The following R packages has to be installed on the compute box.
library(specL) library(prozor) library(Matrix) library(yaml)
If no INPUT
is defined the report uses the r Biocpkg("specL")
package's data
and the following default parameters.
if(!exists("INPUT")){ INPUT <- list(FASTA_FILE = system.file("extdata", "SP201602-specL.fasta.gz", package = "specL"), BLIB_FILTERED_FILE = system.file("extdata", "peptideStd.sqlite", package = "specL"), BLIB_REDUNDANT_FILE = system.file("extdata", "peptideStd_redundant.sqlite", package = "specL"), MIN_IONS = 5, MAX_IONS = 6, MZ_ERROR = 0.05, MASCOTSCORECUTOFF = 17, FRAGMENTIONMZRANGE = c(300, 1250), FRAGMENTIONRANGE = c(5, 200), OUTPUT_LIBRARY_FILE = "assay_library.tsv" ) }
The library generation workflow was performed using the following parameters:
cat( " FASTA_FILE = ", INPUT$FASTA_FILE, "\n", " BLIB_FILTERED_FILE = ", INPUT$BLIB_FILTERED_FILE, "\n", " BLIB_REDUNDANT_FILE = ", INPUT$BLIB_REDUNDANT_FILE, "\n", " MZ_ERROR = ", INPUT$MZ_ERROR, "\n", " FRAGMENTIONMZRANGE = ", INPUT$FRAGMENTIONMZRANGE, "\n", " FRAGMENTIONRANGE = ", INPUT$FRAGMENTIONRANGE, "\n", " FASTA_FILE = ", INPUT$FASTA_FILE, "\n", " MAX_IONS = ", INPUT$MAX_IONS, "\n", " MIN_IONS = ", INPUT$MIN_IONS, "\n" )
The following R helper function is used for composing the in-silico
fragment ion using r CRANpkg("protViz")
.
fragmentIonFunctionUpTo2 <- function (b, y) { Hydrogen <- 1.007825 Oxygen <- 15.994915 Nitrogen <- 14.003074 b1_ <- (b ) y1_ <- (y ) b2_ <- (b + Hydrogen) / 2 y2_ <- (y + Hydrogen) / 2 return( cbind(b1_, y1_, b2_, y2_) ) }
BLIB_FILTERED <- read.bibliospec(INPUT$BLIB_FILTERED_FILE) summary(BLIB_FILTERED)
BLIB_REDUNDANT <- read.bibliospec(INPUT$BLIB_REDUNDANT_FILE) summary(BLIB_REDUNDANT)
After processing the psm using bibliospec the protein information is gone.
The read.fasta
function is provided by the CRAN package r CRANpkg("seqinr")
.
FASTA <- read.fasta(INPUT$FASTA_FILE, seqtype = "AA", as.string=TRUE) BLIB_FILTERED <- annotate.protein_id(BLIB_FILTERED, fasta=FASTA)
specLibrary <- specL::genSwathIonLib( data = BLIB_FILTERED, data.fit = BLIB_REDUNDANT, max.mZ.Da.error = INPUT$MZ_ERROR, topN = INPUT$MAX_IONS, fragmentIonMzRange = INPUT$FRAGMENTIONMZRANGE, fragmentIonRange = INPUT$FRAGMENTIONRANGE, fragmentIonFUN = fragmentIonFunctionUpTo2, mascotIonScoreCutOFF = INPUT$MASCOTSCORECUTOFF )
Total Number of PSM's with Mascot e score < 0.05, in your search is r length(BLIB_REDUNDANT)
. The number of unique precurosors is r length(BLIB_FILTERED)
.
The size of the generated ion library is r length(specLibrary@ionlibrary)
.
That means that r round(length(specLibrary@ionlibrary)/length(BLIB_FILTERED) * 100, 2)
% of the unique precursors fullfilled the filtering criteria.
summary(specLibrary )
length(specLibrary) slotNames(specLibrary) length(specLibrary@rt.input) length(specLibrary@rt.normalized) specLibrary@ionlibrary[[1]] slotNames(specLibrary@ionlibrary[[1]])
plot(specLibrary)
write.spectronaut(specLibrary, file = INPUT$OUTPUT_LIBRARY_FILE)
This report was generated using the packages:
http://bioconductor.org/packages/specL version r packageVersion('specL')
https://github.com/protViz/prozor version r packageVersion('prozor')
We have invested a lot of time and effort in creating and maintaining this software. Please cite our publication:
For questions and improvements please do contact the authors of the application generateSpecLibrary.
Here is the output of sessionInfo()
on the system on which this
document was compiled:
sessionInfo()
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