in Europe, Japan, and US major databases Data formats standardized by minimum items in MIACARM guidelines Searches specific stem cell lines according to disease, donor, tissue, etc. User-friendly website accesses >6,000 diseased stem cell lines from 36 countries
Collapse
Affiliation(s)
- Ying Chen
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Kunie Sakurai
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Sumihiro Maeda
- Department of Physiology, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Tohru Masui
- National Center for Medical Genetics, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Johannes Dewender
- Fraunhofer Institute for Biomedical Engineering, Biomedical Data and Bioethics, Anna-Louisa-Karsch-Strasse 2, 10178 Berlin, Germany
| | - Stefanie Seltmann
- Fraunhofer Institute for Biomedical Engineering, Biomedical Data and Bioethics, Anna-Louisa-Karsch-Strasse 2, 10178 Berlin, Germany
| | - Andreas Kurtz
- Fraunhofer Institute for Biomedical Engineering, Biomedical Data and Bioethics, Anna-Louisa-Karsch-Strasse 2, 10178 Berlin, Germany; BIH Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Hiroshi Masuya
- Integrated Bioresource Information Division, RIKEN BioResource Research Center, Tsukuba, Ibaraki 305-0074, Japan
| | - Yukio Nakamura
- Cell Engineering Division, RIKEN BioResource Research Center, Tsukuba, Ibaraki 305-0074, Japan
| | - Michael Sheldon
- Department of Genetics and Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Juliane Schneider
- Harvard Catalyst
- The Harvard Clinical and Translational Science Center, Boston, MA 02215, USA
| | - Glyn N Stacey
- International Stem Cell Banking Initiative, 2 High Street, Barley, Hertfordshire SG88HZ, UK; National Stem Cell Resource Center, Institute of Zoology, Chinese Academy of Sciences, Beijing 100190, China; Innovation Academy for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Yulia Panina
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto 606-8507, Japan
| | - Wataru Fujibuchi
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto 606-8507, Japan.
| |
Collapse
|
3
|
Open-Source Software Tools, Databases, and Resources for Single-Cell and Single-Cell-Type Metabolomics. Methods Mol Biol 2020; 2064:191-217. [PMID: 31565776 DOI: 10.1007/978-1-4939-9831-9_15] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
In this age of -omics data-guided big data revolution, metabolomics has received significant attention as compared to genomics, transcriptomics, and proteomics for its proximity to the phenotype, the promises it makes and the challenges it throws. Although metabolomes of entire organisms, organs, biofluids, and tissues are of immense interest, a cell-specific resolution is deemed critical for biomedical applications where a granular understanding of cellular metabolism at cell-type and subcellular resolution is desirable. Mass spectrometry (MS) is a versatile technique that is used to analyze a broad range of compounds from different species and cell-types, with high accuracy, resolution, sensitivity, selectivity, and fast data acquisition speeds. With recent advances in MS and spectroscopy-based platforms, the research community is able to generate high-throughput data sets from single cells. However, it is challenging to handle, store, process, analyze, and interpret data in a routine manner. In this treatise, I present a workflow of metabolomics data generation from single cells and single-cell types to their analysis, visualization, and interpretation for obtaining biological insights.
Collapse
|
4
|
Lee JE, Sung JH, Sarpong D, Efird JT, Tchounwou PB, Ofili E, Norris K. Knowledge Management for Fostering Biostatistical Collaboration within a Research Network: The RTRN Case Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:E2533. [PMID: 30424550 PMCID: PMC6266008 DOI: 10.3390/ijerph15112533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 10/04/2018] [Accepted: 11/05/2018] [Indexed: 11/25/2022]
Abstract
Purpose: While the intellectual and scientific rationale for research collaboration has been articulated, a paucity of information is available on a strategic approach to facilitate the collaboration within a research network designed to reduce health disparities. This study aimed to (1) develop a conceptual model to facilitate collaboration among biostatisticians in a research network; (2) describe collaborative engagement performed by the Network's Data Coordinating Center (DCC); and (3) discuss potential challenges and opportunities in engaging the collaboration. Methods: Key components of the strategic approach will be developed through a systematic literature review. The Network's initiatives for the biostatistical collaboration will be described in the areas of infrastructure, expertise and knowledge management and experiential lessons will be discussed. Results: Components of the strategic approach model included three Ps (people, processes and programs) which were integrated into expert management, infrastructure management and knowledge management, respectively. Ongoing initiatives for collaboration with non-DCC biostatisticians included both web-based and face-to-face interaction approaches: Network's biostatistical capacities and needs assessment, webinar statistical seminars, mobile statistical workshop and clinics, adjunct appointment program, one-on-one consulting, and on-site workshop. The outreach program, as a face-to-face interaction approach, especially resulted in a useful tool for expertise management and needs assessment as well as knowledge exchange. Conclusions: Although fostering a partnered research culture, sustaining senior management commitment and ongoing monitoring are a challenge for this collaborative engagement, the proposed strategies centrally performed by the DCC may be useful in accelerating the pace and enhancing the quality of the scientific outcomes within a multidisciplinary clinical and translational research network.
