Make Named Character Vector Of Sequential C-Terminal Fragments

Description

Make named character vector of sequential C-terminal fragments.

Usage

.CtermPepCut(
  pe,
  mi,
  se1 = ".",
  se2 = "-",
  mainName = NULL,
  indexOffs = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a numeric vector with mass(es) and sequence in name(s)

See Also

more flexible/sophisticated see .termPepCut; makeFragments; convAASeq2mass

Examples

## Ubiquitin example
P0CG48 <- "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"  
cut1 <- .CtermPepCut(P0CG48, mi=3, mainName="P0CG48")
head(cut1); tail(cut1)

Make Named Character Vector Of Sequential C-Terminal Fragments

Description

Make named character vector of sequential C-terminal fragments.

Usage

.NtermPepCut(
  pe,
  mi,
  se1 = ".",
  se2 = "-",
  mainName = NULL,
  sepNC = FALSE,
  indexOffs = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a numeric vector with mass(es) and sequence in name(s)

See Also

more flexible/sophisticated see .termPepCut; makeFragments; convAASeq2mass

Examples

## Ubiquitin example
P0CG48 <- "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"  
cut1 <- .CtermPepCut(P0CG48, mi=3, mainName="P0CG48")
head(cut1); tail(cut1)
#' @export

Check Column Names from Matrix Or data.frame

Description

Check matrix or data.frame for containing columns with specified name and optionally remove other columns (by their names)

Usage

.chColNa(x, colNa = "index", rmCol = NULL, callFrom = NULL)

Arguments

Value

This function returns a matrix or data.frame with adjusted columns

See Also

scoreFragments

Examples

ma1 <- matrix(1:6, nrow=2, dimnames=list(NULL, c("index","aa","bb")))
.chColNa(ma1)
.chColNa(ma1, colNa="zz", rmCol="aa") 

Cite Charge Catching Amino-Acids

Description

Return a matrix with charge-catching amino-acids and their assumed strength. So far, the strength shown/used is set rather empirically.

Usage

.chargeCatchingAA(
  chargeMode = "pos",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix with charge-catching amino-acids and their assumed strength

See Also

fragmentSeq

Examples

.chargeCatchingAA()

Check Modification Type

Description

Check Modification Type

Usage

.checkModTy(
  modTy,
  knownMods,
  phoDePho = c("p", "q"),
  modTyGr = c("basMod", "varMod"),
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns the corrected list of mixed of variable and fixed modifications ($basMod, $varMod and $varMo2)

See Also

AAfragSettings

Examples

modTy1 <- list(basMod=c("b","y","h"),varMod=c("p","o","q"))
.checkModTy(modTy1, knownMods=c("a","b","h","o","p","q","y"))

Count Letters

Description

Count how many time a given letter occurs in each element of 'seq'

Usage

.countLET(sequ, countCh = "K", silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a numeric vector of counts for 'countCh' (single element !) in each element of 'seq'

See Also

AAfragSettings, makeFragments

Examples

protP2 <- c(mesp="MESPEPTIDES", pepe="PEPEPEP")
.countLET(protP2,"P")

Count For All Proteins The Occurance Of Modification Types

Description

Count for all protein 'sequ' the occurance of modification types defined in list 'modTyp' (only if in names(specAAMod)).

Usage

.countModif(
  sequ,
  modTyp,
  specAAMod,
  knownMods,
  detailedCount = FALSE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a list of matrixes $cou and $combTerm, with number of modifications per peptides (line in 'pepTab') for basMod, varMod & varMo2

See Also

AAfragSettings, makeFragments

Examples

protP2 <- c(mesp="MESPEPTIDES", pepe="PEPEPEP")
pepTab1 <- makeFragments(protTab=protP2, minFra=6, internFr=TRUE, massTy="mono")
modTy2 <- list(basMod=c("b","y","h"), varMod=c("x","p","o","q","e","j"))
.countModif(pepTab1[,2], modTyp=c("b","y"), specAAMod=AAfragSettings(outTy="all")$specAAMod, 
  knownMods=AAfragSettings(outTy="all")$knownMods)

Evaluate Selected Lines Of PepTab

Description

Evaluate selected lines of pepTab of SAME AA-length AND iso-mass for preferential cutting sites.

Usage

.evalIsoFra(
  x,
  prefFragPat = NULL,
  seqCol = "seq",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns line ID-numbers (pepTab[,"no"]) for those below median score (ie to remove from pepTab)

See Also

makeFragments

Examples

peTab <- matrix(c("9","13","14","15", "LPVIAGHEAAG","PVIAGHEAAGI","EKKPFSI","KKPFSIE", 
  "P","L","E","E", "I","V","E","E"),nr=4,dimnames=list(NULL,c("no","seq","precAA","tailAA")))
.evalIsoFra(peTab)

Reorganize List Of Peptide Fragments To Matrix

Description

This function allows reorganiziong a list (of lists) of peotide fragments into matrix

Usage

.exNamesTyDeList(
  x,
  subLiNames = c("full", "Nter", "Cter", "inter"),
  inclNo = TRUE,
  fullSeq = NULL,
  outCol = c("seq", "orig", "origNa", "ty", "seqNa", "beg", "end", "precAA", "tailAA",
    "ambig", "mass"),
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

This function returns matrix with fragment sequence, mass, start- and end-position, heading and tailing AA (or NA if terminal fragment)

See Also

makeFragments; evalIsoFragm, from package wrProteo convAASeq2mass, AAmass, massDeFormula

Examples

prot1 <- c(protP="KEPTIDE", pro2="MPRATE")
## fragment all target proteins
pep3 <- lapply(prot1, fragmentSeq, minSize=3, maxSize=5, internFragments=FALSE, 
  separTerm=TRUE, keepRedSeqs=TRUE)
pepTab <- .exNamesTyDeList(pep3, fullSeq=prot1) 

Multiply Values Of Matrix By Its Colnames And Sum By Row

Description

This function allows multiplying values of 'mat' by its colnames and (optionally) summing along rows.

Usage

.multMatByColNa(
  mat,
  sumByRow = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This functions returns a numeric vector or a matrix if sumByRow=FALSE

See Also

convToNum

Examples

mat1 <- 3 + matrix(1:4, ncol=2, dimnames=list(letters[1:2], c("3","2")))
.multMatByColNa(mat1)
.multMatByColNa(mat1, sumByRow=FALSE)

Multiprocessor Version For Full Combinatorial And Cumulative Values

Description

This function combines all variants and sums them

Usage

.parCombinateAllAndSum(
  uniqCo,
  massModV,
  nProc = NULL,
  firstOfRepeated = NULL,
  parRegDefault = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

This function requires the packages 'parallel' and 'BiocParallel' (from Bioconductor) Note : The function may work only on some Windows systems or may give warnings on Windows

Value

This functions returns a list with single and combined mass-modifications (PTMs) for each peptide

See Also

convToNum

Examples

uniqCo <- matrix(c(1,1,1,0,1,1), nrow=2, dimnames=list(c("PTI","KPE"),c("d","p","h"))  )
massModV <- c(d=-18.01056, p=79.96633, h=-18.01056)
chPa <- c(requireNamespace("parallel", quietly=TRUE), 
  requireNamespace("BiocParallel", quietly=TRUE), "windows" %in% .Platform$OS.type)
## Note : the function may work only on some windows systems
if(all(chPa)) if(parallel::detectCores() >1) {
  .parCombinateAllAndSum(uniqCo, massModV, nProc=2)}

Return data.frame with pattern of perferential fragmentation sites

Description

Return data.frame with pattern of perferential fragmentation sites Here a simplified version (elaborate see Kelleher group: Haverland 2017, J Am Soc Mass Spectrom)

Usage

.prefFragPattern(silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a data.frame with pattern of perferential fragmentation sites

See Also

makeFragments

Examples

peTab <- matrix(c("9","13","14","15", "LPVIAGHEAAG","PVIAGHEAAGI","EKKPFSI","KKPFSIE", 
  "P","L","E","E", "I","V","E","E"),nr=4,dimnames=list(NULL,c("no","seq","precAA","tailAA")))
head(.prefFragPattern())

Add Single Specific Modifications

Description

Add single specific modification to peptide/protein fragments .

Usage

.singleSpecModif(
  pepTab,
  specModif,
  nMaxMod = 1,
  massTy = "mono",
  callFrom = NULL,
  silent = FALSE,
  debug = FALSE
)

Arguments

Value

This function returns a list with $massMatch (list of exerimental peptides matching to one or more predicted), $preMa (predicted ions, including fixed modif), $pepTab (predicted neutral peptides, wo modifications), $expMa (experimental mass from input), $recalibFact (recalibration factor as from input), $docTi (time for calculations)

See Also

makeFragments, identifVarModif, identifyPepFragments

Examples

pep1 <- c(pe1="KPEPTI")
# The table of possible terminal fragments (for simplicity terminal only)
pepTab1 <- makeFragments(pep1, min=3, max=7, internFra=FALSE)
specModif1 <- list(modOrigin=pep1, modPos=1, modMass=579.9663, modName="p", modFixed=FALSE)
.singleSpecModif(pepTab1, specModif1 )

protP <- c(protP="PEPTIDEKR")
pep1 <- c("PTI","KPE","EPTI")
papTab1 <- cbind(no=c(7,2,6),seq=pep1, orig=rep("KPEPTI",3), origNa=rep("pe1",3), 
  ty=paste0(c("C","N","C"),"ter"), beg=c(4,1,3), end=c(6,3,4),
  mass= wrProteo::convAASeq2mass(pep1, massTy="mono"), modSpec="")

Make Named Character Vector Of Sequential Terminal Fragments

Description

Make named character vector of sequential terminal fragments

Usage

.termPepCut(
  pe,
  mi,
  ma = 1000,
  se1 = ".",
  se2 = "-",
  mainName = NULL,
  sepNC = FALSE,
  indexOffs = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a numeric vector with mass(es) and sequence in name(s)

See Also

makeFragments; convAASeq2mass

Examples

## Ubiquitin example
P0CG48 <- "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"  
.termPepCut(P0CG48, mi=3, ma=12 ,sepNC=TRUE, mainName="P0CG48")

