BOLDconnectR is a package designed for retrieval, transformation and analysis of the data available in the Barcode Of Life Data Systems (BOLD) database. This package provides the functionality to obtain public and private user data available in the database in the Barcode Core Data Model (BCDM) format. Data include information on the taxonomy,geography,collection,identification and DNA barcode sequence of every submission. The manual is currently hosted here (https://github.com/boldsystems-central/BOLDconnectR_examples/blob/main/BOLDconnectR_1.0.0.pdf)

BOLDconnectR requires R version 4.0 or above to function properly. The versions of the dependent packages, therefore, have also been set to work with R >= 4.0. In addition, there are a few suggested packages that are not mandatory for BOLDconnectR installation, but, are essential for a couple of functions to work. The names and exact versions of all the dependencies/suggestions are given here ('Imports' and 'Suggests' section) (https://github.com/boldsystems-central/BOLDconnectR/blob/main/DESCRIPTION). More details on how to install the Suggested packages are provided below.

Checking the R version and installed packages on the system

R version can be checked quickly by running the R.version command in your console.

All the installed packages with their respective versions can be checked using the installed.packages() function.

BOLDconnectR Installation

The package can be installed using devtools::install_github function from the devtools package in R (which needs to be installed before installing BOLDConnectR).

devtools::install_github("https://github.com/boldsystems-central/BOLDconnectR")

library(BOLDconnectR)

BOLDconnectR has 10 functions currently:

1. bold.fields.info
2. bold.apikey
3. bold.fetch
4. bold.public.search
5. bold.data.summarize
6. bold.analyze.align
7. bold.analyze.tree
8. bold.analyze.diversity
9. bold.analyze.map
10. bold.export

Note on Suggested packages

bold.analyze.align requires the packages msa and Biostrings to be installed and imported in the R session beforehand. Function 7 also uses the output generated from function 6. msa and Biostrings can be installed using using BiocManager package.

if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")

BiocManager::install("msa")
BiocManager::install("Biostrings")

library(msa)
library(Biostrings)

Note on API key

The function bold.fetch requires an api key internally in order to access and download all public + private user data. The API key needed to retrieve BOLD records is found in the BOLD ‘Workbench’ https://bench.boldsystems.org/index.php/Login/page?destination=MAS_Management_UserConsole. After logging in, navigate to ‘Your Name’ (located at the top left-hand side of the window) and click ‘Edit User Preferences’. You can find the API key in the ‘User Data’ section. Please note that to have an API key available in the workbench, a user must have uploaded at least 10,000 records to BOLD. API key can be saved in the R session using bold.apikey() function.

# Substitute ‘00000000-0000-0000-0000-000000000000’ with your key
# bold.apikey(‘00000000-0000-0000-0000-000000000000’)

Basic usage of BOLDConnectR functionality

API key function must be run prior to using the fetch function (Please see above).

Fetch data

BCDM_data<-bold.fetch(get_by = "processid",
                      identifiers = test.data$processid)
#> Initiating download
#>  Downloading data in a single batch  
#> Download complete & BCDM dataframe generated

knitr::kable(head(BCDM_data,4))

Similarly, sampleids or dataset_codes or project_codes can also be used to fetch data. The data can also be filtered on different parameters such as Geography, Attributions and DNA Sequence information using the _filt arguments available in the function

Summarize downloaded data

Downloaded data can then be summarized in differnt ways. Summaries are generated either on the whole dataset, specific presets (please check the details section of bold.export() function in the package manual for details) or specific columns.

BCDM_data_summary<-bold.data.summarize(bold_df = BCDM_data,
                               summarize_by = 'presets',
                               presets = "geography")
#> ── Data Summary ────────────────────────
#>                            Values    
#> Name                       Piped data
#> Number of rows             1336      
#> Number of columns          12        
#> _______________________              
#> Column type frequency:               
#>   character                11        
#>   numeric                  1         
#> ________________________             
#> Group variables            None

BCDM_data_summary$summary

Detailed data summary

Variable type: character

Variable type: numeric

A data summary profile containing aggregates and completeness information is provided as a combination of tabular output and visualization

Export the downloaded data

Downloaded data can also be exported to the local machine either as a flat file or as a FASTA file for any third-party sequence analysis tools. The flat file contents can be modified as per user requirements (entire data/specific presets/individual fields).

# Preset dataframe
# bold.export(bold_df = BCDM_data,
#             export_type = "preset_df",
#             presets = 'taxonomy',
#             export_to = "file_path_with_intended_name")

# Unaligned fasta file
# bold.export(bold_df = BCDM_data,
#             export_type = "fas",
#             cols_for_fas_names = c("bin_uri","genus","species"),
#             export_to = "file_path_with_intended_name")

Other functions

The package also has analyses functions that provide sequence alignment, NJ clustering, biodiversity analysis, occurrence mapping using the downloaded BCDM data. Additionally, these functions also output objects that are commonly used by other R packages (‘sf’ dataframe, occurrence matrix). Please go through the help manual (Link provided above) for detailed usage of all the functions of BOLDConnectR with examples.

BOLDconnectR is able to retrieve data very fast (~100k records in a minute on a fast wired connection).

Citation: Padhye SM, Agda TJA, Agda JRA, Ballesteros-Mejia CL, Ratnasingham S. BOLDconnectR: An R package for interacting with the Barcode of Life Data (BOLD) system.(MS in prep)