ABSTRACT
As recently demonstrated by the COVID-19 pandemic, large-scale pathogen genomic data are crucial to characterize transmission patterns of human infectious diseases. Yet, current methods to process raw sequence data into analysis-ready variants remain slow to scale, hampering rapid surveillance efforts and epidemiological investigations for disease control. Here, we introduce an accelerated, scalable, reproducible, and cost-effective framework for pathogen genomic variant identification and present an evaluation of its performance and accuracy across benchmark datasets of Plasmodium falciparum malaria genomes. We demonstrate superior performance of the GPU framework relative to standard pipelines with mean execution time and computational costs reduced by 27× and 4.6×, respectively, while delivering 99.9% accuracy at enhanced reproducibility.
Subject(s)
COVID-19 , Communicable Diseases , Malaria , COVID-19/epidemiology , COVID-19/genetics , Genomics/methods , Humans , Pandemics , Reproducibility of ResultsABSTRACT
Background: Using a combination of data from routine surveillance, genomic sequencing, and phylogeographic analysis, we tracked the spread and introduction events of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants focusing on a large university community. Methods: Here, we sequenced and analyzed 677 high-quality SARS-CoV-2 genomes from positive RNA samples collected from Purdue University students, faculty, and staff who tested positive for the virus between January 2021 and May 2021, comprising an average of 32% of weekly cases across the time frame. Results: Our analysis of circulating SARS-CoV-2 variants over time revealed periods when variants of concern (VOC) Alpha (B.1.1.7) and Iota (B.1.526) reached rapid dominance and documented that VOC Gamma (P.1) was increasing in frequency as campus surveillance was ending. Phylodynamic analysis of Gamma genomes from campus alongside a subsampling of >20 000 previously published P.1 genomes revealed 10 independent introductions of this variant into the Purdue community, predominantly from elsewhere in the United States, with introductions from within the state of Indiana and from Illinois, and possibly Washington and New York, suggesting a degree of domestic spread. Conclusions: We conclude that a robust and sustained active and passive surveillance program coupled with genomic sequencing during a pandemic offers important insights into the dynamics of pathogen arrival and spread in a campus community and can help guide mitigation measures.