,
Jonas Grossmann [aut] ,
Simon Barkow-Oesterreicher [ctb]| Title: | Visualizing and Analyzing Mass Spectrometry Related Data in Proteomics |
|---|---|
| Description: | Helps with quality checks, visualizations and analysis of mass spectrometry data, coming from proteomics experiments. The package is developed, tested and used at the Functional Genomics Center Zurich <https://fgcz.ch>. We use this package mainly for prototyping, teaching, and having fun with proteomics data. But it can also be used to do data analysis for small scale data sets. |
| Authors: | Christian Panse [aut, cre] ,
Jonas Grossmann [aut] ,
Simon Barkow-Oesterreicher [ctb] |
| Maintainer: | Christian Panse <[email protected]> |
| License: | GPL-3 |
| Version: | 0.7.9 |
| Built: | 2025-01-30 04:23:57 UTC |
| Source: | https://github.com/cpanse/protviz |
Among other attributes it contains '1-letter code', 'monoisotopic mass' and 'average mass' of each amino acids.
contains a table
returns a data.frame
Christian Panse 2013
data(AA) AA AA.lm<-lm(AA$Monoisotopic ~ AA$Average) plot(AA$Monoisotopic, AA$Average); abline(AA.lm, col='grey') text(AA$Monoisotopic, AA$Average, AA$letter1, pos=3) plot(AA$Average-AA$Monoisotopic) axis(3,1:20,AA$letter1); abline(v=1:20,col='grey') # computes monoisotopic mass out of formula using the CDK package ## Not run: if (require(rcdk)){ plot(AA$Monoisotopic, sapply(AA$formula, function(x){ get.formula(as.character(x), charge = 1)@mass })) } ## End(Not run) ## Not run: if (require(XML)){ unimodurl <- url("http://www.unimod.org/xml/unimod_tables.xml") unimod.list <- XML::xmlToList( XML::xmlParse( scan(unimodurl, what = character()))) unimod.AA <- data.frame( do.call('rbind', unimod.list$amino_acids)) rownames(unimod.AA) <- unimod.AA$one_letter } ## End(Not run)data(AA) AA AA.lm<-lm(AA$Monoisotopic ~ AA$Average) plot(AA$Monoisotopic, AA$Average); abline(AA.lm, col='grey') text(AA$Monoisotopic, AA$Average, AA$letter1, pos=3) plot(AA$Average-AA$Monoisotopic) axis(3,1:20,AA$letter1); abline(v=1:20,col='grey') # computes monoisotopic mass out of formula using the CDK package ## Not run: if (require(rcdk)){ plot(AA$Monoisotopic, sapply(AA$formula, function(x){ get.formula(as.character(x), charge = 1)@mass })) } ## End(Not run) ## Not run: if (require(XML)){ unimodurl <- url("http://www.unimod.org/xml/unimod_tables.xml") unimod.list <- XML::xmlToList( XML::xmlParse( scan(unimodurl, what = character()))) unimod.AA <- data.frame( do.call('rbind', unimod.list$amino_acids)) rownames(unimod.AA) <- unimod.AA$one_letter } ## End(Not run)
The function determines the weight of each amino acid fiven sequence.
aa2mass(peptideSequence, mass=AA$Monoisotopic, letter1=AA$letter1)aa2mass(peptideSequence, mass=AA$Monoisotopic, letter1=AA$letter1)
peptideSequence |
a double vector which can be considered as query objects. |
mass |
A vector of size 20 containing the weight of the AA. |
letter1 |
AA letter 1 code in the same size and order as the |
For the computation no C-Term and N-Term is considered.
aa2mass is useful if you have AA modifications.
Christian Panse 2014
peptides<-c('HTLNQIDSVK', 'ALGGEDVR', 'TPIVGQPSIPGGPVR') C_term <- 17.002740 N_term <- 1.007825 H_ <- 1.008 unlist(lapply(aa2mass(peptides), sum)) + C_term + N_term + H_ - parentIonMass(peptides) # determine the fragment ions lapply(aa2mass(peptides), function(x){fragmentIon(x)[[1]]}) # an example with [STY] AA modification peptide<-'HTLNQIDSVK' mod <- rep(0.0, nchar(peptide)); mod[8] <- 79.998; aa2mass(peptide)[[1]] + modpeptides<-c('HTLNQIDSVK', 'ALGGEDVR', 'TPIVGQPSIPGGPVR') C_term <- 17.002740 N_term <- 1.007825 H_ <- 1.008 unlist(lapply(aa2mass(peptides), sum)) + C_term + N_term + H_ - parentIonMass(peptides) # determine the fragment ions lapply(aa2mass(peptides), function(x){fragmentIon(x)[[1]]}) # an example with [STY] AA modification peptide<-'HTLNQIDSVK' mod <- rep(0.0, nchar(peptide)); mod[8] <- 79.998; aa2mass(peptide)[[1]] + mod
Data for reproducing Figures 1-5 in doi:10.1021/jasms.0c00040.
Christian Panse 2022
defines a class of peptide spectrum matches.
is.psmSet(object) ## S3 method for class 'mascot' as.psmSet(object, ...)is.psmSet(object) ## S3 method for class 'mascot' as.psmSet(object, ...)
object |
an |
... |
whatoever |
the is.psmSet method checks if the as input given objects fullfills the
proterties to be an psmSet object.
as.psmSet transformas an object into a psmSet object.
while is.psmSet returns TRUE or FALSE,
as.psmSet returns an instance of psmSet or NULL.
Christian Panse 2017
The function implements a space-filling curve mapping 1D to 2D. This function aims to assign a sequence of samples to an instrument plate, e.g., 48 well plate 85.4x127.5mm.
assignPlatePosition( S, x = as.character(1:8), y = c("A", "B", "C", "D", "E", "F"), plate = 1:4, volume = 1, reserve = 46:48 )assignPlatePosition( S, x = as.character(1:8), y = c("A", "B", "C", "D", "E", "F"), plate = 1:4, volume = 1, reserve = 46:48 )
S |
input data frame |
x |
a vector of possible x-coordinates of the plate |
y |
a vector of possible y-coordinates of the plate |
plate |
a vector of plates |
volume |
injection volume |
reserve |
block plate positions |
a data.frame
iris[c(1:15, 51:65, 101:115), ] |> blockRandom(x = "Species", check=FALSE) |> assignPlatePosition()iris[c(1:15, 51:65, 101:115), ] |> blockRandom(x = "Species", check=FALSE) |> assignPlatePosition()
generated using proteowizards IsotopeEnvelopeEstimator class.
first column mass, column 1:8 l2 normalized isotope intensities
Witold Wolski and Christian Panse 2013
A general approach to calculating isotopic distributions for mass spectrometry James A. Yergey Volume 52, Issues 2 1 September 1983, Pages 3379
doi:10.1016/0020-7381(83)85053-0 https://proteowizard.sourceforge.io/
data(averagine) r<-seq(0,1,length=200); cm<-c(rgb(r,r,r), '#555599') image(m<-as.matrix(averagine), col=rev(cm), axes=FALSE, main='protViz averagine data - normalized isotope intensities ', sub='the blue color indicates fields with value 0.', xlab='isotops', ylab='mass'); box() axis(1, seq(0,1,length=nrow(m)), 1:nrow(m)); axis(2, seq(0,1,length=10), colnames(m)[seq(1,ncol(m), length=10)])data(averagine) r<-seq(0,1,length=200); cm<-c(rgb(r,r,r), '#555599') image(m<-as.matrix(averagine), col=rev(cm), axes=FALSE, main='protViz averagine data - normalized isotope intensities ', sub='the blue color indicates fields with value 0.', xlab='isotops', ylab='mass'); box() axis(1, seq(0,1,length=nrow(m)), 1:nrow(m)); axis(2, seq(0,1,length=10), colnames(m)[seq(1,ncol(m), length=10)])
Derive a randomization of a table.
blockRandom(S, x = NA, check = TRUE)blockRandom(S, x = NA, check = TRUE)
S |
a |
x |
the column name of the group used for the block randomization. |
check |
check if the size of each group is equal. |
returns a randomization order.
set.seed(1) iris[c(1:2, 51:52, 101:103), ] |> blockRandom(x = "Species", check=FALSE)set.seed(1) iris[c(1:2, 51:52, 101:103), ] |> blockRandom(x = "Species", check=FALSE)
returns a centroid spectrum of a recorded profile mode spectrum.
centroid(mZ, intensity, tolppm=100, debug=FALSE)centroid(mZ, intensity, tolppm=100, debug=FALSE)
mZ |
Numerical vector of profile recorded data and sorted mZ values. |
intensity |
corresponding intensity values. |
tolppm |
maximal distance in Da between to profile mZ values. default is set to 100ppm. |
debug |
if true all peak profiles are plotted. default is false. |
the method is tested on an Orbitrap Fusion Lumos FSN20242 data set.
returns a data.frame with a mZ and a intensity column.
thanks to Nienke Meekel and Andrea Brunner (kwrwater.nl) and Witold E. Wolski.
Christian Panse and Jonas Grossmann, April 2020
# Orbitrap Fusion Lumos FSN20242 # p2722/.../.../stds_pos_neg_MS_highconc_UVPD_50_300.raw # scan 1959 # CC(C)(C)C(O)C(OC1=CC=C(Cl)C=C1)N1C=NC=N1 1 # exact.mass 295.1088 # FTMS + p ESI d Full ms2 [email protected] p <- protViz:::.getProfileMS2() # determine eps plot((diff(p$mZ)) ~ p$mZ[2:length(p$mZ)], log='y'); abline(h=0.1, col='red') points(p$mZ , 1E-4 * p$mZ, col='grey', type='l') abline(v=296.1162, col='cyan') op <- par(mfrow=c(2, 1)) plot(p$mZ, p$intensity, type='h', main='profile', xlim=c(100,300)) abline(v=296.1162, col='cyan') plot(centroid(p$mZ, p$intensity),type='h', main="centroid",xlim=c(100,300)) par(op) op <- par(mfrow=c(2, 1), ask = TRUE) rv <- centroid(p$mZ, p$intensity, debug = TRUE)# Orbitrap Fusion Lumos FSN20242 # p2722/.../.../stds_pos_neg_MS_highconc_UVPD_50_300.raw # scan 1959 # CC(C)(C)C(O)C(OC1=CC=C(Cl)C=C1)N1C=NC=N1 1 # exact.mass 295.1088 # FTMS + p ESI d Full ms2 [email protected] p <- protViz:::.getProfileMS2() # determine eps plot((diff(p$mZ)) ~ p$mZ[2:length(p$mZ)], log='y'); abline(h=0.1, col='red') points(p$mZ , 1E-4 * p$mZ, col='grey', type='l') abline(v=296.1162, col='cyan') op <- par(mfrow=c(2, 1)) plot(p$mZ, p$intensity, type='h', main='profile', xlim=c(100,300)) abline(v=296.1162, col='cyan') plot(centroid(p$mZ, p$intensity),type='h', main="centroid",xlim=c(100,300)) par(op) op <- par(mfrow=c(2, 1), ask = TRUE) rv <- centroid(p$mZ, p$intensity, debug = TRUE)
This function computes the mass differences of MS2 peaks and matches the 'delta masses' to the amino acid weights.
de_novo(data)de_novo(data)
data |
ms data set. |
This function should only be used for demonstration!
Christian Panse 2013
data(msms) de_novo(msms[[1]])data(msms) de_novo(msms[[1]])
deisotoper returns the indices and scores of the isotop pattern.
The score is computed by considering the isotop L2 norm of a given iostop
model. protViz contains the averagine data.
deisotoper(data, Z=1:4, isotopPatternDF=averagine, massError=0.005, plot=FALSE)deisotoper(data, Z=1:4, isotopPatternDF=averagine, massError=0.005, plot=FALSE)
data |
A mass spectrometric measurement containing a list. |
Z |
Charge states to be considered. The default is |
isotopPatternDF |
a data frame containing isotope envelopes of peptide averagine. |
massError |
mass error in Dalton. default is 0.005. |
plot |
boolean if the isotops should be plotted. The default is false. |
The output of the deisotoper function is a list data structure containing
for each input MS data set a result, score, and group lists.
result is a list which contains for each charge state a list of isotop groups.
Analoge to result a score is provide in the score list.
group provides information about the charge states of each isotop cluster.
The algorithm for finding the isotop chains as implemented in protViz is based on the idea of
'Features-Based Deisotoping Method for Tandem Mass Spectra'.
Each peak represents one node of a graph G=(V,E) with V = {1, ..., n}.
The edges are defined by (v, node_array_G[v]) iff node_array_G[v] > -1.
In other words an edge is defined between two peaks if the mZ difference is below the given
massError. The isotop chains are determined by a DFS run.
The time complexity is O(n log (n)) where n is the number of peaks.
Witold Eryk Wolski, Christian Trachsel, and Christian Panse 2013
isotopic-cluster graphs:
Features-Based Deisotoping Method for Tandem Mass Spectra, Zheng Yuan Jinhong Shi Wenjun Lin Bolin Chen and Fang-Xiang Wu Advances in Bioinformatics Volume 2011 (2011), Article ID 210805, 12 pages doi:10.1155/2011/210805
# example1 - tandem ms x <- list(mZ = c(726.068, 726.337, 726.589, 726.842, 727.343, 727.846, 728.346, 728.846, 729.348, 730.248, 730.336, 730.581, 730.836), intensity = c(6.77850e+03, 2.81688e+04, 6.66884e+04, 1.22032e+07, 9.90405e+06, 4.61409e+06, 1.50973e+06, 3.33996e+05, 5.09421e+04, 1.15869e+03, 2.14788e+03, 5.37853e+03, 5.79094e+02)) # the plain C interface function out1 <- .Call("deisotoper_main", x$mZ, x$intensity, Z=1:4, averagine, massError=0.01, PACKAGE="protViz") out<-deisotoper(data=list(x), Z=2:4, isotopPatternDF=averagine) # example2 - a ms from heavy labeld peptide in water background x <- list(mZ=c(642.572, 643.054, 643.569, 644.062, 644.557), intensity=c(17000, 25000, 12000, 9000,4000)) diff(x$mZ) diff(x$mZ,lag=2) diff(x$mZ, difference=2) out2<-deisotoper(data=list(x), Z=1:2, isotopPatternDF=averagine, massError=0.02, plot=TRUE)# example1 - tandem ms x <- list(mZ = c(726.068, 726.337, 726.589, 726.842, 727.343, 727.846, 728.346, 728.846, 729.348, 730.248, 730.336, 730.581, 730.836), intensity = c(6.77850e+03, 2.81688e+04, 6.66884e+04, 1.22032e+07, 9.90405e+06, 4.61409e+06, 1.50973e+06, 3.33996e+05, 5.09421e+04, 1.15869e+03, 2.14788e+03, 5.37853e+03, 5.79094e+02)) # the plain C interface function out1 <- .Call("deisotoper_main", x$mZ, x$intensity, Z=1:4, averagine, massError=0.01, PACKAGE="protViz") out<-deisotoper(data=list(x), Z=2:4, isotopPatternDF=averagine) # example2 - a ms from heavy labeld peptide in water background x <- list(mZ=c(642.572, 643.054, 643.569, 644.062, 644.557), intensity=c(17000, 25000, 12000, 9000,4000)) diff(x$mZ) diff(x$mZ,lag=2) diff(x$mZ, difference=2) out2<-deisotoper(data=list(x), Z=1:2, isotopPatternDF=averagine, massError=0.02, plot=TRUE)
Implements as Rcpp module performing tryptic digests and summaries. The support is limited for protein sequences.
FastaFasta
returns a list or vector objects.
Christian Panse <[email protected]> 2006-2017
https://en.wikipedia.org/wiki/FASTA_format
# >sp|P12763|FETUA_BOVIN Alpha-2-HS-glycoprotein OS=Bos taurus fname <- system.file("extdata", name='P12763.fasta', package = "protViz") F <- Fasta$new(fname) F F$summary() F$getTrypticPeptides() plot(parentIonMass(F$getTrypticPeptides()), ssrc(F$getTrypticPeptides()), log='xy') text(parentIonMass(F$getTrypticPeptides()), ssrc(F$getTrypticPeptides()), F$getTrypticPeptides(), pos=3, cex=0.5)# >sp|P12763|FETUA_BOVIN Alpha-2-HS-glycoprotein OS=Bos taurus fname <- system.file("extdata", name='P12763.fasta', package = "protViz") F <- Fasta$new(fname) F F$summary() F$getTrypticPeptides() plot(parentIonMass(F$getTrypticPeptides()), ssrc(F$getTrypticPeptides()), log='xy') text(parentIonMass(F$getTrypticPeptides()), ssrc(F$getTrypticPeptides()), F$getTrypticPeptides(), pos=3, cex=0.5)
This data set contains the read-out abundance for three different methods (APEX, emPAI, T3PQ) for seven proteins from a complex yeast background (abundance levels unchanged) and the Fetuin that was spiked into the extracts in concentrations ranging from 20fmol to 300fmol on column (in triplicates).
Samples were analyzed on a LTQ-FTICR Ultra mass spectrometer (Thermo Fischer Scientific, Bremen, Germany) coupled to an Eksigent-Nano-HPLC system (Eksigent Technologies, Dublin (CA), USA).
The data can be derived out of the mzXML files:
1 186240760 2008-12-11 20080816_01_fetuin.mzXML
2 179013841 2008-12-11 20080816_02_fetuin_0.mzXML
3 178692924 2008-12-11 20080816_03_fetuin_0.mzXML
4 178608302 2008-12-11 20080816_04_fetuin_0.mzXML
5 180995463 2008-12-11 20080816_05_fetuin_20.mzXML
6 175934898 2008-12-11 20080816_06_fetuin_20.mzXML
7 178364454 2008-12-11 20080816_07_fetuin_20.mzXML
8 184376874 2008-12-11 20080816_08_fetuin_40.mzXML
9 187205031 2008-12-11 20080816_09_fetuin_40.mzXML
10 185646382 2008-12-11 20080816_10_fetuin_40.mzXML
11 183498944 2008-12-11 20080816_11_fetuin_60.mzXML
12 184752098 2008-12-11 20080816_12_fetuin_60.mzXML
13 186243524 2008-12-11 20080816_13_fetuin_60.mzXML
14 187794055 2008-12-11 20080816_14_fetuin_80.mzXML
15 183463368 2008-12-11 20080816_15_fetuin_80.mzXML
16 186331090 2008-12-11 20080816_16_fetuin_80.mzXML
17 188027950 2008-12-11 20080816_17_fetuin_100.mzXML
18 186881098 2008-12-11 20080816_18_fetuin_100.mzXML
19 187219923 2008-12-11 20080816_19_fetuin_100.mzXML
20 187157009 2008-12-11 20080816_20_fetuin_120.mzXML
21 190008885 2008-12-11 20080816_21_fetuin_120.mzXML
22 184226648 2008-12-11 20080816_22_fetuin_120.mzXML
23 190681343 2008-12-11 20080816_23_fetuin_160.mzXML
24 194653066 2008-12-11 20080816_24_fetuin_160.mzXML
25 191045349 2008-12-11 20080816_25_fetuin_160.mzXML
26 184869491 2008-12-11 20080816_26_fetuin_200.mzXML
27 185490782 2008-12-11 20080816_27_fetuin_200.mzXML
28 185635558 2008-12-11 20080816_28_fetuin_200.mzXML
29 187743192 2008-12-11 20080816_29_fetuin_300.mzXML
30 190613235 2008-12-11 20080816_30_fetuin_300.mzXML
31 189570723 2008-12-11 20080816_31_fetuin_300.mzXML
32 226404551 2008-12-11 20080819_32_fetuin.mzXML
contained in a 3GBytes compressed tar ball http://fgcz-data.uzh.ch/public/fqms.tgz md5=af804e209844d055c0eded716ef9eea8.
The t3pq data can be derived using the code from https://sourceforge.net/projects/fqms/
A data set with approx. 600 rows and four variables, 4KBytes file size, derived out of 32 single LC-MS runs, 5GBytes size, of a protein mixture.
Christian Panse 2009, 2010
doi:10.1016/j.jprot.2010.05.011
library(lattice) data(fetuinLFQ) cv<-1-1:7/10 t<-trellis.par.get("strip.background") t$col<-(rgb(cv,cv,cv)) trellis.par.set("strip.background",t) my.xlab="Fetuin concentration spiked into experiment [fmol]" my.ylab<-"Abundance" xyplot(abundance~conc|prot*method, data=fetuinLFQ$apex, groups=prot, aspect=1, panel = function(x, y, subscripts, groups) { if (groups[subscripts][1] == "Fetuin") { panel.fill(col="#ffcccc") } panel.grid(h=-1,v=-1) panel.xyplot(x, y) panel.loess(x,y, span=1) }, xlab=my.xlab, ylab=my.ylab ) xyplot(abundance~conc|prot*method, data=fetuinLFQ$empai, groups=prot, aspect=1, panel = function(x, y, subscripts, groups) { if (groups[subscripts][1] == "Fetuin") { panel.fill(col="#ffcccc") } panel.grid(h=-1,v=-1) panel.xyplot(x, y) panel.loess(x,y, span=1) }, xlab=my.xlab, ylab=my.ylab ) xyplot(abundance~conc|prot*method, data=fetuinLFQ$t3pq, groups=prot, aspect=1, panel = function(x, y, subscripts, groups) { if (groups[subscripts][1] == "Fetuin") { panel.fill(col="#ffcccc") } panel.grid(h=-1,v=-1) panel.xyplot(x, y) panel.loess(x,y, span=1) if (groups[subscripts][1] == "Fetuin") { panel.text(min(fetuinLFQ$t3pq$conc), max(fetuinLFQ$t3pq$abundance), paste("R-squared:", round(summary(lm(x~y))$r.squared,2)), cex=0.75, pos=4) } }, xlab=my.xlab, ylab=my.ylab )library(lattice) data(fetuinLFQ) cv<-1-1:7/10 t<-trellis.par.get("strip.background") t$col<-(rgb(cv,cv,cv)) trellis.par.set("strip.background",t) my.xlab="Fetuin concentration spiked into experiment [fmol]" my.ylab<-"Abundance" xyplot(abundance~conc|prot*method, data=fetuinLFQ$apex, groups=prot, aspect=1, panel = function(x, y, subscripts, groups) { if (groups[subscripts][1] == "Fetuin") { panel.fill(col="#ffcccc") } panel.grid(h=-1,v=-1) panel.xyplot(x, y) panel.loess(x,y, span=1) }, xlab=my.xlab, ylab=my.ylab ) xyplot(abundance~conc|prot*method, data=fetuinLFQ$empai, groups=prot, aspect=1, panel = function(x, y, subscripts, groups) { if (groups[subscripts][1] == "Fetuin") { panel.fill(col="#ffcccc") } panel.grid(h=-1,v=-1) panel.xyplot(x, y) panel.loess(x,y, span=1) }, xlab=my.xlab, ylab=my.ylab ) xyplot(abundance~conc|prot*method, data=fetuinLFQ$t3pq, groups=prot, aspect=1, panel = function(x, y, subscripts, groups) { if (groups[subscripts][1] == "Fetuin") { panel.fill(col="#ffcccc") } panel.grid(h=-1,v=-1) panel.xyplot(x, y) panel.loess(x,y, span=1) if (groups[subscripts][1] == "Fetuin") { panel.text(min(fetuinLFQ$t3pq$conc), max(fetuinLFQ$t3pq$abundance), paste("R-squared:", round(summary(lm(x~y))$r.squared,2)), cex=0.75, pos=4) } }, xlab=my.xlab, ylab=my.ylab )
Given a vector of sorted double values vec of size
n and a vector of m query objects q.
findNN determines for each element q[i] in q
the nearest neighbor index o so that the following remains true:
there is no element k with 1 k
n and k is not o so that
abs(vec[k] - q[i]) < abs(vec[o] - q[i]).
findNN(q, vec, check) findNN_(q, vec, check)findNN(q, vec, check) findNN_(q, vec, check)
q |
a double vector which can be considered as query objects. |
vec |
a sorted double vector which can be considered as a data base. |
check |
boolean enables test if |
The internal algorithm of findNN is implemented as binary search.
findNN has time complexity where n is defined as
length(vec) and m is length(m).
findNN is implemented using C library function - bsearch(),
while findNN_ uses C++11 STL function lower_bound().
Christian Panse 2007, 2008, 2009, 2010, 2012 , 2015 based on the C++ STL lower_bound method.
(NNidx <- findNN(q <- c(1, 1.0001, 1.24, 1.26), DB <- seq(1, 5 , by = 0.25))) (NNidx == c(1, 1, 2, 2)) DB <- sort(rnorm(100, mean=100, sd = 10)) # should be 0 unique(DB[findNN(DB,DB)] - DB) q <- rnorm(100, mean=100) idx.NN <- findNN(q,DB) hist(DB[findNN(q,DB)] - q) # definition of findNN holds i <- 1:5 findNN(3.5, i) i <- 1:6 findNN(3.5, i) # compare ANSI-C binary search with C++ std::lower_bound DB <- c(rep(1.0, 3), rep(2.0, 3)) q <- c(-1, 1.0, 1.01, 1.5, 1.9) abs(DB[findNN(q, DB)] - q) abs(DB[findNN_(q, DB)] - q) DB <- sort(rnorm(100, mean=100, sd=10)) # should be 0 unique(DB[findNN_(DB,DB)] - DB) q <- rnorm(100, mean=100) idx.NN <- findNN_(q, DB) hist(DB[findNN_(q, DB)] - q) # definition of findNN_ holds i <- 1:5 findNN_(3.5, i) i <- 1:6 findNN_(3.5, i)(NNidx <- findNN(q <- c(1, 1.0001, 1.24, 1.26), DB <- seq(1, 5 , by = 0.25))) (NNidx == c(1, 1, 2, 2)) DB <- sort(rnorm(100, mean=100, sd = 10)) # should be 0 unique(DB[findNN(DB,DB)] - DB) q <- rnorm(100, mean=100) idx.NN <- findNN(q,DB) hist(DB[findNN(q,DB)] - q) # definition of findNN holds i <- 1:5 findNN(3.5, i) i <- 1:6 findNN(3.5, i) # compare ANSI-C binary search with C++ std::lower_bound DB <- c(rep(1.0, 3), rep(2.0, 3)) q <- c(-1, 1.0, 1.01, 1.5, 1.9) abs(DB[findNN(q, DB)] - q) abs(DB[findNN_(q, DB)] - q) DB <- sort(rnorm(100, mean=100, sd=10)) # should be 0 unique(DB[findNN_(DB,DB)] - DB) q <- rnorm(100, mean=100) idx.NN <- findNN_(q, DB) hist(DB[findNN_(q, DB)] - q) # definition of findNN_ holds i <- 1:5 findNN_(3.5, i) i <- 1:6 findNN_(3.5, i)
The function computes the fragment ions of a peptide sequence, according to the rules of fragmentation in a collision cell. It can be either CID fragmentation (collision-induced dissociation) or ETD (electron transfer dissociation) which are common in proteomics experiments. All are in a positive mode.
If multiple peptide sequences are given it returns a list of fragment ion table.
fragmentIon(sequence, FUN, modified, modification, N_term, C_term)fragmentIon(sequence, FUN, modified, modification, N_term, C_term)
sequence |
peptide sequence encoded as character sequence using the 20 amino acid letters or a double vector of amino acid weights. |
FUN |
the function to be applied to compute further ions.
If no function is assigned |
modified |
a vector of interger containing the varmod id of the peptide position. |
modification |
a double vector of defining the varmod mass in Dalton. |
N_term |
N-Term mass in Dalton. Default is set to 1.007825. |
C_term |
C-Term mass in Dalton. Default is set to 17.002740. |
The fragment ions of a peptide can be computed following the rules proposed in PMID:6525415.
Beside the b and y ions the FUN argument of fragmentIon defines
which ions are computed.
the default ions beeing computed are defined in the function defaultIon.
The are no limits for defining other forms of fragment ions for ETD (c and z ions) CID (b and y ions).
NOTE: for simplicity, we have set a Carbamidomethyl (C) fixed modification with 160.030649 (Cysteine mono mass is 103.00919). The fixed modifications setting are not enabled in the package yet.
If only a vector of amino acids weights is given fragmentIon computes the ions series according to weights.
If this case applies, the function returns only a list having one data frame.
Christian Panse, Bertran Gerrits 2006.
Protein Sequencing and Identification Using Tandem Mass Spectrometry, Michael Kinter and Nicholas E. Sherman, Wiley-Interscience; 1st edition (September 18, 2000)
first used in peakplot.
# Example 1 peptide.AA<-"KINHSFLR"; peptide.AA.weights <- c(128.09496,113.08406,114.04293, 137.05891,87.03203,147.06841,113.08406,156.10111); fragmentIon(peptide.AA); fragmentIon(peptide.AA.weights); HCD_Ion <- function(b, y){ return(cbind(b = b, y = y)) } ETD_Ion <- function(b, y){ Hydrogen <- 1.007825 Oxygen <- 15.994915 Nitrogen <- 14.003074 y_0 <- y - Oxygen - Hydrogen - Hydrogen c <- b + (Nitrogen + (3 * Hydrogen)) z <- y - (Nitrogen + (3 * Hydrogen)) return(cbind(y_0, c, z)) } fragmentIon(peptide.AA, FUN = ETD_Ion) peptides<-c('HTLNQIDSVK', 'ALGGEDVR', 'TPIVGQPSIPGGPVR') pim <- parentIonMass(peptides) fi <- fragmentIon(peptides) (df <- as.data.frame(fi)) op <- par(mfrow=c(3,1)); for (i in 1:length(peptides)){ plot(0, 0, xlab='m/Z', ylab='', xlim=range(c(fi[[i]]$b,fi[[i]]$y)), ylim=c(0,1), type='n', axes=FALSE, sub=paste(peptides[i], "/", pim[i], "Da")); box() axis(1, fi[[i]]$b, round(fi[[i]]$b,1), las=2) axis(1, fi[[i]]$y, round(fi[[i]]$y,1), las=2) pepSeq<-strsplit(peptides[i], "") axis(3,fi[[i]]$b, paste("b", row.names(fi[[i]]),sep=''),las=2) axis(3,fi[[i]]$y, paste("y", row.names(fi[[i]]),sep=''),las=2) text(fi[[i]]$b, rep(0.3, nchar(peptides[i])), pepSeq[[1]],pos=3,cex=4, lwd=4, col="#aaaaaaaa") abline(v=fi[[i]]$b, col='red') abline(v=fi[[i]]$y, col='blue',lwd=2) } par(op) fi <- fragmentIon(c("ATSFYK","XGXFNAGVGK"))[[2]] fi$b[1] + fi$y[9] fi$b[2] + fi$y[8] ION2C <- function(b, y){ Hydrogen <- 1.007825 Oxygen <- 15.994915 Nitrogen <- 14.003074 # yo <- fi$y - Oxygen - Hydrogen - Hydrogen c <- b + (Nitrogen + (3 * Hydrogen)) z <- y - (Nitrogen + (3 * Hydrogen)) # compute doubly charged fragment ions b2 <- (b + Hydrogen) / 2 y2 <- (y + Hydrogen) / 2 return(cbind(b, y, b2 ,y2)) }# Example 1 peptide.AA<-"KINHSFLR"; peptide.AA.weights <- c(128.09496,113.08406,114.04293, 137.05891,87.03203,147.06841,113.08406,156.10111); fragmentIon(peptide.AA); fragmentIon(peptide.AA.weights); HCD_Ion <- function(b, y){ return(cbind(b = b, y = y)) } ETD_Ion <- function(b, y){ Hydrogen <- 1.007825 Oxygen <- 15.994915 Nitrogen <- 14.003074 y_0 <- y - Oxygen - Hydrogen - Hydrogen c <- b + (Nitrogen + (3 * Hydrogen)) z <- y - (Nitrogen + (3 * Hydrogen)) return(cbind(y_0, c, z)) } fragmentIon(peptide.AA, FUN = ETD_Ion) peptides<-c('HTLNQIDSVK', 'ALGGEDVR', 'TPIVGQPSIPGGPVR') pim <- parentIonMass(peptides) fi <- fragmentIon(peptides) (df <- as.data.frame(fi)) op <- par(mfrow=c(3,1)); for (i in 1:length(peptides)){ plot(0, 0, xlab='m/Z', ylab='', xlim=range(c(fi[[i]]$b,fi[[i]]$y)), ylim=c(0,1), type='n', axes=FALSE, sub=paste(peptides[i], "/", pim[i], "Da")); box() axis(1, fi[[i]]$b, round(fi[[i]]$b,1), las=2) axis(1, fi[[i]]$y, round(fi[[i]]$y,1), las=2) pepSeq<-strsplit(peptides[i], "") axis(3,fi[[i]]$b, paste("b", row.names(fi[[i]]),sep=''),las=2) axis(3,fi[[i]]$y, paste("y", row.names(fi[[i]]),sep=''),las=2) text(fi[[i]]$b, rep(0.3, nchar(peptides[i])), pepSeq[[1]],pos=3,cex=4, lwd=4, col="#aaaaaaaa") abline(v=fi[[i]]$b, col='red') abline(v=fi[[i]]$y, col='blue',lwd=2) } par(op) fi <- fragmentIon(c("ATSFYK","XGXFNAGVGK"))[[2]] fi$b[1] + fi$y[9] fi$b[2] + fi$y[8] ION2C <- function(b, y){ Hydrogen <- 1.007825 Oxygen <- 15.994915 Nitrogen <- 14.003074 # yo <- fi$y - Oxygen - Hydrogen - Hydrogen c <- b + (Nitrogen + (3 * Hydrogen)) z <- y - (Nitrogen + (3 * Hydrogen)) # compute doubly charged fragment ions b2 <- (b + Hydrogen) / 2 y2 <- (y + Hydrogen) / 2 return(cbind(b, y, b2 ,y2)) }
This function can be used to screen precursor masses for meaningful mass shifts which could correspond to post translational modifications. We suggest to only use a maxiumum of a handful of mass shifts.
genMod(sequences, modificationPattern, nModification=2)genMod(sequences, modificationPattern, nModification=2)
sequences |
peptide sequences encoded as character sequence using the 20 amino acid letters. |
modificationPattern |
a rbind list structure containing the mono and avg mass as well as the description of a the modifications. |
nModification |
number of maximal modifications. |
t.b.d.
Hubert Rehrauer and Christian Panse 2012
ptm.0<-cbind(AA="-", mono=0.0, avg=0.0, desc="unmodified", unimodAccID=NA) ptm.616<-cbind(AA='S', mono=-27.010899, avg=NA, desc="Substituition", unimodAccID=616) ptm.651<-cbind(AA='N', mono=27.010899, avg=NA, desc="Substituition", unimodAccID=651) m<-as.data.frame(rbind(ptm.0, ptm.616, ptm.651)) genMod(c('TAFDEAIAELDTLNEESYK','TAFDEAIAELDTLSEESYK'), m$AA)ptm.0<-cbind(AA="-", mono=0.0, avg=0.0, desc="unmodified", unimodAccID=NA) ptm.616<-cbind(AA='S', mono=-27.010899, avg=NA, desc="Substituition", unimodAccID=616) ptm.651<-cbind(AA='N', mono=27.010899, avg=NA, desc="Substituition", unimodAccID=651) m<-as.data.frame(rbind(ptm.0, ptm.616, ptm.651)) genMod(c('TAFDEAIAELDTLNEESYK','TAFDEAIAELDTLSEESYK'), m$AA)
HexNAc is intented for demonstration of the PTM_MarkerFinder methode
descriped in [PROTEOMICS pmic.201300036].
It contains the mass pattern (m/z) of
c(126.05495, 138.05495, 144.06552, 168.06552, 186.07608, 204.08665).
A sample containing enriched glycopeptides from yeast was analysed by LC-MS/MS on a LTQ-Orbitrap Velos instrument using sequential HCD and ETD fragmentation techniques. The dataset contains only 11 tandem mass spectra extracted from this experiment. Three pairs of HCD/ETD spectra correspond to peptides carrying N-HexNAc modification identified with high confidence (6 spectra in total). One pair of HCD/ETD spectra corresponds to peptides carrying N-HexNAc modification, identified with very low confidence (2 spectra in total). The remaining 3 spectra are from unmodified peptides.
A data set consists of eleven tandem mass spectra.
Paolo Nanni, Christian Panse, 2013
data(HexNAc) HexNAc[[1]] plot(HexNAc[[1]]$mZ, HexNAc[[1]]$intensity, type='h')data(HexNAc) HexNAc[[1]] plot(HexNAc[[1]]$mZ, HexNAc[[1]]$intensity, type='h')
Insert sample on a given position
insertSamples( S, stdName = "autoQC01", stdPosX = "8", stdPosY = "F", plate = 1, volume = 2, method = "", ... )insertSamples( S, stdName = "autoQC01", stdPosX = "8", stdPosY = "F", plate = 1, volume = 2, method = "", ... )
S |
input |
stdName |
name of the sample |
stdPosX |
x location on the plate |
stdPosY |
y location on the plate |
plate |
number of the plate |
volume |
injection volume |
method |
a path to the method file (optional) |
... |
addition parameter, e.g., |
data.frame
iris[c(1:15,51:65,101:115), ] |> assignPlatePosition() |> insertSamples(howoften=4, begin=FALSE, end=FALSE, stdPosX='6', stdPosY='F', plate=1, stdName = "clean")iris[c(1:15,51:65,101:115), ] |> assignPlatePosition() |> insertSamples(howoften=4, begin=FALSE, end=FALSE, stdPosX='6', stdPosY='F', plate=1, stdName = "clean")
iRTpeptides data are used for genSwathIonLib rt normalization
assuming.
contains a table
Jonas Grossmann and Christian Panse 2013
Using iRT, a normalized retention time for more targeted measurement of peptides. Escher C, Reiter L, MacLean B, Ossola R, Herzog F, Chilton J, MacCoss MJ, Rinner O. Source Proteomics. 2012 Apr;12(8):1111-21. doi: 10.1002/pmic.201100463.
plot(sort(iRTpeptides$rt)) plot(pim<-parentIonMass(as.character(iRTpeptides$peptide)) ~ iRTpeptides$rt)plot(sort(iRTpeptides$rt)) plot(pim<-parentIonMass(as.character(iRTpeptides$peptide)) ~ iRTpeptides$rt)
This dataset contains quantification data from iTRAQ (isobaric tag for relative and absolute quantification) LC-MSMS identified peptides using the software ProteinPilot from ABSciex. The data is generated on a Thermo LTQ-Oribtrap. The mascot generic files are produced with MascotDistiller and searched with ProteinPilot V.4. The peptide summary export is used and filtered towards confident peptides (Confidence above 90) and only columns with Protein accession, peptide sequence, the 8 reporter ion channels (area) are kept as well as the protein description. The rest of the peptide summary is deleted. This data is only a small illustrative part out of the whole data produced. The package design is done in a way, that it is not restricted to ProteinPilot exports but can be used with all other kind of iTRAQ tools, as long as each reporter channel with areas or intensities can be exported. The main point of this function is, to get a hand on what peptides and proteins are used to quantify. Also to be able to not use ratio based quantification but to clearly specify the input and the experimental design.
A data set with approx. 260 rows and 10 variables, 40KBytes file size, derived from biological data from an iTRAQ project..
Jonas Grossmann, Christian Panse, 2012
doi:10.1016/j.jprot.2012.09.011
data(iTRAQ) iTRAQ[1:10,]data(iTRAQ) iTRAQ[1:10,]
The function performes a two group analysis with a t-test for data sets like iTRAQ. Result files are generated where the 2 groups are compared for each protein, while a p-value is calculated. If plot equals TRUE, the boxplots are drawn.
iTRAQ2GroupAnalysis(data, group1, group2, INDEX, FUN=function(x){return(x)}, plot)iTRAQ2GroupAnalysis(data, group1, group2, INDEX, FUN=function(x){return(x)}, plot)
data |
a data set like iTRAQ. |
group1 |
a vector of column ids. |
group2 |
a vector of column ids. |
INDEX |
list of factors, each of same length as |
FUN |
function for doing the data transformation, e.g. log, asinh. |
plot |
boolean. if TRUE boxplots are drawn. |
t.b.d.
Christian Panse, Jonas Grossmann 2011
data(iTRAQ) par(mfrow=c(2,3)) qProt<-iTRAQ2GroupAnalysis(data=iTRAQ, group1=c(3,4,5,6), group2=7:10, INDEX=iTRAQ$prot, plot=TRUE) qProt qPeptide<-iTRAQ2GroupAnalysis(data=iTRAQ, group1=c(3,4,5,6), group2=7:10, INDEX=paste(iTRAQ$prot,iTRAQ$peptide), plot=FALSE)data(iTRAQ) par(mfrow=c(2,3)) qProt<-iTRAQ2GroupAnalysis(data=iTRAQ, group1=c(3,4,5,6), group2=7:10, INDEX=iTRAQ$prot, plot=TRUE) qProt qPeptide<-iTRAQ2GroupAnalysis(data=iTRAQ, group1=c(3,4,5,6), group2=7:10, INDEX=paste(iTRAQ$prot,iTRAQ$peptide), plot=FALSE)
The function graphs a LC-MS map having for all charge states and each single charge state. The rt is on the x axis and the y axis represents the peptide mass.
lcmsmap(data, charges=2:3, score.cutoff = 30, ...)lcmsmap(data, charges=2:3, score.cutoff = 30, ...)
data |
an R data object as it can be obtained by |
charges |
a vector of charge states to be displayed. |
score.cutoff |
a numeric value taken as score cut-off. |
... |
pass arguments to |
Christian Panse and Jonas Grossmann 2013;
idea taken from from the ISB TPP Pep3D view PMID: 15228367
does something with a mascot object using the package's functions,
e.g., peakplot or lcmsmap.
## S3 method for class 'mascot' is(object, class2) ## S3 method for class 'mascot' plot(x, ...) ## S3 method for class 'mascot' as.data.frame(x, ...) ## S3 method for class 'mascot' summary(object, ...) ## S3 method for class 'mascot_query' is(object, class2) ## S3 method for class 'mascot_query' plot(x, obj = NULL, FUN = defaultIon, ...)## S3 method for class 'mascot' is(object, class2) ## S3 method for class 'mascot' plot(x, ...) ## S3 method for class 'mascot' as.data.frame(x, ...) ## S3 method for class 'mascot' summary(object, ...) ## S3 method for class 'mascot_query' is(object, class2) ## S3 method for class 'mascot_query' plot(x, obj = NULL, FUN = defaultIon, ...)
x |
a |
object |
a |
obj |
a |
class2 |
dummy |
... |
arguments will be passwed through. |
FUN |
ion series. |
the object has been generated by using the mascot server command
./export_dat_2.pl $EXPORTOPTIONS file=$DAT.
