Reidys, C. M. | bioRxiv Science

bioRxiv2023

Motifs in SARS-CoV-2 evolution

We present a novel framework enhancing the prediction of whether novel lineage poses the threat of eventually dominating the viral population. The framework is based purely on genomic sequence data, without requiring prior established biological analysis. Its building blocks are sets of co-evolving sites in the alignment (motifs), identified via co-evolutionary signals. The collection of such motifs forms a relational structure over the polymorphic sites. Motifs are constructed using distances quantifying the co-evolutionary coupling of pairs and manifest as co-evolving clusters of sites. We present an approach to genomic surveillance based on this notion of relational structure. Our system will issue an alert regarding a lineage, based on its contribution to drastic changes in the relational structure. We then conduct a comprehensive retrospective analysis of the COVID-19 pandemic based on SARS-CoV-2 genomic sequence data in GISAID from October 2020 to September 2022, across 21 lineages and 27 countries with weekly resolution. We investigate the performance of this surveillance system in terms of its accuracy, timeliness and robustness. Lastly, we study how well each lineage is classified by such a system.

bioinformatics↗

bioRxiv2022

Buying time: detecting VOCs in SARS-CoV-2 via co-evolutionary signals

We present a novel framework facilitating the rapid detection of variants of interest (VOI) and concern (VOC) in a viral multiple sequence alignment (MSA). The framework is purely based on the genomic sequence data, without requiring prior established biological analysis. The frameworks building blocks are sets of co-evolving sites (motifs), identified via co-evolutionary signals within the MSA. Motifs form a weighted simplicial complex, whose vertices are sites that satisfy a certain nucleotide diversity. Higher dimensional simplices are constructed using distances quantifying the co-evolutionary coupling of pairs and in the context of our method maximal motifs manifest as clusters. The framework triggers an alert via a cluster with a significant fraction of newly emerging polymorphic sites. We apply our method to SARS-CoV-2, analyzing all alerts issued from November 2020 through August 2021 with weekly resolution for England, USA, India and South America. Within a week at most a handful of alerts, each of which involving on the order of 10 sites are triggered. Cross referencing alerts with a posteriori knowledge of VOI/VOC-designations and lineages, motif-induced alerts detect VOIs/VOCs rapidly, typically weeks earlier than current methods. We show how motifs provide insight into the organization of the characteristic mutations of a VOI/VOC, organizing them as co-evolving blocks. Finally we study the dependency of the motif reconstruction on metric and clustering method and provide the receiver operating characteristic (ROC) of our alert criterion.

bioinformatics↗