read.abif | R Documentation |
ABIF stands for Applied Biosystem Inc. Format, a binary fromat modeled after TIFF format.
Corresponding files usually have an *.ab1
or *.fsa
extension.
read.abif(filename, max.bytes.in.file = file.info(filename)$size,
pied.de.pilote = 1.2, verbose = FALSE)
filename |
The name of the file. |
max.bytes.in.file |
The size in bytes of the file, defaulting to what is returned by |
pied.de.pilote |
Safety factor: the argument |
verbose |
logical [FALSE]. If TRUE verbose mode is on. |
All data are imported into memory, there is no attempt to read items on the fly.
A list with three components: Header
which is a list that contains various low-level information,
among which numelements
is the number of elements in the directory and dataoffset
the offset to find the location of the directory. Directory
is a data.frame for the directory
of the file with the number of row being the number of elements in the directory and the 7
columns describing various low-level information about the elements. Data
is a list
with the number of components equal to the number of elements in the directory.
J.R. Lobry
citation("seqinR")
Anonymous (2006) Applied Biosystem Genetic Analysis Data File Format. Available at https://www.thermofisher.com/de/de/home/brands/applied-biosystems.html. Last visited on 03-NOV-2008.
The figure in the example section is an attempt to reproduce figure 1A from:
Krawczyk, J., Goesmann, A., Nolte, R., Werber, M., Weisshaar, B. (2009) Trace2PS and FSA2PS: two software toolkits for converting trace and fsa files to PostScript format. Source Code for Biology and Medicine, 4:4.
readBin
which is used here to import the binary file and file.info
to
get the size of the file. See JLO
for the files used in quality check.
#
# Quality check:
#
data(JLO)
JLO.check <- read.abif(system.file("abif/2_FAC321_0000205983_B02_004.fsa",
package = "seqinr"))
stopifnot(identical(JLO, JLO.check))
#
# Try to reproduce figure 1A from Krawczyk et al. 2009:
#
Krawczyk <- read.abif(system.file("abif/samplefsa2ps.fsa",
package = "seqinr"))$Data
x <- 1:length(Krawczyk[["DATA.1"]])
par(mar = c(2,4,2,0)+0.1, cex = 0.5)
plot(x, Krawczyk[["DATA.1"]], type = "l", col = "blue",
ylab = "", xlab = "",
ylim = c(-2000, 10000), cex = 0.5,
main = "Figure 1A from Krawczyk et al. 2009",
xaxs = "i", yaxs = "i",
xaxt = "n", yaxt = "n")
axis(1, at = seq(2000, 24000, by = 2000))
axis(2, at = seq(-1000, 10000, by = 1000), las = 1)
lines(x, Krawczyk[["DATA.2"]], col = "green")
lines(x, Krawczyk[["DATA.3"]], col = "black")
lines(x, Krawczyk[["DATA.4"]], col = "red")
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