Settings For AA Fragmentation

Description

This function provides basic settings for what types of fragments may accomodate which type of modifications : $knownMods: information about which modifications may be considered, $specAAMod: specifc AA sites (if applicable), $specAAMod: specifc AA sites (if applicable). For example, here 'p' codes for gain of mass for HPO3 only at S, T and Y residues. Note: $knownMods$Nterm and $knownMods$Cterm are treated as mutually exclusive

Usage

AAfragSettings(outTy = "all", silent = FALSE, debug = FALSE, callFrom = NULL)

Arguments

Value

This function returns a list ($knownMods, $knspecAAMods, $modChem, $neutralLossOrGain)

See Also

makeFragments, fragmentSeq, massDeFormula

Examples

AAfragSettings()

Add Modifications To Peptide Mass

Description

Adjust/add mass for modifications from 'modTy' to all peptides in 'pepTab' based on count 'cou' of occurances of modifications : Either fixed or variable modifications will be added to the mass of initial peptides from argument papTab.

Usage

addMassModif(
  cou,
  pepTab,
  combTerm,
  modTy,
  lastIndex = NULL,
  modChem = NULL,
  basVarMod = "basMod",
  massTy = "mono",
  knownMods = NULL,
  nProc = 1,
  parallDefault = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

Terminal ionization (like 'b' or 'y' -fragments) is treated as fixed modification and the resulting masses will correspond to standard mono-protonated ions. Since variable and fixed modification types can't be run in a single instance, the function has to get calles twice, it is recommended to always start with the fixed modfications, In the case of fixed modifications (like defining 'b' or 'y' fragments) neutral peptide masses should be given to add the corresponding mass-shift (and to obtain mono-protonated ions). In case of variable modifications (like 'd' or 'p'), the corresponding ions from the fixed modifications should get furnished to add the corresponding mass-shift, the masses resulting from the initial fixed modifications run can be used. Note, that transforming a neutral precursor M into MH+ is also considered a modification. The results are also correct with obligatory fragments that can't occur the same time (eg x & y ions can't be same time, need to make add'l lines...). This function has a multiprocessor mode, with small data-sets (like the toy example below) there is typcally no gain in performance.

Value

This functions returns a list containing $pepTab (table of peptide as single charge positive ions), $abc ('representative' list of all combinations to add). Main result in $pepTab

See Also

convToNum

Examples

pep1 <- c(pe1="KPEPTI")
# The table of possible terminal fragments (for simplicity terminal only)
pepTab1 <- makeFragments(pep1, min=3, max=7, internFra=FALSE)
# Which fragment may be subject to how many modification (including ionization by H+)
cou1 <- countPotModifAAs(pepTab=pepTab1, modTy=list(basMod=c("b","y")))
# Add modifications (here: ionize all pepptides by H+)
preMa1 <- addMassModif(cou=cou1$cou, pepTab=pepTab1, combTerm=cou1$combTerm, 
  modTy=list(basMod=c("b","y")), basVarMod="basMod")
preMa1

## Example including variable modifications
modT3 <- list(basMod=c("b","y"),varMod=c("p","h","d"))
cou3 <- countPotModifAAs(pepTab=pepTab1, modTy=modT3)
## Now we re-use/inject the results for the fixed modificatons
preMa3 <- addMassModif(cou=cou3$cou, pepTab=preMa1$pepTab, combTerm=cou1$combTerm, 
  modTy=modT3, basVarMod="varMod")
head(preMa3$pepTab,12)

Check & complete mixed of variable and fixed modifications

Description

Check & complete settings for mixed of variable and fixed modifications. The final format is a list with $basMod, $varMod and $varMo2

Usage

checkModTy(
  modTy,
  knownMods = NULL,
  silent = TRUE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns the corrected list of mixed of variable and fixed modifications ($basMod, $varMod and $varMo2)

See Also

AAfragSettings

Examples

modTy1 <- list(basMod=c("b","y","h"),varMod=c("p","o","q"))
checkModTy(modTy1)

Planing For Making All Multiplicative Combinations

Description

Provide all combinations for each of n elements of vector 'nMax' (positive integer, eg number of max multiplicative value). Results allow to see possible total compositons and must be read vertically.

Usage

combinatIntTable(
  nMax,
  include0 = TRUE,
  asList = FALSE,
  callFrom = NULL,
  debug = FALSE,
  silent = TRUE
)

Arguments

Value

This functions returns a list or array (as 2- or 3 dim) with possible number of occurances for each of the 3 elements in nMax. Read results vertical : out[[1]] or out[,,1] .. (multiplicative) table for 1st element of nMax; out[,,2] .. for 2nd

See Also

combinateAllAndSum

Examples

combinatIntTable(c(1,1,1,2), include0=TRUE, asList=FALSE, silent=TRUE)
nMa <- c(Paris=2,Lyon=1,Strasbourg=1)
combinatIntTable(nMa, include0=TRUE, asList=TRUE, silent=TRUE) 

Full Combinatorial And Cumulative Values

Description

Use this function for preparing all combinations of non-compulsatory, ie variable, mass modifications. Variable modifications may or may not be present. Thus, for a given amino-acid with a variable modification two versions of the molecular weight need to be considered.

Usage

combinateAllAndSum(
  nMax,
  modVal,
  notSingle = NULL,
  silent = TRUE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

Most (variable) modifications are linked to a type of amino acid, like serine- or thyrosine residues for phosphorlylation. Thus in this case, each instance of the amino acids S or T may or may not be modified. So, for example if there are 2 serines on a given peptide/protein, 0, 1 or 2 phosphorylation modifications may be present. For this reason there is an argument called nMax to allow staying within biologically relevant ranges (external knowledge) and allowing to reduce complexity significantly. In the case of phosporylations, the total number of actually phosphoylated amino-acids is typically way below the number of S and T residues in pthe initial sequence. Some modifications are exclusive to others, argument notSingle : An (artificially occuring) de-phosphorylation event during fragmentation can only happen if the amino acid was already phosphorylated in the first place.

Value

This functions returns a named (concatenated names of modVal) numeric vector

See Also

convToNum

Examples

uniqCo <- matrix(c(1,1,1,0,1,1), nrow=2, dimnames=list(c("PTI","KPE"),c("d","p","h"))  )
massModV <- c(d= -18.01056, p= 79.96633, h= -18.01056)
## for 1st peptide
combinateAllAndSum(uniqCo[1,], massModV, notSingle=c("q","p"))
## for all peptides
apply(uniqCo, 1, combinateAllAndSum, massModV, notSingle=c("q","p"))

Corrective Values For Random Sequences For In/Dels

Description

The protein sequence and composition of most proteomes cannot be well mimicked by pure random sequences. Thus, random sequences (for comparing the quality of fitting) shhould be corrected accordingly, this vector provides help to do so. This function loads corMutShift- or corInDelShift-values from RData. The vector contains possible mass alterations for random drawing either by mutating a given aminoacid (corMutShift), or by making or in/del changes (corInDelShift). These values are based on simulations in the human proteome (from UniProt).

Usage

corInDelShift(fi = NULL)

Arguments

Value

This functions returns a numeric vector (1907 possible mass alterations for random drawing)

See Also

corMutShift, randMassByStochastic

Examples

corMutShift <- corMutShift()
str(corMutShift)

corInDelShift <- corInDelShift()
str(corInDelShift)

Corrective Values For Random Sequences For Mutations

Description

The protein sequence and composition of most proteomes cannot be well mimicked by pure random sequences. Thus, random sequences (for comparing the quality of fitting) shhould be corrected accordingly, this vector provides help to do so. This function loads corMutShift- or corInDelShift-values from RData. The vector contains possible mass alterations for random drawing either by mutating a given aminoacid (corMutShift), or by making or in/del changes (corInDelShift). These values are based on simulations in the human proteome (from UniProt).

Usage

corMutShift(fi = NULL)

Arguments

Value

This functions returns a numeric vector (possible mass alterations for random drawing)

See Also

corInDelShift, randMassByStochastic

Examples

corMutShift <- corMutShift()
str(corMutShift)

corInDelShift <- corInDelShift()
str(corInDelShift)

Identify Children/Parent Settings As a+b=c

Description

This functions helps identifying fragments ('parent') characterized by a start- and end-position, that got split into 2 'children' fragments. So, each one of the new 'children' conserves either the start- or end-site of the parent and the the remaining ends are on consecutive positions. For example if the sequence 'BCDEFG' (parent) gets split into 'BCD' (positions 1-3) and 'EFG' (positions 4-6), this will be identified as a children/parent 'family' which could be represented as 'a+b=c' case. Note : At this point only settings with 2 children are considered, for more complex scenarions one may build trees using buildTree (however, this function does not identify 'parents'). In proteomics-applications some start- and end-sites may occur multiple times, representing eg unmodified and modified versions of the same basal peptide-sequence. Such duplicated start- and end-cases are handeled as allowed, a 'child' (characterized by its start- and end-position) may occur multiple times, and the corresponding redundant rownames (eg peptide sequence like 'BCD') will be conserved. However, information reflecting eg different peptide modifications must be stored separately. If redudant start- and end-sites accur with different row-names, repeated start- and end-sites will display NA.

Usage

countChildrenParent(
  fragments,
  output = "count",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This functions returns either numeric vector with cumulated counts (corresponding to rows of fragments) or list with $count and $detailIndex (list with indexes refering to non-redundant entries of all a+b=c settings identified)

See Also

simpleFragFig ; for building longer consecutive trees (without identification of 'parent') buildTree

Examples

frag3 <- cbind(beg=c(4,2,3,7,13,13,15, 2,9,2,9), end=c(14,6,12,8,18,20,20, 8,12,12,18)) 
rownames(frag3) <- c("K","A","E","B","C","D","F", "H","G","I","J")
countChildrenParent(frag3)
## example with duplicate start- and end-position positions
frag3c <- cbind(beg=c(4,2,3,7, 7,13, 13,13,15, 2,9,2,9,9),
  end=c(14,6,12,8, 8,18, 18,20,20, 8,12,12,12,18))
rownames(frag3c) <- c("K","A","E", "B","B", "C","C","D","F", "H","G","I","G","J")
countChildrenParent(frag3c, out="det")

Make Table With Counts of Potential Modification Sites

Description

Makes table 'cou' with counts of (potential) modification sites based on column 'seq' in matrix 'pepTab'. Note: if multiple N-or C-term modifs, then only the first is shown in resulting table 'cou'.

Usage

countPotModifAAs(
  pepTab,
  modTy,
  maxMod = c(p = 3, h = 1, k = 1, o = 1, m = 1, n = 1, u = 1, r = 1, s = 1),
  specAAMod = NULL,
  knownMods = NULL,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

list of matrixes $cou and $combTerm, with number of modifications per peptides (line in 'pepTab') for basMod, varMod & varMo2

See Also

AAfragSettings, makeFragments

Examples

protP2 <- c(mesp="MESPEPTIDES", pepe="PEPEPEP")
pepTab1 <- makeFragments(protTab=protP2, minFra=6, internFr=TRUE, massTy="mono")
cou1 <- countPotModifAAs(pepTab=pepTab1, modTy=list(basMod=c("b","y"),
  varMod=c("p","h")))
modTy2 <- list(basMod=c("b","y","h"), varMod=c("x","p","o","q","e","j"))
cou2 <- countPotModifAAs(pepTab=pepTab1, modTy=modTy2)

Evaluate Selected Lines Of PepTab (iso-mass) For Preferential Cutting Sites

Description

Evaluate selected lines of pepTab (iso-mass) for preferential cutting sites. Such sites are taken by default from .prefFragPattern() simplified from a publication by the Kelleher group (Haverland 2017, J Am Soc Mass Spectrom) or can be furnished by the user.

Usage

evalIsoFragm(
  z,
  prefFragPat = NULL,
  seqCol = "seq",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns line ID-numbers (pepTab[,"no"]) for those below median score (ie to remove from pepTab) or NULL if nothing to remove due to preferential fragmentation

See Also

makeFragments

Examples

peTab <- matrix(c("9","13","14","15", "LPVIAGHEAAG","PVIAGHEAAGI","EKKPFSI","KKPFSIE", 
  "P","L","E","E", "I","V","E","E"),nr=4,dimnames=list(NULL,c("no","seq","precAA","tailAA")))
evalIsoFragm(peTab)

Fragment Protein Or Peptide Sequence

Description

Makes internal/terminal fragments of a SINGLE peptide/protein input (as single letter amino-acid code) and returns list of all possible sequences ($full, $Nter, $Cter, $inter).

Usage

fragmentSeq(
  sequ,
  minSize = 3,
  maxSize = 300,
  internFragments = TRUE,
  separTerm = FALSE,
  keepRedSeqs = TRUE,
  prefName = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

Note: Thus function can handle only 1 sequence at each run ! Note: The mass values returned correspond to neutral peptides. If you are looking for ions, you need to adjust masses to the repsective ion-charcteriostics (adding H+, etc).

Value

This function returns a numeric vector with the (neutral) mass of fragmented peptides

See Also

makeFragments; convAASeq2mass

Examples

fragmentSeq("ABCDE")
fragmentSeq("ABCDE", minSize=3, internFragments=FALSE)
fragmentSeq("ABCDE", minSize=3, internFragments=TRUE)

## Run multiple peptides/proteins
twoPep <- cbind(c("a","ABCABCA"), c("e","EFGEFGEF"))
apply(twoPep, 2, function(x) fragmentSeq(x[2], mi=3, kee=FALSE, sep=TRUE, pre=x[1]))

## Ubiquitin example
P0CG48 <- "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"  
system.time( fra1 <- (fragmentSeq(P0CG48, mi=5, kee=FALSE)))      # < 0.5 sec  

Identify Fixed Modifications

Description

Identify peptide/protein fragments based on experimental m/z values 'expMass' for given range of aa-length. Internally all possible fragments will be predicted and their mass compared to the experimental values (argument expMass).

Usage

identifFixedModif(
  prot,
  expMass,
  minFragSize = 5,
  maxFragSize = 60,
  indexStart = 1,
  suplPepTab = NULL,
  internFra = TRUE,
  chargeCatchFilter = TRUE,
  maxMod = c(p = 3, h = 1, k = 1, o = 1, m = 1, n = 1, u = 1, r = 1, s = 1),
  modTy = NULL,
  specModif = NULL,
  knownMods = NULL,
  identMeas = "ppm",
  limitIdent = 5,
  filtAmbiguous = FALSE,
  recalibrate = FALSE,
  massTy = "mono",
  prefFragPat = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Details

The main matching results are in output$massMatch : This list has one entry for each predicted mass where some matches were found. Thus, the names of the list-elements design the index from argument expMass. Each list-element contains a numeric vector giving the difference observed to predicted, the names design the unique predicted peptide index/number from output$preMa[,"no"]

The main element of the output is the $massMatch -list, which is in the format of findCloseMatch. Thus, the list-elements names represent the line-number of mass-predictions and the values the delta-mass and their names the position of the initial query.

Value

This function returns a list with $massMatch (list of exerimental peptides matching to one or more predicted), $preMa (predicted ions, including fixed modif), $pepTab (predicted neutral peptides, wo modifications), $expMa (experimental mass from input), $recalibFact (recalibration factor as from input), $docTi (time for calculations)

See Also

makeFragments, identifVarModif, identifyPepFragments, findCloseMatch

Examples

pro3 <- "HLVDEPQNLIK"
exp3 <- c( b4=465.2451, b5=594.2877, b6=691.3404,  y7=841.4772, y6=712.4347, y5=615.3819)
ident3 <- identifFixedModif(prot=pro3, expMass=exp3, minFragSize=4, 
  maxFragSize=60, modTy=list(basMod=c("b","y")))
ident3$massMatch                                                                                              
## as human readable table:
ident3$preMa[ ident3$preMa[,"no"] %in% (names(ident3$massMatch)),]

Idenitfy Variable Modifications

Description

Take result from identifFixedModif and search for variable modifications (only on identified fixed modif), ie 2nd step for identif of var modifs. To reduce the complexity of the search space, only peptide fragments identified with fixed identifiactions will be considered for possible variable modifications.

