  SARS-CoV-2 usually evolves at a relatively constant rate over time.
Occasionally, however, lineages arise with higher-than-expected numbers of
mutations given the date of sampling. Such lineages can arise for a variety of
reasons, including selection pressures imposed by evolution during a chronic
infection or exposure to mutation-inducing drugs like molnupiravir. We have
developed an open-source web-based application (SMDP: SARS-CoV-2 Mutation
Distribution Profiler;
https://eringill.shinyapps.io/covid_mutation_distributions) that compares a
list of user-submitted lineage-defining mutations with established mutation
distributions including those observed during (1) the first nine months of the
pandemic, (2) during the global transmission of Omicron, (3) during the chronic
infection of immunocompromised patients, and (4) during zoonotic spillover from
humans to deer. The application calculates the most likely distribution for the
user's mutation list and displays log likelihoods for all distributions. In
addition, the transition:transversion ratio of the user's list is calculated to
determine whether there is evidence of exposure to a mutation-inducing drug
such as molnupiravir and indicates whether the list contains mutations in the
proofreading domain of nsp14 which could lead to a higher-than-expected
mutation rate in the lineage. This tool will be useful for public health and
researchers seeking to rapidly infer evolutionary histories of SARS-CoV-2
variants, which can aid risk assessment and public health responses.