Collapse
Affiliation(s)
- Jae Eun Lee
- Research Centers in Minority Institutions Translational Research Network Data Coordinating Center, Mississippi e-Center, Jackson State University, 1230 Raymond Rd., Jackson, MS 39204, USA.
- Department of Biostatistics and Epidemiology, College of Public Services, Jackson State University, 350 W. Woodrow Wilson Drive Jackson Medical Mall, Suite 301, Jackson, MS 39213, USA.
| | - Jung Hye Sung
- Department of Biostatistics and Epidemiology, College of Public Services, Jackson State University, 350 W. Woodrow Wilson Drive Jackson Medical Mall, Suite 301, Jackson, MS 39213, USA.
| | - Daniel Sarpong
- Center for Minority Health and Health Disparities Research and Education, Xavier University, 1 Drexel Drive, New Orleans, LA 70125, USA.
| | - Jimmy T Efird
- Center for Clinical Epidemiology and Biostatistics (CCEB), School of Medicine and Public Health, the University of Newcastle (UoN), Callaghan, NSW 2308, Australia.
| | - Paul B Tchounwou
- Research Centers in Minority Institutions Translational Research Network Data Coordinating Center, Mississippi e-Center, Jackson State University, 1230 Raymond Rd., Jackson, MS 39204, USA.
| | - Elizabeth Ofili
- Clinical Research Center & Clinical and Translational Research, Morehouse School of Medicine, 720 Westview Drive, Atlanta, GA 30310, USA.
| | - Keith Norris
- Department of Medicine, David Geffen School of Medicine, UCLA, 911 Broxton Ave, Room 103, Los Angeles, CA 90024, USA.
| |
Collapse
|
5
|
Abstract
The Cellosaurus is a knowledge resource on cell lines. It aims to describe all cell lines used in biomedical research. Its scope encompasses both vertebrates and invertebrates. Currently, information for >100,000 cell lines is provided. For each cell line, it provides a wealth of information, cross-references, and literature citations. The Cellosaurus is available on the ExPASy server (https://web.expasy.org/cellosaurus/) and can be downloaded in a variety of formats. Among its many uses, the Cellosaurus is a key resource to help researchers identify potentially contaminated/misidentified cell lines, thus contributing to improving the quality of research in the life sciences.
Collapse
Affiliation(s)
- Amos Bairoch
- Computer and Laboratory Investigation of Proteins of Human Origin Group, Faculty of Medicine, Swiss Institute of Bioinformatics, University of Geneva, Geneva 4, Switzerland
| |
Collapse
|
6
|
McMurry JA, Juty N, Blomberg N, Burdett T, Conlin T, Conte N, Courtot M, Deck J, Dumontier M, Fellows DK, Gonzalez-Beltran A, Gormanns P, Grethe J, Hastings J, Hériché JK, Hermjakob H, Ison JC, Jimenez RC, Jupp S, Kunze J, Laibe C, Le Novère N, Malone J, Martin MJ, McEntyre JR, Morris C, Muilu J, Müller W, Rocca-Serra P, Sansone SA, Sariyar M, Snoep JL, Soiland-Reyes S, Stanford NJ, Swainston N, Washington N, Williams AR, Wimalaratne SM, Winfree LM, Wolstencroft K, Goble C, Mungall CJ, Haendel MA, Parkinson H. Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data. PLoS Biol 2017; 15:e2001414. [PMID: 28662064 PMCID: PMC5490878 DOI: 10.1371/journal.pbio.2001414] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.
Collapse
Affiliation(s)
- Julie A. McMurry
- Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Nick Juty
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Niklas Blomberg
- ELIXIR Hub, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Tony Burdett
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Tom Conlin
- Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Nathalie Conte
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Mélanie Courtot
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - John Deck
- Berkeley Natural History Museums, University of California at Berkeley, Berkely, California, United States of America
| | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, the Netherlands
| | - Donal K. Fellows
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | | | - Philipp Gormanns
- Institute of Experimental Genetics, Helmholtz Centre Munich, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jeffrey Grethe
- Center for Research in Biological Systems, University of California San Diego, La Jolla, California, United States of America
| | | | | | - Henning Hermjakob
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Jon C. Ison
- Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
| | - Rafael C. Jimenez
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Simon Jupp
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - John Kunze
- California Digital Library, Oakland, California, United States of America
| | - Camille Laibe
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | | | - James Malone
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Maria Jesus Martin
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Johanna R. McEntyre
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Chris Morris
- Science and Technology Facilities Council, Daresbury Laboratory, Warrington, United Kingdom
| | - Juha Muilu
- Genomics Coordination Center, Department of Genetics, University Medical Center Groningen and Groningen Bioinformatics Center, University of Groningen, Groningen, the Netherlands
| | - Wolfgang Müller
- Scientific Databases and Visualization at Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | | | | | - Murat Sariyar
- Institute for Medical Informatics, Bern University of Applied Sciences, Engineering and Information Technology, Bern, Switzerland
| | - Jacky L. Snoep
- Manchester Institute of Biology, University of Manchester, Manchester, United Kingdom
- Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa
| | - Stian Soiland-Reyes
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Natalie J. Stanford
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals, University of Manchester, Manchester, United Kingdom
| | - Nicole Washington
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Alan R. Williams
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Sarala M. Wimalaratne
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Lilly M. Winfree
- Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, the Netherlands
| | - Carole Goble
- School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Melissa A. Haendel
- Department of Medical Informatics and Epidemiology and OHSU Library, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Helen Parkinson
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| |
Collapse
|
7
|
Brandizi M, Melnichuk O, Bild R, Kohlmayer F, Rodriguez-Castro B, Spengler H, Kuhn KA, Kuchinke W, Ohmann C, Mustonen T, Linden M, Nyrönen T, Lappalainen I, Brazma A, Sarkans U. Orchestrating differential data access for translational research: a pilot implementation. BMC Med Inform Decis Mak 2017; 17:30. [PMID: 28330491 PMCID: PMC5363029 DOI: 10.1186/s12911-017-0424-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 03/03/2017] [Indexed: 01/30/2023] Open
Abstract
Background Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data. Methods In this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements. Results Open source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility. Conclusions Common open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.
Collapse
Affiliation(s)
- Marco Brandizi
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK.
| | - Olga Melnichuk
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Raffael Bild
- Chair of Medical Informatics, Institute of Medical Statistics and Epidemiology, University Medical Center rechts der Isar, Technical University of Munich, Munich, Germany
| | - Florian Kohlmayer
- Chair of Medical Informatics, Institute of Medical Statistics and Epidemiology, University Medical Center rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benedicto Rodriguez-Castro
- Chair of Medical Informatics, Institute of Medical Statistics and Epidemiology, University Medical Center rechts der Isar, Technical University of Munich, Munich, Germany
| | - Helmut Spengler
- Chair of Medical Informatics, Institute of Medical Statistics and Epidemiology, University Medical Center rechts der Isar, Technical University of Munich, Munich, Germany
| | - Klaus A Kuhn
- Chair of Medical Informatics, Institute of Medical Statistics and Epidemiology, University Medical Center rechts der Isar, Technical University of Munich, Munich, Germany
| | - Wolfgang Kuchinke
- Heinrich-Heine Universität Düsseldorf, Coordination Centre for Clinical Trials, Düsseldorf, Germany
| | - Christian Ohmann
- European Clinical Research Infrastructure Network (ECRIN), Düsseldorf, Germany
| | | | | | | | | | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK.
| |
Collapse
|
8
|
Utecht J, Judkins J, Otte JN, Colvin T, Rogers N, Rose R, Alvi M, Hicks A, Ball J, Bowman SM, Maxson RT, Nabaweesi R, Pradhan R, Sanddal ND, Tudoreanu ME, Winchell RJ, Brochhausen M. OOSTT: a Resource for Analyzing the Organizational Structures of Trauma Centers and Trauma Systems. CEUR WORKSHOP PROCEEDINGS 2016; 1747:http://ceur-ws.org/Vol-1747/IT504_ICBO2016.pdf. [PMID: 28217041 PMCID: PMC5312685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Organizational structures of healthcare organizations has increasingly become a focus of medical research. In the CAFÉ project we aim to provide a web-service enabling ontology-driven comparison of the organizational characteristics of trauma centers and trauma systems. Trauma remains one of the biggest challenges to healthcare systems worldwide. Research has demonstrated that coordinated efforts like trauma systems and trauma centers are key components of addressing this challenge. Evaluation and comparison of these organizations is essential. However, this research challenge is frequently compounded by the lack of a shared terminology and the lack of effective information technology solutions for assessing and comparing these organizations. In this paper we present the Ontology of Organizational Structures of Trauma systems and Trauma centers (OOSTT) that provides the ontological foundation to CAFÉ's web-based questionnaire infrastructure. We present the usage of the ontology in relation to the questionnaire and provide the methods that were used to create the ontology.
