SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929589105721344 |
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| author | Gill, Erin E. Harari, Sheri Feng, Aijing Brinkman, Fiona S. L. Otto, Sarah |
| author_facet | Gill, Erin E. Harari, Sheri Feng, Aijing Brinkman, Fiona S. L. Otto, Sarah |
| contents | 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 or a FASTA file containing a single genome (from which lineage-defining mutations are calculated) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11201 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages Gill, Erin E. Harari, Sheri Feng, Aijing Brinkman, Fiona S. L. Otto, Sarah Populations and Evolution Genomics Quantitative Methods 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 or a FASTA file containing a single genome (from which lineage-defining mutations are calculated) 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. |
| title | SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages |
| topic | Populations and Evolution Genomics Quantitative Methods |
| url | https://arxiv.org/abs/2407.11201 |