Using The Virus Name Mapping Tables

Jason Taylor



The purpose of this feature is to offer users a list of various virus name synonyms that the DataSpace team has found over the years and has mapped to the standard virus names used in DataSpace. We hope that by making these maps available to the public, users who find unfamiliar virus names can use these data to help them find standard names and metadata for those viruses, or help drive questions to laboratories as to what are the appropriate metadata or standard names for a virus name found in their data.

We encourage any users who find new synonyms for virus names not listed here to reach out to the DataSpace team () with that information, and we can add it to these tables.

Our sources

We use copies of the virus databases maintained by the Comprehensive Antibody Vaccine Immune Monitoring Consortium (CAVIMC) Neutralizing Antibodies Cores at the Duke University (David Montefiori) and Harvard University (Mike Seaman) labs to generate our standard virus lists. As we process data from those labs, we have kept running lists of the different ways those viruses have been identified and have compiled those here. We update our database periodically to sync with the updates at the labs. We only provide data for viruses that we have processed or plan to process, so our records are not exhaustive mappings of the records found in those databases.

Usage caveats

Its important to know that there can be typos and misnamed viruses found in raw source data. The data that the DataSpace team provisions is checked for these types of errors before posting. A mapping that we have made in the past from a synonym to a standard name may not be appropriate for all synonyms found in your data.

Tutorial for using mapping tables

First, connect to DataSpace via the R API.

con <- connectDS()

The mapping tables are gathered via the connection object into a list that can be inspected. Each table can be merged together on the cds_virus_id field. There are three tables:

table name description
virus_metadata_all a table of CDS virus IDs, standard names, and metadata
virus_synonym a table of known synonyms for the viruses found in virus_metadata_all
virus_lab_id a table of CDS virus IDS and lab database IDs, as well as harvest dates for QC

The list can be inspected as follows:

vnm <- con$virusNameMappingTables
#> [1] "virus_metadata_all" "virus_lab_id"       "virus_synonym"

Checking the dataset for a virus name synonym

Find standard names using the combination of the virus_synonym and virus_metadata_all tables. In this example, we search for virus synonyms containing, “A10”.

Once we have identified a virus, we can view the metadata for it using the cds_virus_id in the virus_metadata_all table.

This process can also be done by merging the two tables first, then searching in the merged table.

