BOLDconnectR (version 1.0.0)

bold.export: Export files generated by BOLDconnectR

Description

The function is used to export some of the output data generated by BOLDconnectR

Usage

bold.export(
  bold_df,
  export_type = c("preset_df", "msa", "fas"),
  presets = NULL,
  cols_for_fas_names = NULL,
  export
)

Value

It exports a .fas or a csv/tsv file based on the export argument.

Arguments

bold_df

The data.frame either retrieved from bold.fetch(),bold.analyze.align or a user modified BCDM dataset.

export_type

A character input specifying the type of output required. Should be either of "preset_df","msa" or "fas".

presets

A single character vector specifying a preset for which a data summary is sought (Check the details section for more information). Default value is NULL.

cols_for_fas_names

A single or multiple character vector indicating the column headers that should be used to name each sequence for the unaligned FASTA file. Default is NULL; in this case, only the processid is used as the name.

export

A character value specifying the data file path and the name for the file. Extension should be included.

Details

bold.export offers an added export option for some of the sequence-based outputs obtained from functions within the BOLDconnectR package as well as a preset defined modified BCDM dataframe. Sequence information from the BCDM data downloaded via bold.fetch() can be directly exported as an unaligned FASTA file with export_type=fas, while the aligned sequences (in the modified BCDM dataframe) obtained from bold.analyze.align can be exported as a FASTA file with export_type=msa. The FASTA headers for individual sequences when export_type=fas can be customized by using the cols_for_fas_names argument. If more than one field is specified, the name will follow the sequence of the fields given in the vector. The multiple sequence aligned FASTA file uses the same name provided by the user in the bold.analyze.align() function and using the cols_for_fas_names argument in this case will throw an error. presets can be considered as collections of predefined columns from the fetched BCDM data that relate to a common theme. The number of columns in each preset varies based on data availability. There are six presets currently available in the package (taxonomy, geography, sequences, attributions, ecology_biogeography & other_meta_data). Fields included in each preset is as follows:

• taxonomy = "kingdom", "phylum", "class", "order", "family", "subfamily", "genus", "species", "bin_uri".

• geography = "country.ocean", "country_iso", "province.state", "region", "sector", "site", "site_code", "coord", "coord_accuracy", "coord_source".

• sequences = "nuc", "nuc_basecount", "marker_code", "sequence_run_site", "sequence_upload_date".

• attributions = "inst", "identification", "identification_method", "identification_rank", "identified_by", "collectors".

• ecology_biogeography = "elev", "elev_accuracy", "depth", "depth_accuracy", "habitat", "ecoregion", "biome", "realm", "coord", "coord_source".

• other_meta_data = "notes", "taxonomy_notes", "funding_src", "voucher_type", "tissue_type", "sampling_protocol". "processids" and "sampleids" are present in all the presets. Only one preset can be used at a time. presets should be NULL when exporting a FASTA file to avoid errors . Tabular data can be exported as a csv/tsv file. Data path with the name of the output file with the corresponding file extension (csv or tsv) should be provided (Ex. 'C:/Users/xyz/Desktop/fetch_data_output.csv' for Windows). This functionality is developed with the future potential of uploading data to BOLD using the package.

Examples

if (FALSE) {
# Download the records
data_for_export_ids <- bold.public.search(taxonomy = list("Poecilia reticulata"))

# Fetch the data using the ids.
#1. api_key must be obtained from BOLD support before using `bold.fetch()` function.
#2. Use the `bold.apikey()` function  to set the apikey in the global env.

bold.apikey('apikey')

# Fetching the data using the ids
data_for_export <- bold.fetch(get_by = "processid",
                              identifiers = data_for_export_ids$processid)

#1. Export the BCDM data using 'presets' as a csv file
bold.export(bold_df=data_for_export,
            export_type = "preset_df",
            presets = 'taxonomy',
            export = file.path(tempdir(), "file_path_with_intended_name.csv"))

#2. Export the fasta file (unaligned)
# Note that input data here is the original BCDM data (data_for_export)
bold.export(bold_df = data_for_export,
            export_type = "fas",
            cols_for_fas_names = c("bin_uri","genus","species"),
            export = file.path(tempdir(),"file_path_with_intended_name.fas"))

#3. Export multiple sequence alignment
#a. Align the data
# (using processid and bin_uri as fields for sequence names)
# Users need to install and load packages `msa` and `Biostrings` before using bold.analyze.align.
seq_align<-bold.analyze.align(data_for_export,
                              cols_for_seq_names = c("processid","bin_uri"),
                              align_method = "ClustalOmega")

#b. Export the multiple sequence alignment
# Note the input data here is the modified BCDM data (seq_align)
bold.export(bold_df=seq_align,
           export_type = "msa",
            export = "file_path_with_intended_name.fas")#'
}