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SARS-CoV-2 mutation effects and 3D structure prediction from sequence covariation (RRID:SCR_018759)Copy Citation Copied
URL: https://marks.hms.harvard.edu/sars-cov-2/
Proper Citation: SARS-CoV-2 mutation effects and 3D structure prediction from sequence covariation (RRID:SCR_018759)
Description: Portal for quantitative models using virus sequence variation to predict mutation effects for SARS-CoV-2 proteins, alignments to homologs in other viruses, 3D structures, evolutionarily coupled residues and structure predictions.
Resource Type: data or information resource, disease-related portal, portal, topical portal
Keywords: SARS-CoV-2, in silico mutation scan, 3D contact prediction, COVID-19, virus sequence variation, mutation prediction, SARS-CoV-2 protein, alignment, 3D structure, structure prediction
Related Condition: COVID-19
Availability: Free, Available for download, Freely available
Resource Name: SARS-CoV-2 mutation effects and 3D structure prediction from sequence covariation
Resource ID: SCR_018759
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Source: SciCrunch Registry (RRID:SCR_005400)
Description: Interactive portal for finding and submitting biomedical resources. Resources within SciCrunch have assigned RRIDs which are used to cite resources in scientific manuscripts. SciCrunch Registry, formerly NIF Registry, provides resources catalog. Allows to add new resources. Allows edit existing resources after registration. Curators are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping contents current.
URL: https://rrid.site/rin/sources/SCR_005400
The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
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