vsn <- merge(vnm$virus_metadata_all, vnm$virus_synonym, by = "cds_virus_id")
vsn[grepl("A10", virus_synonym)]
#>     cds_virus_id                              virus
#>  1:       cds_23                    19715820_A10_H2
#>  2:       cds_23                    19715820_A10_H2
#>  3:      cds_389 AA104aRH5-40061.v3c8.LucR.T2A.ecto
#>  4:      cds_389 AA104aRH5-40061.v3c8.LucR.T2A.ecto
#>  5:      cds_389 AA104aRH5-40061.v3c8.LucR.T2A.ecto
#>  6:      cds_389 AA104aRH5-40061.v3c8.LucR.T2A.ecto
#>  7:      cds_389 AA104aRH5-40061.v3c8.LucR.T2A.ecto
#>  8:      cds_390 AA107awg8-40061.v3c8.LucR.T2A.ecto
#>  9:      cds_390 AA107awg8-40061.v3c8.LucR.T2A.ecto
#> 10:      cds_390 AA107awg8-40061.v3c8.LucR.T2A.ecto
#> 11:      cds_390 AA107awg8-40061.v3c8.LucR.T2A.ecto
#> 12:      cds_390 AA107awg8-40061.v3c8.LucR.T2A.ecto
#> 13:      cds_471                          AA104aRH5
#> 14:      cds_471                          AA104aRH5
#> 15:      cds_471                          AA104aRH5
#> 16:      cds_471                          AA104aRH5
#> 17:      cds_472                          AA107awg8
#> 18:      cds_472                          AA107awg8
#> 19:      cds_472                          AA107awg8
#> 20:      cds_472                          AA107awg8
#> 21:      cds_665                       ME067_A10-15
#> 22:      cds_665                       ME067_A10-15
#> 23:      cds_665                       ME067_A10-15
#> 24:      cds_665                       ME067_A10-15
#>     cds_virus_id                              virus
#>                                        virus_full_name virus_backbone
#>  1:                  19715820_A10_H2 [SG3Δenv] 293T/17        SG3Δenv
#>  2:                  19715820_A10_H2 [SG3Δenv] 293T/17        SG3Δenv
#>  3: AA104aRH5-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#>  4: AA104aRH5-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#>  5: AA104aRH5-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#>  6: AA104aRH5-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#>  7: AA104aRH5-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#>  8: AA107awg8-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#>  9: AA107awg8-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#> 10: AA107awg8-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#> 11: AA107awg8-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#> 12: AA107awg8-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17           <NA>
#> 13:                        AA104aRH5 [SG3Δenv] 293T/17        SG3Δenv
#> 14:                        AA104aRH5 [SG3Δenv] 293T/17        SG3Δenv
#> 15:                        AA104aRH5 [SG3Δenv] 293T/17        SG3Δenv
#> 16:                        AA104aRH5 [SG3Δenv] 293T/17        SG3Δenv
#> 17:                        AA107awg8 [SG3Δenv] 293T/17        SG3Δenv
#> 18:                        AA107awg8 [SG3Δenv] 293T/17        SG3Δenv
#> 19:                        AA107awg8 [SG3Δenv] 293T/17        SG3Δenv
#> 20:                        AA107awg8 [SG3Δenv] 293T/17        SG3Δenv
#> 21:                     ME067_A10-15 [SG3Δenv] 293T/17        SG3Δenv
#> 22:                     ME067_A10-15 [SG3Δenv] 293T/17        SG3Δenv
#> 23:                     ME067_A10-15 [SG3Δenv] 293T/17        SG3Δenv
#> 24:                     ME067_A10-15 [SG3Δenv] 293T/17        SG3Δenv
#>                                        virus_full_name virus_backbone
#>     virus_host_cell virus_plot_label     virus_type virus_species    clade
#>  1:         293T/17             <NA> Env Pseudotype           HIV        C
#>  2:         293T/17             <NA> Env Pseudotype           HIV        C
#>  3:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#>  4:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#>  5:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#>  6:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#>  7:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#>  8:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#>  9:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#> 10:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#> 11:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#> 12:         293T/17             <NA>   Chimeric IMC           HIV CRF01_AE
#> 13:         293T/17             <NA> Env Pseudotype           HIV CRF01_AE
#> 14:         293T/17             <NA> Env Pseudotype           HIV CRF01_AE
#> 15:         293T/17             <NA> Env Pseudotype           HIV CRF01_AE
#> 16:         293T/17             <NA> Env Pseudotype           HIV CRF01_AE
#> 17:         293T/17             <NA> Env Pseudotype           HIV CRF01_AE
#> 18:         293T/17             <NA> Env Pseudotype           HIV CRF01_AE
#> 19:         293T/17             <NA> Env Pseudotype           HIV CRF01_AE
#> 20:         293T/17             <NA> Env Pseudotype           HIV CRF01_AE
#> 21:         293T/17             <NA> Env Pseudotype           HIV        C
#> 22:         293T/17             <NA> Env Pseudotype           HIV        C
#> 23:         293T/17             <NA> Env Pseudotype           HIV        C
#> 24:         293T/17             <NA> Env Pseudotype           HIV        C
#>     virus_host_cell virus_plot_label     virus_type virus_species    clade
#>     neutralization_tier                                      virus_synonym
#>  1:                   2                  19715820_A10_H2 [SG3Δenv] 293T/17
#>  2:                   2                                    19715820_A10_H2
#>  3:                <NA> AA104aRH5-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17
#>  4:                <NA>                           AA104aRH5-40061v3c8.LucR
#>  5:                <NA>                 AA104aRH5-40061.v3c8.LucR.T2A.ecto
#>  6:                <NA>             AA104aRH5-40061v3c8.LucR (IMC) 293T/17
#>  7:                <NA>               HIV AA104aRH5-40061v3c8.LucR/293T/17
#>  8:                <NA>                           AA107awg8-40061v3c8.LucR
#>  9:                <NA> AA107awg8-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17
#> 10:                <NA>               HIV AA107awg8-40061v3c8.LucR/293T/17
#> 11:                <NA>             AA107awg8-40061v3c8.LucR (IMC) 293T/17
#> 12:                <NA>                 AA107awg8-40061.v3c8.LucR.T2A.ecto
#> 13:                <NA>                                      HIV AA104aRH5
#> 14:                <NA>                                          AA104aRH5
#> 15:                <NA>                        AA104aRH5 [SG3Δenv] 293T/17
#> 16:                <NA>                      HIV AA104aRH5[SG3Δenv]293T/17
#> 17:                <NA>                        AA107awg8 [SG3Δenv] 293T/17
#> 18:                <NA>                                      HIV AA107awg8
#> 19:                <NA>                      HIV AA107awg8[SG3Δenv]293T/17
#> 20:                <NA>                                          AA107awg8
#> 21:                   2                                       ME067_A10_15
#> 22:                   2                                       Me067_A10_15
#> 23:                   2                                       ME067_A10-15
#> 24:                   2                     ME067_A10-15 [SG3Δenv] 293T/17
#>     neutralization_tier                                      virus_synonym

Getting standard names from virus_metadata_all

Once a CDS virus ID has been identified, CDS standard names can be applied to your data via the metadata found in virus_metadata_all. The fields in virus_metadata_all are:

Field Description
cds_virus_id a unique identifier used by CDS to identify a given virus
virus a shorter common name of the virus
virus_full_name the full name of the virus derived from an ontology1
virus_backbone the backbone used for the virus
virus_host_cell the hostcell used for the virus
virus_plot_label a name of the virus commonly used in plotting
virus_type the type of the virus
virus_species the species of the virus
clade the clade of the virus
neutralization_tier the neutralization tier of the virus
  1. The virus_full_name ontology is defined as a 4 part combination of virus short name, backbone, target cell, and reagent.

Additional notes for users processing data from the CAVIMC Neutralizing Antibody Cores

Check the harvest date

When processing data from the CAVIMCs, its important to check the harvest date reported in the assay data against the harvest dates provided in the virus_lab_id table. Once a standard name has been identified, check the harvest date in the virus_lab_id table. The harvest date in the neutralizing antibody data should match the harvest date in the virus_lab_id table. If the harvest date does not match, its possible that the name is mislabeled, or our dataset is out of date.

Checking the harvest date can be done by querying the the virus_lab_id table with the cds_virus_id found for a given synonym.

If the combination of the lab_virus_id (and/or virus name), lab, and harvest date found in your data matches the one found in this table, its a pretty good bet that you have the right name mapping. If not, its important to check with the appropriate lab that you have chosen the correct standard name.

Check with the lab

Once virus names have been mapped, check with the appropriate lab to confirm you have the correct standard names. Please let the DataSpace team know if you believe that we have incorrect or misleading data in our database and we will investigate and correct as needed.