Identification of severity related mutation hotspots in SARS-CoV-2 using a density-based clustering approach.

ABSTRACT:

Background

The immune response to SARS-CoV-2 varies greatly among individuals yielding highly varying severity levels among the patients. While there are various methods to spot severity associated biomarkers in COVID-19 patients, we investigated highly mutated regions, or mutation hotspots, within the SARS-CoV-2 genome that correlate with patient severity levels. SARS-CoV-2 mutation hotspots were searched in the GISAID database using a density based clustering algorithm, Mutclust, that searches for loci with high mutation density and diversity.

Results

Using Mutclust, 477 mutation hotspots were searched in the SARS-CoV-2 genome, of which 28 showed significant association with severity levels in a multi-omics COVID-19 cohort comprised of 387 infected patients. The patients w

SUBMITTER: Youn S 

PROVIDER: S-EPMC12400602 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature