Detect microsatellites — detect_microsatellites • radr

Detect Simple Sequence Reapeats (SSR) commonly known as microsatellites... radr is not re-inventing the wheel here, it uses the software GMATA: Genome-wide Microsatellite Analyzing Toward Application.

Usage

detect_microsatellites(data, gmata.dir = NULL, verbose = TRUE, ...)

Arguments

data: (path or object) Object in your global environment or a file in the working directory. The tibble must contain 2 columns named: MARKERS and SEQUENCE. When RADseq data from DArT is used, explore_genomes generates automatically this file under the name whitelist.markers.tsv.
gmata.dir: (path) For the function to work, the path to the directory with GMATA software needs to be given. If not found or NULL, the function download GMATA from github in the working directory. Default: gmata.dir = NULL.
verbose: Logical indicating whether progress messages are emitted. Default: verbose = TRUE.
...: Additional arguments passed to lower-level screening or filtering functions.

Value

6 files are returned in the folder: detect_microsatellites:

".fa.fms": the fasta file of sequences
".fa.fms.sat1": the summary of sequences analysed (not important)
".fa.ssr": The microsatellites found per markers (see GMATA doc)
".fa.ssr.sat2": Extensive summary (see GMATA doc).
"blacklist.microsatellites.tsv": The list of markers with microsatellites.
"whitelist.microsatellites.tsv": The whitelist of markers with NO microsatellites.

In the global environment, the object is a list with the blacklist and the whitelist.

Note

Thanks to Peter Grewe for the idea of including this type of filter inside radr.

Author

Thierry Gosselin thierrygosselin@icloud.com

Examples

if (FALSE) { # \dontrun{
# The simplest way to run the function when the raw data was DArT:
mic <- radr::detect_microsatellites(data = "my_whitelist.tsv")

# With stacks pipeline, the populations module need to be run with --fasta-loci
# You could prepare the file this way (uncomment the function):
#
# prep_stacks_fasta <- function(fasta.file) {
#   fasta <- suppressWarnings(
#     vroom::vroom(
#      file = fasta.file,
#      delim = "\t",
#      col_names = "DATA",
#      col_types = "c",
#      comment = "#"
#    ) %>%
#      dplyr::mutate(MARKERS = stringi::stri_sub(str = DATA, from = 3, to = 7)) %>%
#      tidyr::separate(data = ., col = DATA, into = c("SEQUENCE", "LOCUS"), sep = "_")
#  )
#
#  fasta <- dplyr::bind_cols(
#   dplyr::filter(fasta, MARKERS == "Locus") %>%
#   dplyr::select(LOCUS),
#   dplyr::filter(fasta, MARKERS != "Locus") %>%
#   dplyr::select(SEQUENCE)
#  ) %>%
#  dplyr::mutate(LOCUS = as.numeric(LOCUS))
#   return(fasta)
#  } #prep_stacks_fasta

} # }