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Ortuño FM, Loucera C, Casimiro-Soriguer CS, et al. Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences. Gigascience. 2021;10(12)doi: 10.1093/gigascience/giab078.
Ortuño, F. M., Loucera, C., Casimiro-Soriguer, C. S., Lepe, J. A., Camacho Martinez, P., Merino Diaz, L., de Salazar, A., Chueca, N., García, F., Perez-Florido, J., & Dopazo, J. (2021). Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences. GigaScience, 10(12), . https://doi.org/10.1093/gigascience/giab078
Ortuño, Francisco M, et al. "Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences." GigaScience vol. 10,12 (2021). doi: https://doi.org/10.1093/gigascience/giab078
Ortuño FM, Loucera C, Casimiro-Soriguer CS, Lepe JA, Camacho Martinez P, Merino Diaz L, de Salazar A, Chueca N, García F, Perez-Florido J, Dopazo J. Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences. Gigascience. 2021 Dec 02;10(12). doi: 10.1093/gigascience/giab078. PMID: 34865008; PMCID: PMC8643610.
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Gigascience. 2021 Dec 02;10(12). doi: 10.1093/gigascience/giab078.

Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences.

GigaScience

Francisco M Ortuño, Carlos Loucera, Carlos S Casimiro-Soriguer, Jose A Lepe, Pedro Camacho Martinez, Laura Merino Diaz, Adolfo de Salazar, Natalia Chueca, Federico García, Javier Perez-Florido, Joaquin Dopazo

Affiliations

  1. Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain.
  2. Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013 Sevilla, Spain.
  3. Unidad Clínica Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen del Rocío, 41013 Sevilla, Spain.
  4. Servicio de Microbiología, Hospital Universitario San Cecilio, 18016 Granada, Spain.
  5. FPS/ELIXIR-es, Hospital Virgen del Rocío, Sevilla 42013, Spain.
  6. CIBER de Enfermedades Infecciosas (CIBERINFEC), Hospital Universitario San Cecilio, 18016 Granada, Spain.

PMID: 34865008 PMCID: PMC8643610 DOI: 10.1093/gigascience/giab078

Abstract

BACKGROUND: The current SARS-CoV-2 pandemic has emphasized the utility of viral whole-genome sequencing in the surveillance and control of the pathogen. An unprecedented ongoing global initiative is producing hundreds of thousands of sequences worldwide. However, the complex circumstances in which viruses are sequenced, along with the demand of urgent results, causes a high rate of incomplete and, therefore, useless sequences. Viral sequences evolve in the context of a complex phylogeny and different positions along the genome are in linkage disequilibrium. Therefore, an imputation method would be able to predict missing positions from the available sequencing data.

RESULTS: We have developed the impuSARS application, which takes advantage of the enormous number of SARS-CoV-2 genomes available, using a reference panel containing 239,301 sequences, to produce missing data imputation in viral genomes. ImpuSARS was tested in a wide range of conditions (continuous fragments, amplicons or sparse individual positions missing), showing great fidelity when reconstructing the original sequences, recovering the lineage with a 100% precision for almost all the lineages, even in very poorly covered genomes (<20%).

CONCLUSIONS: Imputation can improve the pace of SARS-CoV-2 sequencing production by recovering many incomplete or low-quality sequences that would be otherwise discarded. ImpuSARS can be incorporated in any primary data processing pipeline for SARS-CoV-2 whole-genome sequencing.

© The Author(s) 2021. Published by Oxford University Press GigaScience.

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