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Thomas A, Battenfeld T, Kraiselburd I, Anastasiou O, Dittmer U, Dörr AK, Dörr A, Elsner C, Gosch J, Le-Trilling VTK, Magin S, Scholtysik R, Yilmaz P, Trilling M, Schöler L, Köster J, Meyer F. UnCoVar: a reproducible and scalable workflow for transparent and robust virus variant calling and lineage assignment using SARS-CoV-2 as an example. BMC Genomics 2024; 25:647. [PMID: 38943066 PMCID: PMC11214259 DOI: 10.1186/s12864-024-10539-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/18/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND At a global scale, the SARS-CoV-2 virus did not remain in its initial genotype for a long period of time, with the first global reports of variants of concern (VOCs) in late 2020. Subsequently, genome sequencing has become an indispensable tool for characterizing the ongoing pandemic, particularly for typing SARS-CoV-2 samples obtained from patients or environmental surveillance. For such SARS-CoV-2 typing, various in vitro and in silico workflows exist, yet to date, no systematic cross-platform validation has been reported. RESULTS In this work, we present the first comprehensive cross-platform evaluation and validation of in silico SARS-CoV-2 typing workflows. The evaluation relies on a dataset of 54 patient-derived samples sequenced with several different in vitro approaches on all relevant state-of-the-art sequencing platforms. Moreover, we present UnCoVar, a robust, production-grade reproducible SARS-CoV-2 typing workflow that outperforms all other tested approaches in terms of precision and recall. CONCLUSIONS In many ways, the SARS-CoV-2 pandemic has accelerated the development of techniques and analytical approaches. We believe that this can serve as a blueprint for dealing with future pandemics. Accordingly, UnCoVar is easily generalizable towards other viral pathogens and future pandemics. The fully automated workflow assembles virus genomes from patient samples, identifies existing lineages, and provides high-resolution insights into individual mutations. UnCoVar includes extensive quality control and automatically generates interactive visual reports. UnCoVar is implemented as a Snakemake workflow. The open-source code is available under a BSD 2-clause license at github.com/IKIM-Essen/uncovar.
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Affiliation(s)
- Alexander Thomas
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Thomas Battenfeld
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Ivana Kraiselburd
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Olympia Anastasiou
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Ulf Dittmer
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Ann-Kathrin Dörr
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Adrian Dörr
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Carina Elsner
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Jule Gosch
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Vu Thuy Khanh Le-Trilling
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Institute for the Research on HIV & AIDS-associated Diseases, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Simon Magin
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - René Scholtysik
- Institute for the Research on HIV & AIDS-associated Diseases, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Pelin Yilmaz
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Mirko Trilling
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Institute for the Research on HIV & AIDS-associated Diseases, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Lara Schöler
- Institute for Virology, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Institute for the Research on HIV & AIDS-associated Diseases, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Institute of Cell Biology (Cancer Research), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Johannes Köster
- Bioinformatics and Computational Oncology, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
- Division of Molecular and Cellular Oncology, Department of Medical Oncology, Harvard Medical School, Boston, MA, USA
| | - Folker Meyer
- Data Science Research Group, Institute for Artificial Intelligence in Medicine (IKIM), University Hospital of Essen, University of Duisburg-Essen, Essen, Germany.
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Essabbar A, Kartti S, Alouane T, Hakmi M, Belyamani L, Ibrahimi A. IDbSV: An Open-Access Repository for Monitoring SARS-CoV-2 Variations and Evolution. Front Med (Lausanne) 2021; 8:765249. [PMID: 34966754 PMCID: PMC8710592 DOI: 10.3389/fmed.2021.765249] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/05/2021] [Indexed: 11/13/2022] Open
Abstract
Ending COVID-19 pandemic requires a collaborative understanding of SARS-CoV-2 and COVID-19 mechanisms. Yet, the evolving nature of coronaviruses results in a continuous emergence of new variants of the virus. Central to this is the need for a continuous monitoring system able to detect potentially harmful variants of the virus in real-time. In this manuscript, we present the International Database of SARS-CoV-2 Variations (IDbSV), the result of ongoing efforts in curating, analyzing, and sharing comprehensive interpretation of SARS-CoV-2's genetic variations and variants. Through user-friendly interactive data visualizations, we aim to provide a novel surveillance tool to the scientific and public health communities. The database is regularly updated with new records through a 4-step workflow (1—Quality control of curated sequences, 2—Call of variations, 3—Functional annotation, and 4—Metadata association). To the best of our knowledge, IDbSV provides access to the largest repository of SARS-CoV-2 variations and the largest analysis of SARS-CoV-2 genomes with over 60 thousand annotated variations curated from the 1,808,613 genomes alongside their functional annotations, first known appearance, and associated genetic lineages, enabling a robust interpretation tool for SARS-CoV-2 variations to help understanding SARS-CoV-2 dynamics across the world.
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Affiliation(s)
- Abdelmounim Essabbar
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical and Pharmacy School, Mohammed Vth University, Rabat, Morocco
| | - Souad Kartti
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical and Pharmacy School, Mohammed Vth University, Rabat, Morocco
| | - Tarek Alouane
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical and Pharmacy School, Mohammed Vth University, Rabat, Morocco
| | - Mohammed Hakmi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical and Pharmacy School, Mohammed Vth University, Rabat, Morocco
| | - Lahcen Belyamani
- Emergency Department, Military Hospital Mohammed V, Rabat Medical & Pharmacy School, Mohammed Vth University, Rabat, Morocco
| | - Azeddine Ibrahimi
- Medical Biotechnology Laboratory (MedBiotech), Bioinova Research Center, Rabat Medical and Pharmacy School, Mohammed Vth University, Rabat, Morocco
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