$EXPORTOPTIONS is defined as "_minpeplen=5 _server_mudpit_switch=0.000000001 _showsubsets=1 _sigthreshold=0.05 do_export=1 export_format=XML group_family=1 pep_calc_mr=1 pep_delta=1 pep_end=1 pep_exp_mr=1 pep_exp_mz=1 pep_exp_z=1 pep_expect=1 pep_isbold=1 pep_isunique=1 pep_miss=1 pep_query=1 pep_rank=1 pep_scan_title=1 pep_score=1 pep_seq=1 pep_start=1 pep_var_mod=1 peptide_master=1 prot_acc=1 prot_cover=1 prot_desc=1 prot_empai=1 prot_hit_num=1 prot_len=1 prot_mass=1 prot_matches=1 prot_pi=1 prot_score=1 prot_seq=1 protein_master=1 query_master=1 query_params=1 query_peaks=1 query_qualifiers=1 query_raw=1 query_title=1 search_master=1 show_format=1 show_header=1 show_masses=1 show_mods=1 show_params=1 show_pep_dupes=1 use_homology=1 user=command line".
$DAT defines the mascot result file.
The output is written as XML file the following command is applied:
XML::xmlToList(XML::xmlParse(xml_file_name)).
xmlParse and xmlToList are function of the XML package.
returns the peakplot return value.
Christian Panse, 2017
http://www.matrixscience.com/mascot_support_v2_6.html
## Not run: # plot the top ten highes scored PSMs par(ask = TRUE) idx <- order(protViz:::.mascot.get.pep_score(S), decreasing = TRUE)[1:10] rv.peakplot <- lapply(S$queries[idx], plot) myAA <- do.call('rbind', lapply(F225712$masses, function(x){ data.frame(letter1=as.character(x$.attrs), mass=as.numeric(x$text)) })) aa2mass("ELVISR", mass=myAA$mass[1:25], letter1 = myAA$letter1[1:25]) ## End(Not run)## Not run: # plot the top ten highes scored PSMs par(ask = TRUE) idx <- order(protViz:::.mascot.get.pep_score(S), decreasing = TRUE)[1:10] rv.peakplot <- lapply(S$queries[idx], plot) myAA <- do.call('rbind', lapply(F225712$masses, function(x){ data.frame(letter1=as.character(x$.attrs), mass=as.numeric(x$text)) })) aa2mass("ELVISR", mass=myAA$mass[1:25], letter1 = myAA$letter1[1:25]) ## End(Not run)
massDeviationPlot computes the mass deviation of a data set having
the list attributes pepmass and peptideSequence.
mdp(data, sub)mdp(data, sub)
data |
an R data object as it can be obtained by |
sub |
a sub title for the plot |
In this version it igrnoes modified peptides.
Christian Panse 2012-2013;
Zubarev R, Mann M. On the proper use of mass accuracy in proteomics. Mol Cell Proteomics. 2007 Mar;6(3):377-81. Epub 2006 Dec 12. PMID: 17164402
# load("F178767.Rdata") # pdf("massError.pdf",12,9) # massDeviationPlot (F178767) # dev.off()# load("F178767.Rdata") # pdf("massError.pdf",12,9) # massDeviationPlot (F178767) # dev.off()
The purpose of msms is for the demonstration of the peptide
identification methods, e.g., peakplot or psm.
msms contains two tandem mass spectra meassured on a linear ion
trap coupled to an Orbitrap mass analyzer
(Thermo Scientific, Bremen, Germany).
The protein sample originates from 10 mouse brain homogenate, prepared in sterile PBS containing protease inhibitors (Complete; Riche, Switzerland) by repeated extrusion through syringe needles of successive smaller size. To remove gross cellular debris the homogenate was centrifuged for 10 min at 2400 rpm at 4 degrees centigrade.
The data were acquired withing the Sixth Framework Programm (FP6-37457). The project was called SysProt.
Dorothea Rutishauser, Jonas Grossmann, Christian Panse 2008
data(msms) peakplot("TAFDEAIAELDTLNEESYK", msms[[1]])data(msms) peakplot("TAFDEAIAELDTLNEESYK", msms[[1]])
It computes the parent ion mass of a vector of peptide sequences. The weights of the amino acids and the C-Term are hard coded in the C function. Important: The precursor mass is calculated with an additional proton, so to speak the singly charged precursor!
parentIonMass(sequence, fixmod, NTerm)parentIonMass(sequence, fixmod, NTerm)
sequence |
peptide sequence encoded as character sequence using the 20 amino acid letters. |
fixmod |
a double vector of length 26 defining the amino acid mass. |
NTerm |
a double value defining the weight of the NTerm. |
The parentIonMass function reqires one argument secuence.
Christian Panse 2006
fetuin<-list( sequence=c('MK', 'SFVLLFCLAQLWGCHSIPLDPVAGYK', 'EPACDDPDTEQAALAAVDYINK', 'HLPR', 'GYK', 'HTLNQIDSVK', 'VWPR', 'RPTGEVYDIEIDTLETTCHVLDPTPLANCSVR', 'QQTQHAVEGDCDIHVLK', 'QDGQFSVLFTK', 'CDSSPDSAEDVR', 'K', 'LCPDCPLLAPLNDSR', 'VVHAVEVALATFNAESNGSYLQLVEISR', 'AQFVPLPVSVSVEFAVAATDCIAK', 'EVVDPTK', 'CNLLAEK', 'QYGFCK', 'GSVIQK', 'ALGGEDVR', 'VTCTLFQTQPVIPQPQPDGAEAEAPSAVPDAAGPTPSAAGPPVASVVVGPSVVAVPLPLHR', 'AHYDLR', 'HTFSGVASVESSSGEAFHVGK', 'TPIVGQPSIPGGPVR', 'LCPGR', 'IR', 'YFK', 'I'), description= "FETUA_BOVIN Alpha-2-HS-glycoprotein ") plot(peptideMass<-parentIonMass(sequence=fetuin$sequence), sub=list(fetuin$description, cex=0.75), main="tryptic digested peptides") AA<-c(71.037114, 114.534940, 160.030649, 115.026943, 129.042593, 147.068414, 57.021464, 137.058912, 113.084064, 0.000000, 128.094963, 113.084064, 131.040485, 114.042927, 0.000000, 97.052764, 128.058578, 156.101111, 87.032028, 101.047679, 150.953630, 99.068414, 186.079313, 111.000000, 163.063329, 128.550590) parentIonMass(c("ELVIS", "ELVISK"), fixmod=AA)fetuin<-list( sequence=c('MK', 'SFVLLFCLAQLWGCHSIPLDPVAGYK', 'EPACDDPDTEQAALAAVDYINK', 'HLPR', 'GYK', 'HTLNQIDSVK', 'VWPR', 'RPTGEVYDIEIDTLETTCHVLDPTPLANCSVR', 'QQTQHAVEGDCDIHVLK', 'QDGQFSVLFTK', 'CDSSPDSAEDVR', 'K', 'LCPDCPLLAPLNDSR', 'VVHAVEVALATFNAESNGSYLQLVEISR', 'AQFVPLPVSVSVEFAVAATDCIAK', 'EVVDPTK', 'CNLLAEK', 'QYGFCK', 'GSVIQK', 'ALGGEDVR', 'VTCTLFQTQPVIPQPQPDGAEAEAPSAVPDAAGPTPSAAGPPVASVVVGPSVVAVPLPLHR', 'AHYDLR', 'HTFSGVASVESSSGEAFHVGK', 'TPIVGQPSIPGGPVR', 'LCPGR', 'IR', 'YFK', 'I'), description= "FETUA_BOVIN Alpha-2-HS-glycoprotein ") plot(peptideMass<-parentIonMass(sequence=fetuin$sequence), sub=list(fetuin$description, cex=0.75), main="tryptic digested peptides") AA<-c(71.037114, 114.534940, 160.030649, 115.026943, 129.042593, 147.068414, 57.021464, 137.058912, 113.084064, 0.000000, 128.094963, 113.084064, 131.040485, 114.042927, 0.000000, 97.052764, 128.058578, 156.101111, 87.032028, 101.047679, 150.953630, 99.068414, 186.079313, 111.000000, 163.063329, 128.550590) parentIonMass(c("ELVIS", "ELVISK"), fixmod=AA)
peakplot labels a mass spectrum from a peptide sequence
assignment by using the psm function with the appropriate
fragment ion labels.
Using peakplot it is possible to compute the fragment ion
coverage and customize the fragmentation ions used for labelling.
peakplot(peptideSequence, spec, FUN=defaultIon, fi=fragmentIon(peptideSequence, FUN=FUN)[[1]], sub=paste(peptideSequence, spec$title, sep=" / "), itol=0.6, pattern.abc="[abc].*", pattern.xyz="[xyz].*", ion.axes=TRUE, ...)peakplot(peptideSequence, spec, FUN=defaultIon, fi=fragmentIon(peptideSequence, FUN=FUN)[[1]], sub=paste(peptideSequence, spec$title, sep=" / "), itol=0.6, pattern.abc="[abc].*", pattern.xyz="[xyz].*", ion.axes=TRUE, ...)
peptideSequence |
peptide sequence encoded as a character sequence using the 20 amino acid letter code. |
spec |
a |
FUN |
the function to be applied to compute further ions. If no
function is assigned |
fi |
fragment ion table, if not specified
|
sub |
a sub title for the plot |
itol |
fragment ion tolerance default is 0.6 Dalton. values below are considered as hit. |
pattern.abc |
|
pattern.xyz |
|
ion.axes |
boolean default is |
... |
passed to the default plot function. |
peakplot computes the in-silico fragment ion, using the
fragmentIon function, and matches them again with
the MS2. If the mass error between the in-silico peak and the measured one
is below 0.6 Da (default setting), the peptide spectrum match
(psm) is considered as a hit.
The major objective of the labelling is the avoidance of overlapping labels, for
which purpose peakplot applies filtering. A label is only drawn if the
corresponding ion count of the m/z peak is higher than a given threshold.
Experience from several hundred annotations shows that the 0.9 percentile is
a good cut-off value. To most efficiently use the limited screen and printout
space and to ensure that labels representing important local peaks are also
considered for the drawing, peakplot divides the display space into a
number of bins depending on the peptide sequence length along the m/z axis.
From these bins, the top n labels are ordered according to abundance. For the
visual clustering effect, the abc and xyz ions are drawn on different y-axis
levels using different colors. Ion types considered for labelling is
dependent on the instrument setting applied during the initial search.
returns a list object containing all the peptide spectrum match relevant information.
Bertran Gerrits, Christian Panse
2006-2017;
PEAKPLOT: Visualizing Fragmented Peptide Mass Spectra in Proteomics (2009) Panse Christian, Gerrits Bertran, Schlapbach Ralph, useR!2009,
abstract: https://www.r-project.org/conferences/useR-2009/abstracts/pdf/Panse+Gerrits+Schlapbach.pdf,
slides: https://www.r-project.org/conferences/useR-2009/slides/Panse+Gerrits+Schlapbach.pdf.
peakplot has been used for generating
figures and supplemental material in:
doi:10.1074/mcp.M600046-MCP200, doi:10.1038/msb4100182, doi:10.1371/journal.pone.0036980, doi:10.1002/pmic.201300036, doi:10.1074/mcp.M115.052548
data(msms) op <- par(mfrow=c(2,1)) peakplot("TAFDEAIAELDTLNEESYK", msms[[1]]) peakplot("TAFDEAIAELDTLSEESYK", msms[[2]]) par(op) # filter cand. fragment ions fi <- fragmentIon("TAFDEAIAELDTLNEESYK") fi.cyz <- as.data.frame(cbind(c=fi[[1]]$c, y=fi[[1]]$y, z=fi[[1]]$z)) p <- peakplot("TAFDEAIAELDTLNEESYK", spec=msms[[1]], fi=fi.cyz, itol=0.6, ion.axes=FALSE) # customizing the ion series ions <- function(b, y){ Hydrogen <- 1.007825 Oxygen <- 15.994915 Nitrogen <- 14.003074 y0 <- fi$y - Oxygen - Hydrogen - Hydrogen c <- b + (Nitrogen + (3 * Hydrogen)) z <- y - (Nitrogen + (3 * Hydrogen)) return(cbind(b, y, c ,z, y0)) }data(msms) op <- par(mfrow=c(2,1)) peakplot("TAFDEAIAELDTLNEESYK", msms[[1]]) peakplot("TAFDEAIAELDTLSEESYK", msms[[2]]) par(op) # filter cand. fragment ions fi <- fragmentIon("TAFDEAIAELDTLNEESYK") fi.cyz <- as.data.frame(cbind(c=fi[[1]]$c, y=fi[[1]]$y, z=fi[[1]]$z)) p <- peakplot("TAFDEAIAELDTLNEESYK", spec=msms[[1]], fi=fi.cyz, itol=0.6, ion.axes=FALSE) # customizing the ion series ions <- function(b, y){ Hydrogen <- 1.007825 Oxygen <- 15.994915 Nitrogen <- 14.003074 y0 <- fi$y - Oxygen - Hydrogen - Hydrogen c <- b + (Nitrogen + (3 * Hydrogen)) z <- y - (Nitrogen + (3 * Hydrogen)) return(cbind(b, y, c ,z, y0)) }
The function reads a CSV file which is exported using ProgenesisLCMS. The object is flexible when it comes to exported fields, ideally, everything is in the CSV. The delimiter is a semicolon (this setting is ProgenesisLCMS version AND system language setting sensitive). All the information is kept and accessible.
There is an option which allows you to switch the separator.
pgImporter(file, sep=';')pgImporter(file, sep=';')
file |
A csv file. |
sep |
The field separator character. default vaule is set to a semicolon. |
This importer is dedicated to the commercial software ProgenesisLCMS. Nevertheless it is has a simple structure and can be adapted and used also for other maps.
todo: Pass an additonal argument to the pgImporter function which allows to define the separator.
Christian Panse, Hubert Rehauer, Jonas Grossmann 2012
The function performes an ANOVA for data sets like pgLFQ.
pgLFQaov(data, groups, names, idx, plot, FUN)pgLFQaov(data, groups, names, idx, plot, FUN)
data |
a data set like |
groups |
a factor. |
names |
a vector of strings for the main attribute of each plot. |
idx |
a integer vector of indice to be processed. |
plot |
logical. If 'TRUE' (non default), a boxplot is drawn. |
FUN |
function for doing the data transformation, e.g. |
The methode performs an oav analysis using the R oav function.
It returns a vector of the ANOVA "Pr(>F)" values in the same order.
Christian Panse, Jonas Grossmann 2012
data(pgLFQprot) par(mfrow=c(4,3)) ANOVA<-pgLFQaov(pgLFQprot$"Normalized abundance", groups=as.factor(pgLFQprot$grouping), names=pgLFQprot$output$Accession, plot=TRUE) ANOVAdata(pgLFQprot) par(mfrow=c(4,3)) ANOVA<-pgLFQaov(pgLFQprot$"Normalized abundance", groups=as.factor(pgLFQprot$grouping), names=pgLFQprot$output$Accession, plot=TRUE) ANOVA
This data set contains a fraction of the most abundant features from 24 LC-MSMS runs measured on an Orbitrap-Velos. The data contains digested proteins from human HeLa cells infected with Shigella bacteria grown on a time course. It is structured in a way, that 6 biological replicates from 4 conditions are measured (Not_infected, Infected_1hr, Infected_2hr, Infected_3hr). The 24 LC-MSMS runs are aligned with each other and a so called mastermap (feature map) is generated where "normalized volumes" on MS1 are extracted for all features in the respective LC-MSMS runs. Some of the features are further annotated with peptide sequences and protein accessions (using the Mascot search algorithm and an ion score cutoff of 25).
We realized, depending on your language and keyboard setting, the separator for the FeatureData as well as for ProteinMeasurements are different (semicolon and commas are used depending on your setting). We assume, that semicolons are and the individual cells are escaped by ". If this differes, we have an option that can be switched in the pgImporter function.
More information on the commercial software can be found here: http://www.nonlinear.com/products/progenesis/lc-ms/overview/.
A data object from ProgenesisImporter.R
Christian Panse, Jonas Grossmann 2012
doi:10.1016/j.jprot.2010.05.011
data(pgLFQfeature) op<-par(mfrow=c(1,1),mar=c(18,18,4,1),cex=0.5) samples<-names(pgLFQfeature$"Normalized abundance") image(cor(asinh(pgLFQfeature$"Normalized abundance")), col=gray(seq(0,1,length=20)), main='pgLFQfeature correlation', axes=FALSE) axis(1,at=seq(from=0, to=1, length.out=length(samples)), labels=samples, las=2) axis(2,at=seq(from=0, to=1, length.out=length(samples)), labels=samples, las=2) par(op) # example assemble (and quantify) all proteins from peptides # using intensities from the master(feature) map # 1239 features pgLFQfeature$peptideInfo$Sequence[1239] pgLFQfeature$peptideInfo$Protein[1239] # conflicts can be produced through: # 1. shared peptides, # 2. mapping to more than one peptide, # 3. rank two above threshold pgLFQfeature$output$Included[1239] pgLFQfeature$"Normalized abundance"[1239,] # tNpq par(mfrow=c(4,3), mar=c(1,1,4,1)) for (i in 1:12) pgLFQtNpq(QuantitativeValue=pgLFQfeature$"Normalized abundance", peptide=pgLFQfeature$peptideInfo$Sequence, protein=pgLFQfeature$peptideInfo$Protein, N=i)data(pgLFQfeature) op<-par(mfrow=c(1,1),mar=c(18,18,4,1),cex=0.5) samples<-names(pgLFQfeature$"Normalized abundance") image(cor(asinh(pgLFQfeature$"Normalized abundance")), col=gray(seq(0,1,length=20)), main='pgLFQfeature correlation', axes=FALSE) axis(1,at=seq(from=0, to=1, length.out=length(samples)), labels=samples, las=2) axis(2,at=seq(from=0, to=1, length.out=length(samples)), labels=samples, las=2) par(op) # example assemble (and quantify) all proteins from peptides # using intensities from the master(feature) map # 1239 features pgLFQfeature$peptideInfo$Sequence[1239] pgLFQfeature$peptideInfo$Protein[1239] # conflicts can be produced through: # 1. shared peptides, # 2. mapping to more than one peptide, # 3. rank two above threshold pgLFQfeature$output$Included[1239] pgLFQfeature$"Normalized abundance"[1239,] # tNpq par(mfrow=c(4,3), mar=c(1,1,4,1)) for (i in 1:12) pgLFQtNpq(QuantitativeValue=pgLFQfeature$"Normalized abundance", peptide=pgLFQfeature$peptideInfo$Sequence, protein=pgLFQfeature$peptideInfo$Protein, N=i)
This data set contain the top 250 most abundant Proteins identified from 24 LC-MSMS runs measured on an Orbitrap-Velos. The original data contains digested proteins from HeLa cells infected with Shigella bacteria grown in a time course. It is structured in a way, that 6 biological replicates from 4 conditions are measured (Not_infected, Infected_1hr, Infected_2hr, Infected_3hr). The 24 LC-MSMS runs are aligned with each other using ProgenesisLCMS. "Normalized volumes" on MS1 are extracted for all features in the respective LC-MSMS runs. The features with an annotation such as peptide sequences and protein accessions (using Mascot search algorithm) are assembled to proteins. Features volumes are stacked (for non conflicting features) to generate the quantitative protein value for each LC-MSMS run. The csv file is an exported Protein data map.
We realized, depending on your language and keyboard setting, the separator for the FeatureData as well as for ProteinMeasurements are different (semicolon and commas are used depending on your setting). We assume, that semicolons are and the individual cells are escaped by ". If this differes, we have an option that can be switched in the pgImporter function.
More information on the commercial software can be found here: http://www.nonlinear.com/products/progenesis/lc-ms/overview/.
A data object from ProgenesisImporter.R
Christian Panse, Jonas Grossmann 2012
doi:10.1016/j.jprot.2010.05.011
data(pgLFQprot) op<-par(mfrow=c(1,1),mar=c(18,18,4,1),cex=0.5) samples<-names(pgLFQprot$"Normalized abundance") image(cor(asinh(pgLFQprot$"Normalized abundance")), main='pgLFQprot correlation', axes=FALSE, col=gray(seq(0,1,length=20))) axis(1,at=seq(from=0, to=1, length.out=length(samples)), labels=samples, las=2) axis(2,at=seq(from=0, to=1, length.out=length(samples)), labels=samples, las=2) par(op)data(pgLFQprot) op<-par(mfrow=c(1,1),mar=c(18,18,4,1),cex=0.5) samples<-names(pgLFQprot$"Normalized abundance") image(cor(asinh(pgLFQprot$"Normalized abundance")), main='pgLFQprot correlation', axes=FALSE, col=gray(seq(0,1,length=20))) axis(1,at=seq(from=0, to=1, length.out=length(samples)), labels=samples, las=2) axis(2,at=seq(from=0, to=1, length.out=length(samples)), labels=samples, las=2) par(op)
This Function implements the recently emerged TopN strategy which uses only the top N intense features for calculating the proteinVolume. This approach should reveal a quantitative protein value, which should make the protein itself comparable within one condition. This allows to estimate protein stochiometries and simplifies modelling and calculations with copy numbers per cell.
pgLFQtNpq(QuantitativeValue, peptide, protein, N=3, plot=TRUE, FUN=asinh)pgLFQtNpq(QuantitativeValue, peptide, protein, N=3, plot=TRUE, FUN=asinh)
QuantitativeValue |
a data set like
|
peptide |
a vector of peptide sequences. |
protein |
a vector of protein information. |
N |
top |
plot |
logical. If 'TRUE' (non default), a boxplot is drawn. |
FUN |
function for doing the data transformation for the
correlation matrix for the image plot, default transformation is
|
The approach has first been described by Silva et al. in 2005 for Waters Q-tof instruments running in the MSe mode. Grossmann et al, showed in 2010 that this approach also works for more widely spread instruments such as Orbitrap-Velos or FTICR instruments from Thermo.
todo: additional columns (or additonal object) for 'protein names' and the total number of features assigned to protein in the master map. The length should be the same as for how many Ns chosen in the assembly method. Double check, if 'empty' protein names.. - basically - not assigned features.. are also in the list! - get rid of it.
