Assign taxonomy functions

Usage

assign_tax(
  analysis_setup,
  asv_abund_matrix,
  retrieve_files = FALSE,
  overwrite_existing = FALSE,
  db_rps10 = "oomycetedb.fasta",
  db_its = "fungidb.fasta",
  db_16S = "bacteriadb.fasta",
  db_other1 = "otherdb1.fasta",
  db_other2 = "otherdb2.fasta"
)

Arguments

analysis_setup: An object containing directory paths and data tables, produced by the prepare_reads function
asv_abund_matrix: The final abundance matrix containing amplified sequence variants
retrieve_files: Logical, TRUE/FALSE whether to copy files from the temp directory to the output directory. Default is FALSE.
overwrite_existing: Logical, indicating whether to remove or overwrite existing files and directories from previous runs. Default is FALSE.
db_rps10: The reference database for the rps10 metabarcode
db_its: The reference database for the ITS metabarcode
db_16S: The SILVA 16S-rRNA reference database provided by the user
db_other1: The reference database for other metabarcode 1 (assumes format is like SILVA DB entries)
db_other2: The reference database for other metabarcode 2 (assumes format is like SILVA DB entries)

Value

Taxonomic assignments of each unique ASV sequence

Details

At this point DADA2 assignTaxonomy is used to assign taxonomy to the inferred ASVs.

Examples

if (FALSE) { # \dontrun{
# Assign taxonomies to ASVs on a per barcode basis
analysis_setup <- prepare_reads(
  data_directory = system.file("extdata", package = "demulticoder"),
  output_directory = tempdir(),
  tempdir_path = tempdir(),
  tempdir_id = "demulticoder_run_temp",
  overwrite_existing = TRUE
)
cut_trim(
analysis_setup,
cutadapt_path="/usr/bin/cutadapt",
overwrite_existing = TRUE
)
make_asv_abund_matrix(
analysis_setup,
overwrite_existing = TRUE
)
assign_tax(
analysis_setup,
asv_abund_matrix,
retrieve_files=FALSE,
overwrite_existing = TRUE
)
} # }