John-Stuart Brittain, Joseph Tsui, Rhys Inward, Bernardo Gutierrez, Gaspary Mwanyika, Houriiyah Tegally, Tuyen Huynh, George Githinji, Sofonias Kifle Tessema, John T McCrone, Samir Bhatt, Abhishek Dasgupta, Stephen Ratcliffe, Moritz U G Kraemer
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GRAPEVNE - Graphical Analytical Pipeline Development Environment for Infectious Diseases.
The increase in volume and diversity of relevant data on infectious diseases and their drivers provides opportunities to generate new scientific insights that can support 'real-time' decision-making in public health across outbreak contexts and enhance pandemic preparedness. However, utilising the wide array of clinical, genomic, epidemiological, and spatial data collected globally is difficult due to differences in data preprocessing, data science capacity, and access to hardware and cloud resources. To facilitate large-scale and routine analyses of infectious disease data at the local level (i.e. without sharing data across borders), we developed GRAPEVNE (Graphical Analytical Pipeline Development Environment), a platform enabling the construction of modular pipelines designed for complex and repetitive data analysis workflows through an intuitive graphical interface. Built on the Snakemake workflow management system, GRAPEVNE streamlines the creation, execution, and sharing of analytical pipelines. Its modular approach already supports a diverse range of scientific applications, including genomic analysis, epidemiological modeling, and large-scale data processing. Each module in GRAPEVNE is a self-contained Snakemake workflow, complete with configurations, scripts, and metadata, enabling interoperability. The platform's open-source nature ensures ongoing community-driven development and scalability. GRAPEVNE empowers researchers and public health institutions by simplifying complex analytical workflows, fostering data-driven discovery, and enhancing reproducibility in computational research. Its user-driven ecosystem encourages continuous innovation in biomedical and epidemiological research but is applicable beyond that. Key use-cases include automated phylogenetic analysis of viral sequences, real-time outbreak monitoring, forecasting, and epidemiological data processing. For instance, our dengue virus pipeline demonstrates end-to-end automation from sequence retrieval to phylogeographic inference, leveraging established bioinformatics tools which can be deployed to any geographical context. For more details, see documentation at: https://grapevne.readthedocs.io.
Wellcome Open ResearchBiochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
5.50
自引率
0.00%
发文量
426
审稿时长
1 weeks
期刊介绍:
Wellcome Open Research publishes scholarly articles reporting any basic scientific, translational and clinical research that has been funded (or co-funded) by Wellcome. Each publication must have at least one author who has been, or still is, a recipient of a Wellcome grant. Articles must be original (not duplications). All research, including clinical trials, systematic reviews, software tools, method articles, and many others, is welcome and will be published irrespective of the perceived level of interest or novelty; confirmatory and negative results, as well as null studies are all suitable. See the full list of article types here. All articles are published using a fully transparent, author-driven model: the authors are solely responsible for the content of their article. Invited peer review takes place openly after publication, and the authors play a crucial role in ensuring that the article is peer-reviewed by independent experts in a timely manner. Articles that pass peer review will be indexed in PubMed and elsewhere. Wellcome Open Research is an Open Research platform: all articles are published open access; the publishing and peer-review processes are fully transparent; and authors are asked to include detailed descriptions of methods and to provide full and easy access to source data underlying the results to improve reproducibility.