Collapse
Affiliation(s)
| | - John Judkins
- University of Arkansas for Medical Science, USA
- University of Arkansas Little Rock, USA
| | | | - Terra Colvin
- Wake Forest University Comprehensive Cancer Center
| | | | - Robert Rose
- University of Arkansas for Medical Science, USA
| | - Maria Alvi
- American College of Surgeons Committee on Trauma, USA
| | | | - Jane Ball
- American College of Surgeons Committee on Trauma, USA
| | | | | | - Rosemary Nabaweesi
- University of Arkansas for Medical Science, USA
- Arkansas Children's Hospital Research Institute
| | | | | | | | - Robert J. Winchell
- American College of Surgeons Committee on Trauma, USA
- Weill Cornell Medical College, USA
| | | |
Collapse
|
9
|
Catena R, Özcan A, Jacobs A, Chevrier S, Bodenmiller B. AirLab: a cloud-based platform to manage and share antibody-based single-cell research. Genome Biol 2016; 17:142. [PMID: 27356760 PMCID: PMC4928244 DOI: 10.1186/s13059-016-1006-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 06/10/2016] [Indexed: 11/10/2022] Open
Abstract
Single-cell analysis technologies are essential tools in research and clinical diagnostics. These methods include flow cytometry, mass cytometry, and other microfluidics-based technologies. Most laboratories that employ these methods maintain large repositories of antibodies. These ever-growing collections of antibodies, their multiple conjugates, and the large amounts of data generated in assays using specific antibodies and conditions makes a dedicated software solution necessary. We have developed AirLab, a cloud-based tool with web and mobile interfaces, for the organization of these data. AirLab streamlines the processes of antibody purchase, organization, and storage, antibody panel creation, results logging, and antibody validation data sharing and distribution. Furthermore, AirLab enables inventory of other laboratory stocks, such as primers or clinical samples, through user-controlled customization. Thus, AirLab is a mobile-powered and flexible tool that harnesses the capabilities of mobile tools and cloud-based technology to facilitate inventory and sharing of antibody and sample collections and associated validation data.
Collapse
Affiliation(s)
- Raúl Catena
- Institute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, Building/Room: Y11-J-82, CH-8057, Zurich, Switzerland
| | - Alaz Özcan
- Institute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, Building/Room: Y11-J-82, CH-8057, Zurich, Switzerland.,Department of Biology, Biology Master's Program, ETH, Zürich, Switzerland
| | - Andrea Jacobs
- Institute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, Building/Room: Y11-J-82, CH-8057, Zurich, Switzerland
| | - Stephane Chevrier
- Institute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, Building/Room: Y11-J-82, CH-8057, Zurich, Switzerland
| | - Bernd Bodenmiller
- Institute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, Building/Room: Y11-J-82, CH-8057, Zurich, Switzerland.
| |
Collapse
|
10
|
Yaffe MP, Noggle SA, Solomon SL. Raising the standards of stem cell line quality. Nat Cell Biol 2016; 18:236-7. [DOI: 10.1038/ncb3313] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
|
11
|
Seltmann S, Lekschas F, Müller R, Stachelscheid H, Bittner MS, Zhang W, Kidane L, Seriola A, Veiga A, Stacey G, Kurtz A. hPSCreg--the human pluripotent stem cell registry. Nucleic Acids Res 2015; 44:D757-63. [PMID: 26400179 PMCID: PMC4702942 DOI: 10.1093/nar/gkv963] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 09/11/2015] [Indexed: 12/22/2022] Open
Abstract
The human pluripotent stem cell registry (hPSCreg), accessible at http://hpscreg.eu, is a public registry and data portal for human embryonic and induced pluripotent stem cell lines (hESC and hiPSC). Since their first isolation the number of hESC lines has steadily increased to over 3000 and new iPSC lines are generated in a rapidly growing number of laboratories as a result of their potentially broad applicability in biomedicine and drug testing. Many of these lines are deposited in stem cell banks, which are globally established to store tens of thousands of lines from healthy and diseased donors. The Registry provides comprehensive and standardized biological and legal information as well as tools to search and compare information from multiple hPSC sources and hence addresses a translational research need. To facilitate unambiguous identification over different resources, hPSCreg automatically creates a unique standardized name for each cell line registered. In addition to biological information, hPSCreg stores extensive data about ethical standards regarding cell sourcing and conditions for application and privacy protection. hPSCreg is the first global registry that holds both, manually validated scientific and ethical information on hPSC lines, and provides access by means of a user-friendly, mobile-ready web application.
Collapse
Affiliation(s)
- Stefanie Seltmann
- Berlin-Brandenburg Center for Regenerative Therapies, Charité University Medicine Berlin, Berlin, 13353, Germany
| | - Fritz Lekschas
- Berlin-Brandenburg Center for Regenerative Therapies, Charité University Medicine Berlin, Berlin, 13353, Germany
| | - Robert Müller
- Berlin-Brandenburg Center for Regenerative Therapies, Charité University Medicine Berlin, Berlin, 13353, Germany
| | - Harald Stachelscheid
- Berlin-Brandenburg Center for Regenerative Therapies, Charité University Medicine Berlin, Berlin, 13353, Germany Berlin Institute of Health-Stem Cell Core Facility, 13353 Berlin, Germany
| | - Marie-Sophie Bittner
- Berlin-Brandenburg Center for Regenerative Therapies, Charité University Medicine Berlin, Berlin, 13353, Germany
| | - Weiping Zhang
- Berlin-Brandenburg Center for Regenerative Therapies, Charité University Medicine Berlin, Berlin, 13353, Germany
| | - Luam Kidane
- National Institute for Biological Standards and Control, South Mimms EN63QG, UK
| | - Anna Seriola
- Center of Regenerative Medicine in Barcelona, Barcelona Stem Cell Bank, Barcelona 08003, Spain
| | - Anna Veiga
- Center of Regenerative Medicine in Barcelona, Barcelona Stem Cell Bank, Barcelona 08003, Spain
| | - Glyn Stacey
- National Institute for Biological Standards and Control, South Mimms EN63QG, UK
| | - Andreas Kurtz
- Berlin-Brandenburg Center for Regenerative Therapies, Charité University Medicine Berlin, Berlin, 13353, Germany Seoul National University, College of Veterinary Medicine and Research Institute for Veterinary Science, Seoul 151-742, Republic of Korea
| |
Collapse
|
12
|
Ochs C, Perl Y, Geller J, Haendel M, Brush M, Arabandi S, Tu S. Summarizing and visualizing structural changes during the evolution of biomedical ontologies using a Diff Abstraction Network. J Biomed Inform 2015; 56:127-44. [PMID: 26048076 DOI: 10.1016/j.jbi.2015.05.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Revised: 04/01/2015] [Accepted: 05/27/2015] [Indexed: 10/23/2022]
Abstract
Biomedical ontologies are a critical component in biomedical research and practice. As an ontology evolves, its structure and content change in response to additions, deletions and updates. When editing a biomedical ontology, small local updates may affect large portions of the ontology, leading to unintended and potentially erroneous changes. Such unwanted side effects often go unnoticed since biomedical ontologies are large and complex knowledge structures. Abstraction networks, which provide compact summaries of an ontology's content and structure, have been used to uncover structural irregularities, inconsistencies and errors in ontologies. In this paper, we introduce Diff Abstraction Networks ("Diff AbNs"), compact networks that summarize and visualize global structural changes due to ontology editing operations that result in a new ontology release. A Diff AbN can be used to support curators in identifying unintended and unwanted ontology changes. The derivation of two Diff AbNs, the Diff Area Taxonomy and the Diff Partial-area Taxonomy, is explained and Diff Partial-area Taxonomies are derived and analyzed for the Ontology of Clinical Research, Sleep Domain Ontology, and eagle-i Research Resource Ontology. Diff Taxonomy usage for identifying unintended erroneous consequences of quality assurance and ontology merging are demonstrated.
