Varaps: a python package for estimating SARS-CoV-2 lineages proportions from pooled sequencing data (ANRS0160)

Wastewater-based epidemiology has been investigated as a very effective way of monitoring SARS-CoV-2 variants. This can be achieved through accurate lineage deconvolution of wastewater sequencing data. Variants Ratios from Pooled Sequencing (VaRaPS) is a Python package designed for this purpose, utilizing pooled sequencing data and lineage mutation profiles to estimate their proportions. VaRaPS re-implements core algorithms from the literature, achieving significant improvements in computational speed and efficiency. Comparative analyzes with simulated and synthetic data sets demonstrate its superior performance in lineage prevalence estimation, underscored by its user-oriented design for broader accessibility. By improving speed and accuracy in SARS-CoV-2 variant analysis, VaRaPS offers valuable insights into viral evolution, supporting ongoing surveillance efforts in the post-pandemic landscape.

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Publication Details

Journal
BMC Bioinformatics
Published
2025-11-23
DOI
https://doi.org/10.1186/s12859-025-06299-7
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Varaps: a python package for estimating SARS-CoV-2 lineages proportions from pooled sequencing data (ANRS0160)

Marie Courbariaux, Nicolas Cluzel, Grégory Nuel, Vincent Maréchal et al.
BMC Bioinformatics
COVID-19 diagnosis using AI
article

Varaps: a python package for estimating SARS-CoV-2 lineages proportions from pooled sequencing data (ANRS0160)

Marie Courbariaux, Nicolas Cluzel, Grégory Nuel, Vincent Maréchal, El Hacene Djaout
article en

Abstract

Wastewater-based epidemiology has been investigated as a very effective way of monitoring SARS-CoV-2 variants. This can be achieved through accurate lineage deconvolution of wastewater sequencing data. Variants Ratios from Pooled Sequencing (VaRaPS) is a Python package designed for this purpose, utilizing pooled sequencing data and lineage mutation profiles to estimate their proportions. VaRaPS re-implements core algorithms from the literature, achieving significant improvements in computational speed and efficiency. Comparative analyzes with simulated and synthetic data sets demonstrate its superior performance in lineage prevalence estimation, underscored by its user-oriented design for broader accessibility. By improving speed and accuracy in SARS-CoV-2 variant analysis, VaRaPS offers valuable insights into viral evolution, supporting ongoing surveillance efforts in the post-pandemic landscape.

BMC BioinformaticsVol. 26(1)
Inserm (FR), AgroParisTech (FR), Université Paris-Saclay (FR), Microbiologie de l’alimentation au service de la santé (FR), Sorbonne Université (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Génétique Animale et Biologie Intégrative (FR), Centre de Recherche Saint-Antoine (FR), Arcagy Gineco (FR), Laboratoire de Probabilités et Modèles Aléatoires (FR), Laboratoire de Probabilités, Statistique et Modélisation (FR), Méthodes et Outils pour les Sciences Participatives, SUMMIT, Université Paris 1 Panthéon-Sorbonne (FR)
Agence Nationale de la Recherche
Openalex Percentile: Top 57%
COVID-19 diagnosis using AI
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