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Anzaku ET, Mohammed MA, Ozbulak U, Won J, Hong H, Krishnamoorthy J, Van Hoecke S, Magez S, Van Messem A, De Neve W. Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection. Sci Data 2023; 10:716. [PMID: 37853038 PMCID: PMC10584977 DOI: 10.1038/s41597-023-02608-y] [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] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/28/2023] [Indexed: 10/20/2023] Open
Abstract
Trypanosomiasis, a neglected tropical disease (NTD), challenges communities in sub-Saharan Africa and Latin America. The World Health Organization underscores the need for practical, field-adaptable diagnostics and rapid screening tools to address the negative impact of NTDs. While artificial intelligence has shown promising results in disease screening, the lack of curated datasets impedes progress. In response to this challenge, we developed the Tryp dataset, comprising microscopy images of unstained thick blood smears containing the Trypanosoma brucei brucei parasite. The Tryp dataset provides bounding box annotations for tightly enclosed regions containing the parasite for 3,085 positive images, and 93 images collected from negative blood samples. The Tryp dataset represents the largest of its kind. Furthermore, we provide a benchmark on three leading deep learning-based object detection techniques that demonstrate the feasibility of AI for this task. Overall, the availability of the Tryp dataset is expected to facilitate research advancements in diagnostic screening for this disease, which may lead to improved healthcare outcomes for the communities impacted.
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Affiliation(s)
- Esla Timothy Anzaku
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea.
- IDLab, Ghent University, Technologiepark-Zwijnaarde 126, B-9052, Ghent, Belgium.
| | - Mohammed Aliy Mohammed
- IDLab, Ghent University - imec, Technologiepark-Zwijnaarde 126, B-9052, Ghent, Belgium
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia
| | - Utku Ozbulak
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
| | - Jongbum Won
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
| | - Hyesoo Hong
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
| | | | - Sofie Van Hoecke
- IDLab, Ghent University - imec, Technologiepark-Zwijnaarde 126, B-9052, Ghent, Belgium
| | - Stefan Magez
- Biomedical Research Center, Ghent University Global Campus, Incheon, 21985, South Korea
- Laboratory of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Biochemistry and Microbiology, Ghent University, Ghent, Belgium
| | | | - Wesley De Neve
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
- IDLab, Ghent University, Technologiepark-Zwijnaarde 126, B-9052, Ghent, Belgium
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Park HM, Won J, Park Y, Anzaku ET, Vankerschaver J, Van Messem A, De Neve W, Shim H. CRISPR-Cas-Docker: web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins. BMC Bioinformatics 2023; 24:167. [PMID: 37098485 PMCID: PMC10127312 DOI: 10.1186/s12859-023-05296-y] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 04/18/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND CRISPR-Cas-Docker is a web server for in silico docking experiments with CRISPR RNAs (crRNAs) and Cas proteins. This web server aims at providing experimentalists with the optimal crRNA-Cas pair predicted computationally when prokaryotic genomes have multiple CRISPR arrays and Cas systems, as frequently observed in metagenomic data. RESULTS CRISPR-Cas-Docker provides two methods to predict the optimal Cas protein given a particular crRNA sequence: a structure-based method (in silico docking) and a sequence-based method (machine learning classification). For the structure-based method, users can either provide experimentally determined 3D structures of these macromolecules or use an integrated pipeline to generate 3D-predicted structures for in silico docking experiments. CONCLUSION CRISPR-Cas-Docker addresses the need of the CRISPR-Cas community to predict RNA-protein interactions in silico by optimizing multiple stages of computation and evaluation, specifically for CRISPR-Cas systems. CRISPR-Cas-Docker is available at www.crisprcasdocker.org as a web server, and at https://github.com/hshimlab/CRISPR-Cas-Docker as an open-source tool.
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Affiliation(s)
- Ho-Min Park
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
- Department of Electronics and Information Systems, Ghent University, 9000, Ghent, Belgium
| | - Jongbum Won
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
| | - Yunseol Park
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
| | - Esla Timothy Anzaku
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
- Department of Electronics and Information Systems, Ghent University, 9000, Ghent, Belgium
| | - Joris Vankerschaver
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000, Ghent, Belgium
| | - Arnout Van Messem
- Department of Mathematics, University of Liège, 4000, Liège, Belgium
| | - Wesley De Neve
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea
- Department of Electronics and Information Systems, Ghent University, 9000, Ghent, Belgium
| | - Hyunjin Shim
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, 21985, South Korea.
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