Usage

identifVarModif(
  zz,
  modTy,
  expMa,
  maxMod,
  identMeas = "ppm",
  knownMods = NULL,
  limitIdent = 5,
  filtAmbiguous = FALSE,
  indexStart = 1,
  recalibFact = NULL,
  suplPepTab = NULL,
  massTy = "mono",
  silent = FALSE,
  callFrom = NULL,
  debug = TRUE
)

Arguments

Details

The main matching results are in output$massMatch : This list has one entry for each predicted mass where some matches were found. Thus, the names of the list-elements design the index from argument expMass. Each list-element contains a numeric vector giving the difference observed to predicted, the names design the unique predicted peptide index/number from output$preMa[,"no"]

Value

list with $massMatch (list of exerimental peptides matching to one or more predicted), $preMa (predicted ions, including fixed and variable modif), $pepTab (predicted neutral peptides, wo modifications), $expMa (experimental mass from input), $recalibFact (recalibration factor as from input), $docTi (time for calculations)

See Also

makeFragments, identifFixedModif, identifyPepFragments

Examples

protP <- c(protP="PEPTIDE")
obsMassX <- cbind(a=c(199.1077,296.1605,397.2082,510.2922,625.3192),
  b=c(227.1026,324.1554,425.2031,538.2871,653.3141),
  x=c(729.2937,600.2511,503.1984,402.1507,289.0666),
  y=c(703.3145,574.2719,477.2191,376.1714,263.0874))
rownames(obsMassX) <- c("E","P","T","I","D")      # all 1 & 7 ions not included
identP10 <- identifFixedModif(prot=protP,expMass=as.numeric(obsMassX),minFragSize=2, 
  maxFragSize=7,modTy=list(basMod=c("b","y")))     # looks ok
identP10v <- identifVarModif(identP10,list(varMod="h"), as.numeric(obsMassX),2)
identP10v$massMatch                    # list of matches

Identify terminal and internal protein/peptide-fragments as matches to experimental MS-peaks

Description

Function for predicting internal and terminal peptide-fragments and compare them with experimental monoisotopic masses. The accuracy of results is given in ppm and a false discovery rate (FDR) for the identification is estimated. The identifed fragments are also checked for preferential break sites, a score including this and other parameters is given with the results.

Usage

identifyPepFragments(
  expMass,
  pep,
  modTy = NULL,
  minFragSize = 6,
  maxFragSize = 75,
  identMeas = "ppm",
  limitIdent = 5,
  internFra = TRUE,
  specModif = NULL,
  massTy = "mono",
  chargeCatchFilter = TRUE,
  corMutShift = NULL,
  nProc = 1,
  parallDefault = TRUE,
  multParam = NULL,
  maxMod = c(p = 3, h = 1, k = 1, o = 1, m = 1, n = 1, u = 1, r = 1, s = 1),
  recalibrate = TRUE,
  filtAmbiguous = FALSE,
  prefFragmPat = NULL,
  sortOutputByMass = FALSE,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

matrix of idenitfied ions

See Also

makeFragments, identifVarModif, identifFixedModif, findCloseMatch, scoreProteinFragments

Examples

protP <- c(protP="PEPTIDE")
obsMassX <- cbind(a=c(199.1077,296.1605,397.2082,510.2922,625.3192),
  b=c(227.1026,324.1554,425.2031,538.2871,653.3141),
  x=c(729.2937,600.2511,503.1984,402.1507,289.0666),
  y=c(703.3145,574.2719,477.2191,376.1714,263.0874))
rownames(obsMassX) <- c("E","P","T","I","D")      # all 1 & 7 ions not included
modTy1 <- list(basMod=c("b","y"), varMod=c("p","o","q"))
frag1 <- identifyPepFragments(ex=as.numeric(obsMassX), pe=protP, modTy=modTy1, 
  minFragSize=2, chargeCatchFilter=FALSE)
(frag1b <- if(length(unlist(frag1$identif)) >0) modifFragmTabOutput(frag1))

Make Terminal And Internal Fragments From Proteins

Description

Makes terminal and internal fragments based on protein-sequence and present as matrix including heading and/or tailing amino-acid or theoretical molecular mass of all fragments. As the number of theoretically possible fragments increases with the size of the peptide/protein treated it is recommended to adopt arguments like masFragSize to realistic values for the type of mass spectrometer used, since efficient filtering will reduce considerably the amount of memory (RAM) needed and will improve overal performance.