Christian Panse, Jonas Grossmann 2012
doi:10.1074/mcp.M500230-MCP200 doi:10.1016/j.jprot.2010.05.011
data(pgLFQfeature) par(mfrow=c(2,4), mar=c(4,4,4,1)) for (i in c(1, 2, 3, 4)){ tNpq<-pgLFQtNpq(QuantitativeValue=pgLFQfeature$"Normalized abundance", peptide=pgLFQfeature$peptideInfo$Sequence, protein=pgLFQfeature$peptideInfo$Protein, N=i) } for (i in c(1, 2, 3, 4)){ tNpq<-pgLFQtNpq(QuantitativeValue=pgLFQfeature$"Normalized abundance", peptide=pgLFQfeature$peptideInfo$Sequence, protein=pgLFQfeature$peptideInfo$Protein, plot=FALSE, N=i) boxplot(t(tNpq), xlab='proteins', ylab='protein value') }data(pgLFQfeature) par(mfrow=c(2,4), mar=c(4,4,4,1)) for (i in c(1, 2, 3, 4)){ tNpq<-pgLFQtNpq(QuantitativeValue=pgLFQfeature$"Normalized abundance", peptide=pgLFQfeature$peptideInfo$Sequence, protein=pgLFQfeature$peptideInfo$Protein, N=i) } for (i in c(1, 2, 3, 4)){ tNpq<-pgLFQtNpq(QuantitativeValue=pgLFQfeature$"Normalized abundance", peptide=pgLFQfeature$peptideInfo$Sequence, protein=pgLFQfeature$peptideInfo$Protein, plot=FALSE, N=i) boxplot(t(tNpq), xlab='proteins', ylab='protein value') }
pressureProfile is a data frame with a variable number of cases (rows) and two variables (columns) named time(min), Pc(psi)
The pressureProfile data set gives the flow profile in nL/min and
the pressure profile in psi as a function of time, respectively, for 24
consecutive injections on an Eksigent NanoLC-Ultra 1D plus system.
The dataset consists of 24 HPLC pressure profiles of 140 min. Data was acquired with a Eksigent NanoLC-Ultra 1D plus system. Samples were separated with a 100 1E-6m ID. column of 150 mm lenght packed with C-18 AQ 200A beads (Dr. Maisch GmbH). The column was heated to a temperature of 50 grad C during the entire run. Acquisition queue was as follows:
- A block of three (3) samples with a gradient length of 140 min was allways followed a autocalibration run with a gradient length of 25 min.
- A total of seven (7) such blocks was acquired to cover the entire dataset.
Christian Trachsel 2012
data(pressureProfile) ppp(pressureProfile[pressureProfile$filename=="F01",]) par(mfrow=c(1,1)) pps.data<-pps(pressureProfile, time=seq(1,140,by=5)) boxplot(Pc~time, data=pps.data, xlab='time [in minutes]', ylab='Pc(psi)') library(lattice) pps.data<-pps(pressureProfile, time=seq(25,40,by=5)) xyplot(Pc ~ as.factor(file) | paste("time =", as.character(time), "minutes"), panel = function(x, y){ m<-sum(y)/length(y) m5<-(max(y)-min(y))*0.05 panel.abline(h=c(m-m5,m,m+m5), col=rep("#ffcccc",3),lwd=c(2,4,2)) panel.grid(h=-1, v=0) panel.xyplot(x, y) }, ylab='Pc [psi]', layout=c(1,4), sub='The three read lines indicate avg plus min 5%.', scales = list(x = list(rot = 45)), data=pps.data)data(pressureProfile) ppp(pressureProfile[pressureProfile$filename=="F01",]) par(mfrow=c(1,1)) pps.data<-pps(pressureProfile, time=seq(1,140,by=5)) boxplot(Pc~time, data=pps.data, xlab='time [in minutes]', ylab='Pc(psi)') library(lattice) pps.data<-pps(pressureProfile, time=seq(25,40,by=5)) xyplot(Pc ~ as.factor(file) | paste("time =", as.character(time), "minutes"), panel = function(x, y){ m<-sum(y)/length(y) m5<-(max(y)-min(y))*0.05 panel.abline(h=c(m-m5,m,m+m5), col=rep("#ffcccc",3),lwd=c(2,4,2)) panel.grid(h=-1, v=0) panel.xyplot(x, y) }, ylab='Pc [psi]', layout=c(1,4), sub='The three read lines indicate avg plus min 5%.', scales = list(x = list(rot = 45)), data=pps.data)
plots the pressure profiles data on a scatter plot Pc versus time grouped by time range.
ppp(data)ppp(data)
data |
A data set like |
This is a useful function to generate an overview over a sequence of measurments run with the same LC gradient. It shows the behaviour of the LC pumps basically the pressures over the whole gradient are plotted to spot sources of problems.
Bernd Roschitzki, Christian Trachsel, Christian Panse 2012
data(pressureProfile) ppp(pressureProfile[pressureProfile$filename=="F12",])data(pressureProfile) ppp(pressureProfile[pressureProfile$filename=="F12",])
The function computes a list of Pc values for the in time provided NN using findNN. It returns file, time, timediff and the Pc valyes as list.
pps(data, time)pps(data, time)
data |
the data set to be plotted. It reqires the following attributes: |
time |
specifies the timeslots to be used. |
The function is useful for levelplots. It can be used to make a quality check over a sequence of experiments. At our site, it is used in conjugtion with Eksigent pumps. These devices store a text file at the instrument pc. These files are preprocessed as specified in the file structure.
Christian Panse 2012
library(lattice) data(pressureProfile) # do the pre processing pps.data<-pps(pressureProfile, time=seq(25,40,by=5)) print(xyplot(Pc ~ as.factor(file) | paste("time =", as.character(time), "minutes"), panel = function(x, y){ m<-sum(y)/length(y) m5<-(max(y)-min(y))*0.05 panel.abline(h=c(m-m5,m,m+m5), col=rep("#ffcccc",3),lwd=c(1,2,1)) panel.grid(h=-1, v=0) panel.xyplot(x, y) }, ylab='Pc [psi]', layout=c(1,4), sub='The three read lines indicate avg plus min 5%.', scales = list(x = list(rot = 45)), data=pps.data))library(lattice) data(pressureProfile) # do the pre processing pps.data<-pps(pressureProfile, time=seq(25,40,by=5)) print(xyplot(Pc ~ as.factor(file) | paste("time =", as.character(time), "minutes"), panel = function(x, y){ m<-sum(y)/length(y) m5<-(max(y)-min(y))*0.05 panel.abline(h=c(m-m5,m,m+m5), col=rep("#ffcccc",3),lwd=c(1,2,1)) panel.grid(h=-1, v=0) panel.xyplot(x, y) }, ylab='Pc [psi]', layout=c(1,4), sub='The three read lines indicate avg plus min 5%.', scales = list(x = list(rot = 45)), data=pps.data))
The function computes a matching between a given peptide sequence and a given tandem mass spectrum (MS2).
psm determines for each fragment ion mass peak the smallest mass error to a peak in the theretical spectrum from the peptide sequence.
If the mass error is below the given fragmentIonError the match is considered a hit.
psm returns a list of computed fragment ions and a vector of the mass error (Da and ppm).
psm uses a generic ANSI-C function to determines the nearest mass peak of array of double
values.
If the plot is set to TRUE an error plot is drawn.
The function psm reqires the arguments sequence and spec. All other arguments are optional.
psm(sequence, spec, FUN=defaultIon, plot=TRUE, fi=fragmentIon(sequence, FUN=FUN)[[1]], fragmentIonError)psm(sequence, spec, FUN=defaultIon, plot=TRUE, fi=fragmentIon(sequence, FUN=FUN)[[1]], fragmentIonError)
sequence |
peptide sequence encoded as character sequence using the 20 amino acid letters. |
spec |
MS2 which is a R list having a sorted mZ vector and an intensity vector of the same size. |
FUN |
this function is passed to the |
plot |
boolean if the error plot function is to be called. |
fi |
fragment ion table, if not specified |
fragmentIonError |
fragment ion error cut-off. default is 0.6 Da. |
This function can be very useful to make assignements to spectra. Moreover it is used for validation or clairifying ambiguities between different sequences assigned to the same spectrum. Additinally it can be used to generate spectral libraries.
returns a psm match.
Christian Panse 2007, 2008, 2009, 2010, 2012, 2017
peakplot and fragmentIon
spec <- list(scans=1138, title="178: (rt=22.3807) [20080816_23_fetuin_160.RAW]", rtinseconds=1342.8402, charge=2, mZ=c(195.139940, 221.211970, 239.251780, 290.221750, 316.300770, 333.300050, 352.258420, 448.384360, 466.348830, 496.207570, 509.565910, 538.458310, 547.253380, 556.173940, 560.358050, 569.122080, 594.435500, 689.536940, 707.624790, 803.509240, 804.528220, 822.528020, 891.631250, 909.544400, 916.631600, 973.702160, 990.594520, 999.430580, 1008.583600, 1017.692500, 1027.605900), intensity=c(931.8, 322.5, 5045, 733.9, 588.8, 9186, 604.6, 1593, 531.8, 520.4, 976.4, 410.5, 2756, 2279, 5819, 2.679e+05, 1267, 1542, 979.2, 9577, 3283, 9441, 1520, 1310, 1.8e+04, 587.5, 2685, 671.7, 3734, 8266, 3309) ) m <- psm('HTLNQIDSVK', spec,plot=TRUE) hist(m$mZ.Da.error) hist(m$mZ.ppm.error)spec <- list(scans=1138, title="178: (rt=22.3807) [20080816_23_fetuin_160.RAW]", rtinseconds=1342.8402, charge=2, mZ=c(195.139940, 221.211970, 239.251780, 290.221750, 316.300770, 333.300050, 352.258420, 448.384360, 466.348830, 496.207570, 509.565910, 538.458310, 547.253380, 556.173940, 560.358050, 569.122080, 594.435500, 689.536940, 707.624790, 803.509240, 804.528220, 822.528020, 891.631250, 909.544400, 916.631600, 973.702160, 990.594520, 999.430580, 1008.583600, 1017.692500, 1027.605900), intensity=c(931.8, 322.5, 5045, 733.9, 588.8, 9186, 604.6, 1593, 531.8, 520.4, 976.4, 410.5, 2756, 2279, 5819, 2.679e+05, 1267, 1542, 979.2, 9577, 3283, 9441, 1520, 1310, 1.8e+04, 587.5, 2685, 671.7, 3734, 8266, 3309) ) m <- psm('HTLNQIDSVK', spec,plot=TRUE) hist(m$mZ.Da.error) hist(m$mZ.ppm.error)
PTM_MarkerFinder is a fucntion to identify and validate spectra from
peptides carrying post-translational modifications.
