detect_microsatellites: Detect microsatellites

In thierrygosselin/radiator: RADseq Data Exploration, Manipulation and Visualization using R

Detect microsatellites

Description

Detect Simple Sequence Reapeats (SSR) commonly known as microsatellites... radiator 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, ...)

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, filter_rad 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.path = NULL.
...	(optional) Advance mode that allows to pass further arguments for fine-tuning the function. Also used for legacy arguments (see details or special section)

Value

6 files are returned in the folder: detect_microsatellites:

1. ".fa.fms": the fasta file of sequences

2. ".fa.fms.sat1": the summary of sequences analysed (not important)

3. ".fa.ssr": The microsatellites found per markers (see GMATA doc)

4. ".fa.ssr.sat2": Extensive summary (see GMATA doc).

5. "blacklist.microsatellites.tsv": The list of markers with microsatellites.

6. "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 radiator.

Author(s)

Thierry Gosselin [e-mail]

Examples

## Not run: 
# The simplest way to run the function when the raw data was DArT:
mic <- radiator::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


## End(Not run)

thierrygosselin/radiator documentation built on April 25, 2024, 3:20 a.m.