Usage

makeFragments(
  protTab,
  minFragSize = 6,
  maxFragSize = 300,
  internFra = TRUE,
  knownMods = NULL,
  redRedundSeq = FALSE,
  prefFragPat = NULL,
  remNonConfPrefFragm = TRUE,
  ambigLab = c(duplSequence = "duplSequence", isoMass = "isoMass"),
  massTy = "mono",
  specModif = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

matrix with fragment sequence, mass, start- and end-position, heading and tailing AA (or NA if terminal fragment)

See Also

makeFragments; evalIsoFragm, from package wrProteo convAASeq2mass, AAmass, massDeFormula

Examples

protP <- c(protP="PEPTIDE")
pepT1 <- makeFragments(protTab=protP, minFragSize=2, maxFragSize=9, internFra=TRUE)
tail(pepT1)

protP2 <- cbind(se="PEPTIDE", ma="1304.7088503626")
pepT2 <- makeFragments(protTab=protP2, minFragSize=2, maxFragSize=9, internFra=TRUE)
tail(pepT1)

Change fragment identification output format (for biologists)

Description

Change fragment identification output to format better adopted for biologists

Usage

modifFragmTabOutput(
  datafr,
  addData = NULL,
  fuseC = c("precAA", "seq", "tailAA"),
  sep = ".",
  modifCol = "mod",
  replMod = cbind(old = "by", new = "i"),
  finCols = c("fraNa", "origNa", "beg", "end", "seq", "ty", "mod", "modSpec", "obsMass",
    "mass", "ppmToPred", "ambig", "runNo", "FDR", "sco4", "sc.prefFrag",
    "sc.chargeCatch", "sc.complemFra", "sc.sameSite", "logInt"),
  supFinCols = NULL,
  sortTable = "end",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

data.frame of reorganized identification results

See Also

identifyPepFragments

Examples

protP <- c(protP="PEPTIDE")
obsMassX <- cbind(a=c(199.1077,296.1605,397.2082,510.2922,625.3192),
  b=c(227.1026,324.1554,425.2031,538.2871,653.3141),
  x=c(729.2937,600.2511,503.1984,402.1507,289.0666),
  y=c(703.3145,574.2719,477.2191,376.1714,263.0874))
rownames(obsMassX) <- c("E","P","T","I","D")      # all 1 & 7 ions not included
modTy1 <- list(basMod=c("b","y"), varMod=c("p","o","q"))
frag1 <- identifyPepFragments(ex=as.numeric(obsMassX), pe=protP, modTy=modTy1, 
  minFragSize=2, chargeCatchFilter=FALSE)
(frag1b <- if(length(unlist(frag1$identif)) >0) modifFragmTabOutput(frag1))

Plot Identified Fragments Relative To Their Location

Description

Take result from fragIonMass and display identified fragments by their location, an additional parameter (default logIntensity) is used for coloring This function illustrates the distribution of identfied peptidesa and thus common break-points.

Usage

plotFragmLoc(
  fraL,
  extrCol = NULL,
  useLog = FALSE,
  useCol = NULL,
  specLayout = NULL,
  useTi = NULL,
  subTit = NULL,
  footer = NULL,
  batchFig = FALSE,
  legCex = 0.5,
  legOffS = NULL,
  legBorder = NULL,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

This function returns a figure

See Also

identifFixedModif

Examples

protP <- c(protP="PEPTIDE")
obsMassX <- cbind(a=c(199.1077, 296.1605, 397.2082, 510.2922,625.3192),
  b=c(227.1026, 324.1554, 425.2031, 538.2871, 653.3141),
  x=c(729.2937, 600.2511, 503.1984, 402.1507, 289.0666),
  y=c(703.3145, 574.2719, 477.2191, 376.1714, 263.0874))
rownames(obsMassX) <- c("E","P","T","I","D")      # all 1 & 7 ions not included
identP1 <- identifFixedModif(prot=protP,expMass=as.numeric(obsMassX), minFragSize=2, 
  maxFragSize=7, modTy=list(basMod=c("b","y")))     #

Draw simplified (deconvoluted) spectrum of mgf type and highlight peaks with matches found to theoretical data

Description

Draw simplified (deconvoluted) spectrum of mgf type and highlight matches found

Usage

plotMgfLike(
  lst,
  basInp = NULL,
  backgrCol = NULL,
  replNames = c("by", "i"),
  lwd = 1,
  col = NULL,
  tit = NULL,
  xLim = NULL,
  yLab = NULL,
  linPlot = FALSE,
  listNa = c("identif", "overview", "obsMass"),
  useColN = c("obsMass", "logInt", "origNa", "mod", "modSpec", "fraNa", "orig",
    "nIdentif", "minMassDec", "maxMass"),
  cex = 1,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a mgf-like figure

See Also

makeFragments, identifVarModif, identifFixedModif, identifyPepFragments

Examples

set.seed(2025)

Plot the number of theoretical random fragments

Description

This simple function allows plotting the expected number of theoretical fragments from random fragmentation of peptides/proteins (in mass spectrometry). Here, only the pure fragmentation without any variable fragmentation is considered, all fragment-sizes are included (ie, no gating). For simplicity, possible (variable) modifications like loss of neutrals, etc, are not considered.

