Early detection of SARS-CoV-2 variants through dynamic co-mutation network surveillance.

ABSTRACT:

Background

Precise public health and clinical interventions for the COVID-19 pandemic has spurred a global rush on SARS-CoV-2 variant tracking, but current approaches to variant tracking are challenged by the flood of viral genome sequences leading to a loss of timeliness, accuracy, and reliability. Here, we devised a new co-mutation network framework, aiming to tackle these difficulties in variant surveillance.

Methods

To avoid simultaneous input and modeling of the whole large-scale data, we dynamically investigate the nucleotide covarying pattern of weekly sequences. The community detection algorithm is applied to a co-occurring genomic alteration network constructed from mutation corpora of weekly collected data. Co-mutation communities are identified, extracted, and cha

SUBMITTER: Huang Q 

PROVIDER: S-EPMC9901361 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature