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 ([email protected]) with that information, and we can add it to these tables.
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.
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.
First, connect to DataSpace via the R API.
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$virusNameMappingTables
names(vnm)
#> [1] "virus_metadata_all" "virus_lab_id" "virus_synonym"Find standard names using the combination of the
virus_synonym and virus_metadata_all tables.
In this example, we search for virus synonyms containing, “A10”.
vnm$virus_synonym[grepl("A10", virus_synonym)]
#> cds_virus_id virus_synonym
#> 1: cds_23 19715820_A10_H2 [SG3Δenv] 293T/17
#> 2: cds_23 19715820_A10_H2
#> 3: cds_389 AA104aRH5-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17
#> 4: cds_389 AA104aRH5-40061v3c8.LucR
#> 5: cds_389 AA104aRH5-40061.v3c8.LucR.T2A.ecto
#> 6: cds_389 AA104aRH5-40061v3c8.LucR (IMC) 293T/17
#> 7: cds_389 HIV AA104aRH5-40061v3c8.LucR/293T/17
#> 8: cds_390 AA107awg8-40061v3c8.LucR
#> 9: cds_390 AA107awg8-40061.v3c8.LucR.T2A.ecto (ChIMC) 293T/17
#> 10: cds_390 HIV AA107awg8-40061v3c8.LucR/293T/17
#> 11: cds_390 AA107awg8-40061v3c8.LucR (IMC) 293T/17
#> 12: cds_390 AA107awg8-40061.v3c8.LucR.T2A.ecto
#> 13: cds_471 HIV AA104aRH5
#> 14: cds_471 AA104aRH5
#> 15: cds_471 AA104aRH5 [SG3Δenv] 293T/17
#> 16: cds_471 HIV AA104aRH5[SG3Δenv]293T/17
#> 17: cds_472 AA107awg8 [SG3Δenv] 293T/17
#> 18: cds_472 HIV AA107awg8
#> 19: cds_472 HIV AA107awg8[SG3Δenv]293T/17
#> 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 [SG3Δenv] 293T/17
#> cds_virus_id virus_synonymOnce we have identified a virus, we can view the metadata for it
using the cds_virus_id in the
virus_metadata_all table.
vnm$virus_metadata_all[cds_virus_id == "cds_23"]
#> cds_virus_id virus virus_full_name
#> 1: cds_23 19715820_A10_H2 19715820_A10_H2 [SG3Δenv] 293T/17
#> virus_backbone virus_host_cell virus_plot_label virus_type virus_species
#> 1: SG3Δenv 293T/17 <NA> Env Pseudotype HIV
#> clade neutralization_tier
#> 1: C 2This process can also be done by merging the two tables first, then searching in the merged table.
vsn <- merge(vnm$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_synonymvirus_metadata_allOnce 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 |
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.
vnm$virus_lab_id[cds_virus_id == "cds_23"]
#> cds_virus_id lab_code lab_virus_id lab_virus_id_variable_name
#> 1: cds_23 Seaman 521 ID
#> harvest_date
#> 1: 2011-03-17 17:00:00If 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.
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.