From Alpha to Zeta: Identifying variants and subtypes of SARS-CoV-2 via clustering

The availability of millions of SARS-CoV-2 sequences in public databases such as GISAID and EMBL-EBI (UK) allows a detailed study of the evolution, genomic diversity and dynamics of a virus like never before. Here we identify novel variants and subtypes of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intra-host viral populations. We asses our results using clustering entropy --- the first time it has been used in this context. Our clustering approach reaches lower entropies compared to other methods, and we are able to boost this even further through gap filling and Monte Carlo based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the UK and GISAID datasets, but is also able to detect the much less represented (