PTM_MarkerFinder(data, modification, modificationName, mZmarkerIons, minNumberIons=2, itol_ppm=10, minMarkerIntensityRatio=5, mgfFilename=-1, PEAKPLOT=TRUE ) findMz(data, mZmarkerIons, itol_ppm = 10, minNumberIons = 2, minMarkerIntensityRatio = 10) ## S3 method for class 'psmSet' findMz(data, mZmarkerIons, itol_ppm, minNumberIons, minMarkerIntensityRatio) ## S3 method for class 'mascot' findMz(data, mZmarkerIons, itol_ppm = 10, minNumberIons = 2, minMarkerIntensityRatio = 10)PTM_MarkerFinder(data, modification, modificationName, mZmarkerIons, minNumberIons=2, itol_ppm=10, minMarkerIntensityRatio=5, mgfFilename=-1, PEAKPLOT=TRUE ) findMz(data, mZmarkerIons, itol_ppm = 10, minNumberIons = 2, minMarkerIntensityRatio = 10) ## S3 method for class 'psmSet' findMz(data, mZmarkerIons, itol_ppm, minNumberIons, minMarkerIntensityRatio) ## S3 method for class 'mascot' findMz(data, mZmarkerIons, itol_ppm = 10, minNumberIons = 2, minMarkerIntensityRatio = 10)
data |
A list of spectra where each list
element contains a list of |
modification |
A double vector containing the mono mass PTMs. |
modificationName |
A character vector containing the Name of the PTMs. This is the string which will show up in the peptide sequence in square brackets. |
mZmarkerIons |
The m/z patterns which should be searched. |
minNumberIons |
Minimal number of marker ions to be found for further analysis. |
itol_ppm |
The ion tolerance of the marker ions in ppm. default is set to 10ppm. |
minMarkerIntensityRatio |
The marker ions intencity percentage compared to the sum of all peak intensities. |
mgfFilename |
if a mgf(mascot generic file) filename is given,
the function writes an mgf file containing
only the ms2 having the |
PEAKPLOT |
If this boolean is |
The function screens MS2 spectra for the presence of fragment ions specific Post
Translational Modifications (PTMs). The function requires an R-object containing
the mass spectrometric measurement, the peptide assignments, potential
modification information, and a list of marker ions. The R-object can be
retrieved right out of the Mascot Server search result dat files using a perl
script mascotDat2RData.pl which is included in the package's exec/
directory.
The functions iterates over each spectrum of the mass spectrometric measurement and searches for the as input provided marker ions. If a certain number of marker ions (default is, that two marker ions are required) are found and the maker ion intensity ratio is higher than a given threshold, the tandem mass spectrum is considered as HCD scan type and the corresponding ion series are drawn using the protViz:peakplot methode. Furthermore the function is searching for the corresponding ETD scan having the same peptide mass by screening the succeeding scans for ETD spectra. If such a spectrum is found the peptide spectrum assignment containing the c, z, and y ions is drawn. Note that the PTM MarkerFinder expects the ETD scan right after the HCD scan. If the MS protocol changes the PTM MarkerFinder methode has to be adapted.
For each HCD scan PTM MarkerFinder plots both HCD and ETD scans, a ppm error versus maker ion m/z scatter plot, a intensity versus marker ion m/z plot, and two pie charts where the relative and absolute maker ion intensity are shown.
As a summery report the function returns a table containing the following column attributes: "scans", "mZ", "markerIonMZ", "markerIonIntensity", "markerIonMzError", "markerIonPpmError", and "query" which can be used for statistics.
Furthermore, if mgfFilename is defined, a Mascot Generic File
(MGF) is created containig the HCD scans (having the marker ions) and the corresponding
ETD scans.
Paolo Nanni, Peter Gehrig, Christian Panse 2011-2013;
Nanni P, Panse C, Gehrig P, Mueller S, Grossmann J, Schlapbach R.(2013), PTM MarkerFinder, a software tool to detect and validate spectra from peptides carrying post-translational modifications. Proteomics. 2013 Aug;13(15):2251-5. doi:10.1002/pmic.201300036.
ADP_Ribose <- c(136.0618, 250.0935, 348.0704, 428.0367) marker ions have been used in:
Bilan V, Leutert M, Nanni P, Panse C, Hottiger MO.
Combining Higher-Energy Collision Dissociation and Electron-Transfer/Higher-Energy Collision Dissociation Fragmentation in a Product-Dependent Manner Confidently Assigns Proteomewide ADP-Ribose Acceptor Sites,
Anal. Chem., 2017, 89 (3), pp 1523-1530
doi:10.1021/acs.analchem.6b03365.
# some marker ions Glykan_MarkerIons <- c(109.02841, 127.03897, 145.04954, 163.06010, 325.11292) HexNAc_MarkerIons <- c(126.05495, 138.05495, 144.06552, 168.06552, 186.07608, 204.08665) # DOI: 10.1021/acs.analchem.6b03365 # Anal Chem 2017 Feb 13;89(3):1523-1530. Epub 2017 Jan 13. ADP_Ribose <- c(136.0618, 250.0935, 348.0704, 428.0367) data(HexNAc) # prepare modification ptm.0 <- cbind(AA="-", mono=0.0, avg=0.0, desc="unmodified", unimodAccID=NA) ptm.1 <- cbind(AA='N', mono=317.122300, avg=NA, desc="HexNAc", unimodAccID=2) ptm.2 <- cbind(AA='M', mono=147.035400, avg=NA, desc="Oxidation", unimodAccID=1) m <- as.data.frame(rbind(ptm.0, ptm.1, ptm.2), stringsAsFactors = TRUE) S <- PTM_MarkerFinder(data=HexNAc, modification=m$mono, modificationName=m$desc, minMarkerIntensityRatio=3, itol_ppm=20, mZmarkerIons=HexNAc_MarkerIons) boxplot(markerIonIntensity ~ markerIonMZ, data=S, log='y', main='Summary plot: boxplot of marker ion intensities from all pPTM spectra', xlab='markerIon m/z', ylab='log10 based marker ion intensity') # export w <- reshape(S[,c(1,7,3,4)], direction='wide', timevar="markerIonMZ", idvar=c('scans','query')) write.table(w, file=file.path(tempdir(), "HexNAc_PTM_markerFinder.csv"), sep=',', row.names=FALSE, col.names=TRUE, quote=FALSE)# some marker ions Glykan_MarkerIons <- c(109.02841, 127.03897, 145.04954, 163.06010, 325.11292) HexNAc_MarkerIons <- c(126.05495, 138.05495, 144.06552, 168.06552, 186.07608, 204.08665) # DOI: 10.1021/acs.analchem.6b03365 # Anal Chem 2017 Feb 13;89(3):1523-1530. Epub 2017 Jan 13. ADP_Ribose <- c(136.0618, 250.0935, 348.0704, 428.0367) data(HexNAc) # prepare modification ptm.0 <- cbind(AA="-", mono=0.0, avg=0.0, desc="unmodified", unimodAccID=NA) ptm.1 <- cbind(AA='N', mono=317.122300, avg=NA, desc="HexNAc", unimodAccID=2) ptm.2 <- cbind(AA='M', mono=147.035400, avg=NA, desc="Oxidation", unimodAccID=1) m <- as.data.frame(rbind(ptm.0, ptm.1, ptm.2), stringsAsFactors = TRUE) S <- PTM_MarkerFinder(data=HexNAc, modification=m$mono, modificationName=m$desc, minMarkerIntensityRatio=3, itol_ppm=20, mZmarkerIons=HexNAc_MarkerIons) boxplot(markerIonIntensity ~ markerIonMZ, data=S, log='y', main='Summary plot: boxplot of marker ion intensities from all pPTM spectra', xlab='markerIon m/z', ylab='log10 based marker ion intensity') # export w <- reshape(S[,c(1,7,3,4)], direction='wide', timevar="markerIonMZ", idvar=c('scans','query')) write.table(w, file=file.path(tempdir(), "HexNAc_PTM_markerFinder.csv"), sep=',', row.names=FALSE, col.names=TRUE, quote=FALSE)
PTM_MarkerFinder_util is a utility function for PTM_MarkerFinder.
PTM_MarkerFinder_util(dataFileName, mZmarkerIons, minMarkerIntensityRatio, minNumberIons, itol_ppm, write_csv)PTM_MarkerFinder_util(dataFileName, mZmarkerIons, minMarkerIntensityRatio, minNumberIons, itol_ppm, write_csv)
dataFileName |
RData file name without 'RData' ending. |
mZmarkerIons |
A double vector of the m/z patterns which should be searched. |
minMarkerIntensityRatio |
The marker ions intencity percentage compared to the sum of all peak intensities. |
minNumberIons |
Minimal number of marker ions to be found for further analysis. |
itol_ppm |
The ion tolerance of the marker ions in ppm. default is set to 10ppm. |
write_csv |
boolean. |
The function plots summeries in form of boxplots pie charts and scatter plots of the found marker ions.
Paolo Nanni, Christian Panse 2012-2013;
This function returns as output one hydrophobicity value for a given sequence of amino acids (tryptic peptide) which can be used to predict the retention times. The calculation is based on the method described in PMID:15238601.
ssrc(x, H = list())ssrc(x, H = list())
x |
sequence of amino acids, e.g., |
H |
A list of retention coefficients. The default is set to the values of PMID:15238601 table II column 2(Rc values). |
Christian Panse, Christian Trachsel 2015
Krokhin, O. V. et al. An improved model for prediction of retention times of tryptic peptides in ion pair reversed-phase HPLC: its application to protein peptide mapping by off-line HPLC-MALDI MS. Mol. Cell Proteomics 3, 908-919 (2004). doi:10.1074/mcp.M400031-MCP200
# example of table iv [PMID:15238601] lapply(c("SCHTAVGR", "SCHTGLGR", "EDLIAYLK"), ssrc) plot(sapply(as.character(iRTpeptides$peptide), ssrc) ~ iRTpeptides$rt)# example of table iv [PMID:15238601] lapply(c("SCHTAVGR", "SCHTGLGR", "EDLIAYLK"), ssrc) plot(sapply(as.character(iRTpeptides$peptide), ssrc) ~ iRTpeptides$rt)