Collapse
Affiliation(s)
- Christopher Ochs
- Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
| | - Yehoshua Perl
- Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - James Geller
- Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Melissa Haendel
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Matthew Brush
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Samson Tu
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, USA
| |
Collapse
|
13
|
State-of-the-Art and Future Challenges in the Integration of Biobank Catalogues. SMART HEALTH 2015. [DOI: 10.1007/978-3-319-16226-3_11] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
14
|
Borromeo CD, Schleyer TK, Becich MJ, Hochheiser H. Finding collaborators: toward interactive discovery tools for research network systems. J Med Internet Res 2014; 16:e244. [PMID: 25370463 PMCID: PMC4376239 DOI: 10.2196/jmir.3444] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Revised: 08/13/2014] [Accepted: 08/30/2014] [Indexed: 11/25/2022] Open
Abstract
Background Research networking systems hold great promise for helping biomedical scientists identify collaborators with the expertise needed to build interdisciplinary teams. Although efforts to date have focused primarily on collecting and aggregating information, less attention has been paid to the design of end-user tools for using these collections to identify collaborators. To be effective, collaborator search tools must provide researchers with easy access to information relevant to their collaboration needs. Objective The aim was to study user requirements and preferences for research networking system collaborator search tools and to design and evaluate a functional prototype. Methods Paper prototypes exploring possible interface designs were presented to 18 participants in semistructured interviews aimed at eliciting collaborator search needs. Interview data were coded and analyzed to identify recurrent themes and related software requirements. Analysis results and elements from paper prototypes were used to design a Web-based prototype using the D3 JavaScript library and VIVO data. Preliminary usability studies asked 20 participants to use the tool and to provide feedback through semistructured interviews and completion of the System Usability Scale (SUS). Results Initial interviews identified consensus regarding several novel requirements for collaborator search tools, including chronological display of publication and research funding information, the need for conjunctive keyword searches, and tools for tracking candidate collaborators. Participant responses were positive (SUS score: mean 76.4%, SD 13.9). Opportunities for improving the interface design were identified. Conclusions Interactive, timeline-based displays that support comparison of researcher productivity in funding and publication have the potential to effectively support searching for collaborators. Further refinement and longitudinal studies may be needed to better understand the implications of collaborator search tools for researcher workflows.
Collapse
Affiliation(s)
- Charles D Borromeo
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
| | | | | | | |
Collapse
|
15
|
Obeid JS, Johnson LM, Stallings S, Eichmann D. Research Networking Systems: The State of Adoption at Institutions Aiming to Augment Translational Research Infrastructure. JOURNAL OF TRANSLATIONAL MEDICINE & EPIDEMIOLOGY 2014; 2:1026. [PMID: 26491707 PMCID: PMC4610407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Fostering collaborations across multiple disciplines within and across institutional boundaries is becoming increasingly important with the growing emphasis on translational research. As a result, Research Networking Systems that facilitate discovery of potential collaborators have received significant attention by institutions aiming to augment their research infrastructure. We have conducted a survey to assess the state of adoption of these new tools at the Clinical and Translational Science Award (CTSA) funded institutions. Survey results demonstrate that most CTSA funded institutions have either already adopted or were planning to adopt one of several available research networking systems. Moreover a good number of these institutions have exposed or plan to expose the data on research expertise using linked open data, an established approach to semantic web services. Preliminary exploration of these publically-available data shows promising utility in assessing cross-institutional collaborations. Further adoption of these technologies and analysis of the data are needed, however, before their impact on cross-institutional collaboration in research can be appreciated and measured.
Collapse
Affiliation(s)
- Jihad S Obeid
- South Carolina Clinical and Translational Research Institute, Medical University of South Carolina, USA
| | - Layne M Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, USA
| | - Sarah Stallings
- Vanderbilt Institution for Clinical and Translational Research, Vanderbilt University Medical Center, USA
| | - David Eichmann
- School of Library and Information Science, University of Iowa, USA
| |
Collapse
|
16
|
Malone J, Brown A, Lister AL, Ison J, Hull D, Parkinson H, Stevens R. The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation. J Biomed Semantics 2014; 5:25. [PMID: 25068035 PMCID: PMC4098953 DOI: 10.1186/2041-1480-5-25] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 04/19/2014] [Indexed: 01/07/2023] Open
Abstract
Motivation Biomedical ontologists to date have concentrated on ontological descriptions of biomedical entities such as gene products and their attributes, phenotypes and so on. Recently, effort has diversified to descriptions of the laboratory investigations by which these entities were produced. However, much biological insight is gained from the analysis of the data produced from these investigations, and there is a lack of adequate descriptions of the wide range of software that are central to bioinformatics. We need to describe how data are analyzed for discovery, audit trails, provenance and reproducibility. Results The Software Ontology (SWO) is a description of software used to store, manage and analyze data. Input to the SWO has come from beyond the life sciences, but its main focus is the life sciences. We used agile techniques to gather input for the SWO and keep engagement with our users. The result is an ontology that meets the needs of a broad range of users by describing software, its information processing tasks, data inputs and outputs, data formats versions and so on. Recently, the SWO has incorporated EDAM, a vocabulary for describing data and related concepts in bioinformatics. The SWO is currently being used to describe software used in multiple biomedical applications. Conclusion The SWO is another element of the biomedical ontology landscape that is necessary for the description of biomedical entities and how they were discovered. An ontology of software used to analyze data produced by investigations in the life sciences can be made in such a way that it covers the important features requested and prioritized by its users. The SWO thus fits into the landscape of biomedical ontologies and is produced using techniques designed to keep it in line with user’s needs. Availability The Software Ontology is available under an Apache 2.0 license at http://theswo.sourceforge.net/; the Software Ontology blog can be read at http://softwareontology.wordpress.com.
