Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples.

ABSTRACT:

Background

The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja).

Methods

We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Techn

SUBMITTER: Kayikcioglu T 

PROVIDER: S-EPMC9884472 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature