read.Tetrasat | R Documentation |
Given a file containing genotypes in the TETRASAT format,
read.Tetrasat
produces a genambig
object containing
genotypes and population identities from the file.
read.Tetrasat(infile)
infile |
A character string of the file path to be read. |
read.Tetrasat
reads text files that are in the exact format
specified by the software TETRASAT and TETRA (see references for more
information). This is similar to the file format for GenePop but allows
for up to four alleles per locus. All alleles must be coded by two
digits. Another difference between the TETRASAT and GenePop formats is
that in TETRASAT the sample name and genotypes are not separated by a
comma, because the columns of data have fixed widths.
Since TETRASAT files also contain information about which samples belong
to which populations, this information is put into the PopInfo
slot of the genambig
object. Population names are not taken from
the file. The Ploidies
slot is
filled with the number 4 (using the "ploidyone"
class), because all individuals should be tetraploid.
The first line of the file is put into the Description
slot.
Locus names should not contain the letters "pop", uppercase or lowercase, adjacent to each other.
A genambig
object containing data from the file.
Lindsay V. Clark
Markwith, S. H., Stewart, D. J. and Dyer, J. L. (2006) TETRASAT: a program for the population analysis of allotetraploid microsatellite data. Molecular Ecology Notes 6, 586-589.
Liao, W. J., Zhu, B. R., Zeng, Y. F. and Zhang, D. Y. (2008) TETRA: an improved program for population genetic analysis of allotetraploid microsatellite data. Molecular Ecology Resources 8, 1260–1262.
read.GeneMapper
, write.Tetrasat
,
read.ATetra
, read.GenoDive
,
read.Structure
,
read.SPAGeDi
, read.POPDIST
,
read.STRand
# example with defined data: myfile <- tempfile() cat("Sample Data", "A1_Gtype", "A10_Gtype", "B1_Gtype", "D7_Gtype", "D9_Gtype", "D12_Gtype", "Pop", "BCRHE 1 0406 04040404 0208 02020202 03030303 0710", "BCRHE 10 0406 04040404 07070707 02020202 0304 0710", "BCRHE 2 04040404 04040404 0708 02020202 010305 0710", "BCRHE 3 04040404 04040404 02020202 0203 03030303 0809", "BCRHE 4 04040404 04040404 0608 0203 03030303 070910", "BCRHE 5 04040404 04040404 0208 02020202 03030303 050710", "BCRHE 6 0304 04040404 0207 02020202 03030303 07070707", "BCRHE 7 0406 04040404 0708 02020202 03030303 07070707", "BCRHE 8 0304 04040404 0203 0203 03030303 0709", "BCRHE 9 0406 04040404 0708 02020202 03030303 0710", "Pop", "BR 1 0406 04040404 05050505 02020202 03030303 1012", "BR 10 030406 04040404 0607 02020202 03030303 1011", "BR 2 030406 04040404 07070707 02020202 03030303 09090909", "BR 3 010304 04040404 07070707 02020202 03030303 09090909", "BR 4 030406 04040404 07070707 0203 03030303 10101010", "BR 5 030406 04040404 07070707 02020202 03030303 10101010", "BR 6 0406 04040404 0507 0203 03030303 10101010", "BR 7 0304 04040404 0809 02020202 03030303 070910", "BR 8 030406 04040404 07070707 02020202 03030303 070910", "BR 9 0406 04040404 07070707 02020202 03030303 07070707", sep="\n", file=myfile) mydata2 <- read.Tetrasat(myfile) summary(mydata2) viewGenotypes(mydata2, loci="B1_Gtype")
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