Collapse
Affiliation(s)
- James Malone
- EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK
| | - Andy Brown
- School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Allyson L Lister
- School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Jon Ison
- EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK
| | - Duncan Hull
- School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Helen Parkinson
- EMBL-EBI, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK
| | - Robert Stevens
- School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| |
Collapse
|
17
|
Walls RL, Deck J, Guralnick R, Baskauf S, Beaman R, Blum S, Bowers S, Buttigieg PL, Davies N, Endresen D, Gandolfo MA, Hanner R, Janning A, Krishtalka L, Matsunaga A, Midford P, Morrison N, Tuama ÉÓ, Schildhauer M, Smith B, Stucky BJ, Thomer A, Wieczorek J, Whitacre J, Wooley J. Semantics in support of biodiversity knowledge discovery: an introduction to the biological collections ontology and related ontologies. PLoS One 2014; 9:e89606. [PMID: 24595056 PMCID: PMC3940615 DOI: 10.1371/journal.pone.0089606] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Accepted: 01/24/2014] [Indexed: 11/19/2022] Open
Abstract
The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.
Collapse
Affiliation(s)
- Ramona L. Walls
- The iPlant Collaborative, University of Arizona, Tucson, Arizona, United States of America
- * E-mail:
| | - John Deck
- University of California, Berkeley, Berkeley, California, United States of America
| | - Robert Guralnick
- Department of Ecology and Evolutionary Biology and the CU Museum of Natural History, University of Colorado at Boulder, Boulder, Colorado, United States of America
| | - Steve Baskauf
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Reed Beaman
- University of Florida, Florida Museum of Natural History, Gainesville, Florida, United States of America
| | - Stanley Blum
- Research Informatics, California Academy of Sciences, San Francisco, California, United States of America
| | - Shawn Bowers
- Gonzaga University, Computer Science, Spokane, Washington, United States of America
| | - Pier Luigi Buttigieg
- Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
| | - Neil Davies
- University of California, Berkeley, Gump South Pacific Research Station, Moorea, French Polynesia
| | - Dag Endresen
- GBIF Norway, Natural History Museum, University in Oslo, Oslo, Norway
| | - Maria Alejandra Gandolfo
- LH Bailey Hortorium, Department of Plant Biology, Cornell University, Ithaca, New York, United States of America
| | - Robert Hanner
- Biodiversity Institute of Ontario, University of Guelph, Guelph, ON, Canada
| | - Alyssa Janning
- School of Information Resources and Library Science, University of Arizona, Tucson, Arizona, United States of America
| | - Leonard Krishtalka
- Biodiversity Institute and Ecology & Evolutionary Biology, The University of Kansas, Lawrence, Kansas, United States of America
| | - Andréa Matsunaga
- University of Florida, Gainesville, Florida, United States of America
| | - Peter Midford
- Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America
| | - Norman Morrison
- The BioVeL Project, School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | | | - Mark Schildhauer
- National Center for Ecological Analysis and Synthesis, Santa Barbara, California, United States of America
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, New York, United States of America
| | - Brian J. Stucky
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, United States of America
| | - Andrea Thomer
- Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America
| | - John Wieczorek
- 3101 VLSB, Museum of Vertebrate Zoology, University of California, Berkeley, Berkeley, California, United States of America
| | - Jamie Whitacre
- Informatics Branch, Information Technology Office, National Museum of Natural History, Smithsonian Institution, Washington, DC, United States of America
| | - John Wooley
- University of California San Diego, La Jolla, California, United States of America
| |
Collapse
|
18
|
Shirey-Rice J, Mapes B, Basford M, Zufelt A, Wehbe F, Harris P, Alcorn M, Allen D, Arnim M, Autry S, Briggs MS, Carnegie A, Chavis-Keeling D, De La Pena C, Dworschak D, Earnest J, Grieb T, Guess M, Hafer N, Johnson T, Kasper A, Kopp J, Lockie T, Lombardo V, McHale L, Minogue A, Nunnally B, O'Quinn D, Peck K, Pemberton K, Perry C, Petrie G, Pontello A, Posner R, Rehman B, Roth D, Sacksteder P, Scahill S, Schieri L, Simpson R, Skinner A, Toussant K, Turner A, Van der Put E, Wasser J, Webb CD, Williams M, Wiseman L, Yasko L, Pulley J. The CTSA Consortium's Catalog of Assets for Translational and Clinical Health Research (CATCHR). Clin Transl Sci 2014; 7:100-7. [PMID: 24456567 DOI: 10.1111/cts.12144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
The 61 CTSA Consortium sites are home to valuable programs and infrastructure supporting translational science and all are charged with ensuring that such investments translate quickly to improved clinical care. Catalog of Assets for Translational and Clinical Health Research (CATCHR) is the Consortium's effort to collect and make available information on programs and resources to maximize efficiency and facilitate collaborations. By capturing information on a broad range of assets supporting the entire clinical and translational research spectrum, CATCHR aims to provide the necessary infrastructure and processes to establish and maintain an open-access, searchable database of consortium resources to support multisite clinical and translational research studies. Data are collected using rigorous, defined methods, with the resulting information made visible through an integrated, searchable Web-based tool. Additional easy-to-use Web tools assist resource owners in validating and updating resource information over time. In this paper, we discuss the design and scope of the project, data collection methods, current results, and future plans for development and sustainability. With increasing pressure on research programs to avoid redundancy, CATCHR aims to make available information on programs and core facilities to maximize efficient use of resources.
