Assessing SARS-CoV-2 evolution through the analysis of emerging mutations
IntroThe number of studies on SARS-CoV-2 published on a daily basis is constantly increasing, in an attempt to understand and address the challenges posed by the pandemic in a better way. Most of these studies also include a phylogeny of SARS-CoV-2 as background context, always taking into consideration the latest data in order to construct an updated tree. However, some of these studies have also revealed the difficulties of inferring a reliable phylogeny. [13] have shown that reliable phylogeny is an inherently complex task due to the large number of highly similar sequences, given the relatively low number of mutations evident in each sequence. MotivationFrom this viewpoint, there is indeed a challenge and an opportunity in identifying the evolutionary history of the SARS-CoV-2 virus, in order to assist the phylogenetic analysis process as well as support researchers in keeping track of the virus and the course of its characteristic mutations, and in finding patterns of the emerging mutations themselves and the interactions between them. The research question is formulated as follows: Detecting new patterns of co-occurring mutations beyond the strain-specific / strain-defining ones, in SARS-CoV-2 data, through the application of ML methods. AimGoing beyond the traditional phylogenetic approaches, we will be designing and implementing a clustering method that will effectively create a dendrogram of the involved sequences, based on a feature space defined on the present mutations, rather than the entire sequence. Ultimately, this ML method is tested out in sequences retrieved from public databases and validated using the available metadata as labels. The main goal of the project is to design, implement and evaluate a software that will automatically detect and cluster relevant mutations, that could potentially be used to identify trends in emerging variants. Contacttasos1109@gmail.com