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Coronavirus Resistance Database (CoV-RDB): SARS-CoV-2 susceptibility to monoclonal antibodies, convalescent plasma, and plasma from vaccinated persons (preprint)
biorxiv; 2021.
Preprint
in English
| bioRxiv | ID: ppzbmed-10.1101.2021.11.24.469823
ABSTRACT
As novel SARS-CoV-2 variants with different patterns of spike mutations have emerged, the susceptibility of these variants to neutralization by antibodies has been rapidly assessed. However, neutralization data are generated using different approaches and are scattered across different publications making it difficult for these data to be located and synthesized. The Stanford Coronavirus Resistance Database (CoV-RDB; https//covdb.stanford.edu) is designed to house comprehensively curated published data on the neutralizing susceptibility of SARS-CoV-2 variants and spike mutations to monoclonal antibodies (mAbs), convalescent plasma (CP), and vaccinee plasma (VP). As of October 2021, CoV-RDB contains 186 publications including 64 (34%) containing 7,328 neutralizing mAb susceptibility results, 96 (52%) containing 11,390 neutralizing CP susceptibility results, and 125 (68%) containing 20,872 neutralizing VP results. The database also records which spike mutations are selected during in vitro passage of SARS-CoV-2 in the presence of mAbs and which emerge in persons receiving mAbs as treatment. The CoV-RDB interface interactively displays neutralizing susceptibility data at different levels of granularity by filtering and/or aggregating query results according to one or more experimental conditions. The CoV-RDB website provides a companion sequence analysis program that outputs information about mutations present in a submitted sequence and that also assists users in determining the appropriate mutation-detection thresholds for identifying non-consensus amino acids. The most recent data underlying the CoV-RDB can be downloaded in its entirety from a Github repository in a documented machine-readable format.
Full text:
Available
Collection:
Preprints
Database:
bioRxiv
Language:
English
Year:
2021
Document Type:
Preprint
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