Collapse
|
19
|
Vasilevsky NA, Brush MH, Paddock H, Ponting L, Tripathy SJ, Larocca GM, Haendel MA. On the reproducibility of science: unique identification of research resources in the biomedical literature. PeerJ 2013; 1:e148. [PMID: 24032093 PMCID: PMC3771067 DOI: 10.7717/peerj.148] [Citation(s) in RCA: 148] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2013] [Accepted: 08/12/2013] [Indexed: 12/24/2022] Open
Abstract
Scientific reproducibility has been at the forefront of many news stories and there exist numerous initiatives to help address this problem. We posit that a contributor is simply a lack of specificity that is required to enable adequate research reproducibility. In particular, the inability to uniquely identify research resources, such as antibodies and model organisms, makes it difficult or impossible to reproduce experiments even where the science is otherwise sound. In order to better understand the magnitude of this problem, we designed an experiment to ascertain the “identifiability” of research resources in the biomedical literature. We evaluated recent journal articles in the fields of Neuroscience, Developmental Biology, Immunology, Cell and Molecular Biology and General Biology, selected randomly based on a diversity of impact factors for the journals, publishers, and experimental method reporting guidelines. We attempted to uniquely identify model organisms (mouse, rat, zebrafish, worm, fly and yeast), antibodies, knockdown reagents (morpholinos or RNAi), constructs, and cell lines. Specific criteria were developed to determine if a resource was uniquely identifiable, and included examining relevant repositories (such as model organism databases, and the Antibody Registry), as well as vendor sites. The results of this experiment show that 54% of resources are not uniquely identifiable in publications, regardless of domain, journal impact factor, or reporting requirements. For example, in many cases the organism strain in which the experiment was performed or antibody that was used could not be identified. Our results show that identifiability is a serious problem for reproducibility. Based on these results, we provide recommendations to authors, reviewers, journal editors, vendors, and publishers. Scientific efficiency and reproducibility depend upon a research-wide improvement of this substantial problem in science today.
Collapse
Affiliation(s)
- Nicole A Vasilevsky
- Ontology Development Group, Library, Oregon Health & Science University , Portland, OR , USA
| | | | | | | | | | | | | |
Collapse
|
20
|
Vita R, Overton JA, Greenbaum JA, Sette A, Peters B. Query enhancement through the practical application of ontology: the IEDB and OBI. J Biomed Semantics 2013; 4 Suppl 1:S6. [PMID: 23734660 PMCID: PMC3633001 DOI: 10.1186/2041-1480-4-s1-s6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Ontologies categorize entities, express relationships between them, and provide standardized definitions. Thus, they can be used to present and enforce the specific relationships between database components. The Immune Epitope Database (IEDB, http://www.iedb.org) utilizes the Ontology for Biomedical Investigations (OBI) and several additional ontologies to represent immune epitope mapping experiments. Here, we describe our experiences utilizing this representation in order to provide enhanced database search functionality. We applied a simple approach to incorporate the benefits of the information captured in a formal ontology directly into the user web interface, resulting in an improved user experience with minimal changes to the database itself. The integration is easy to maintain, provides standardized terms and definitions, and allows for subsumption queries. In addition to these immediate benefits, our long-term goal is to enable true semantic integration of data and knowledge in the biomedical domain. We describe our progress towards that goal and what we perceive as the main obstacles.
Collapse
Affiliation(s)
- Randi Vita
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA.
| | | | | | | | | | | |
Collapse
|
21
|
Abstract
The launch of the eagle-i Consortium, a collaborative network for sharing information about research resources, such as protocols and reagents, provides a vivid demonstration of the challenges that researchers, libraries and institutions face in making their data available to others.
Collapse
|