Usage

plotNTheor(
  x,
  tit = "Number of term and intern fragm",
  xlab = "Number of aa",
  ylab = "",
  col = 2:3,
  log = "",
  mark = NULL,
  cexMark = 0.75
)

Arguments

Value

figure only

See Also

AAfragSettings

Examples

marks <- data.frame(name=c("Ubiquitin\n76aa", "Glutamate dehydrogenase 1\n501aa"),
  length=c(76,501))
plotNTheor(x=20:750, log="", mark=marks)

plot preferential fragmenation pattern Plot preferential fragmenation pattern equivalent to Fig 1b of Haverland et al 2017 (J Am Soc Mass Spectrom)

Description

plot preferential fragmenation pattern

Plot preferential fragmenation pattern equivalent to Fig 1b of Haverland et al 2017 (J Am Soc Mass Spectrom)

Usage

plotPrefFragPat(
  prefPat,
  namesCex = 0.8,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a figure

See Also

scoreFragments

Examples

plotPrefFragPat(.prefFragPattern())

Make decoy mass by full randomization

Description

Make full random decoy mass vector (mimick pepTab)

Usage

randMassByMut(
  pepTab,
  randCha,
  useCol = "mass",
  negAvoid = TRUE,
  sepCol = FALSE,
  inDel = FALSE,
  nAlter = 1,
  setSeed = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix with additional column 'decoyMass', or if 'sepCol'=FALSE as additional lines

See Also

randMassByStochastic

Examples

pepTab1 <- cbind(no=11:12, seq=c("YVVDTS","YVVDTSK"), origNa="test.P000",
  ty=c("inter","Cter"),mass=c(681.308997360991,809.403960378691))
randMassByMut(pepTab1, corMutShift())
randMassByMut(pepTab1, corInDelShift(), inDel=TRUE)

Make Decoy Mass By Full Randomization

Description

Make full random decoy mass vector (mimick pepTab)

Usage

randMassByStochastic(
  xChar,
  nRepeat = 5,
  negAvoid = TRUE,
  setSeed = NULL,
  silent = FALSE,
  debug = TRUE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix with additional column 'decoyMass', or if 'sepCol'=FALSE as additional lines

See Also

Uniform, randMassByMut, convToNum

Examples

rand <- randMassByStochastic(c(n=10, minV=2, maxV=7))
summary(rand)

Scoring Of Charge Catching Potential For Peptides

Description

Make score based on cumulative search for AA with given potential to catch charge (H+, or optionally any charge). Note : at current cumulative scoring large peptides may get priviliged.

Usage

scoreChargeCatch(
  resTab,
  pepCol = "seq",
  scale01 = TRUE,
  chargeMode = "pos",
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a numeric vector with score for each peptide of resTab (even if scale01=TRUE minimum may be >0 if all peptides do contain charge-catching AAs)

See Also

fragmentSeq

Examples

resTa <- matrix(c(1:4, "PEPTID","PEPTIK","PEPTRK","AGV"), ncol=2,
  dimnames=list(NULL,c("predInd","seq"))) 
scoreChargeCatch(resTa)

Scoring For Single Protein : Individual Components

Description

Make scoring for single protein : individual components :sameSite,contiguous,prefFragSite,logPeakHeight + combined (sum of scales 0->1)

Usage

scoreFragments(
  resTab,
  fragmInp,
  suplTakeLog = TRUE,
  j = NULL,
  useResCol = c("orig", "seq", "precAA", "tailAA", "beg", "end", "ppmToPred", "obsInd",
    "predInd", "Abundance"),
  prefFragPat = NULL,
  contigTermFragWe = 0.5,
  figDraw = TRUE,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a list with matrix $scaled (combined and indiv rescaled scores) and $raw (matching lines of 'resTab'; 'index' refers to predictedIndex)

See Also

identifyPepFragments, scoreProteinFragments

Examples

tab2 <- matrix(c("20","2","13","11","3","10","4", "PT","PE","EP","DE","PEP","IDE","PEPT", 
  rep(c("PEPTIDE","protP"),each=7), c("inter","Nter","Cter")[c(1,2,1,3,2,3,2)], 
  c(3,1,2,6,1,5,1, 4,2,3,7,3,7,4), "E",NA,"P","I",NA,"T",NA, "I","P","T",NA,"T",NA,"I", 
  c(1,6,6,20,7,19,8), c(-0.094312,-0.14707,-0.14707,0.08641,0.0084762,-0.10965,0.057087), 
  rep(2,7)), nrow=7, dimnames=list(NULL,c("predInd","seq","orig","origNa","ty","beg","end",
  "precAA","tailAA","obsInd","ppmToPred","mass")))
tab2 <- cbind(tab2, seqNa=paste0(tab2[,"origNa"],".",tab2[,"beg"],"-",tab2[,"end"]),Abundance=1)
rownames(tab2) <- paste0(tab2[,"origNa"],".", tab2[,"beg"],"", tab2[,"end"])
obsMassX <- cbind(a=c(199.1077,296.1605,397.2082,510.2922,625.3192),
  b=c(227.1026,324.1554,425.2031,538.2871,653.3141),
  x=c(729.2937,600.2511,503.1984,402.1507,289.0666),
  y=c(703.3145,574.2719,477.2191,376.1714,263.0874))

(outF <- scoreFragments(tab2, fragmInp=cbind(as.numeric(obsMassX), Abundance=1)))

Identifcation and scoring of preferential cuting sites

Description

Search for preferential fragmentation sites from 'pepTab' among the 3 colums specified via 'useCol' (for full AA sequence, preceeding AA, tailing AA) and return sum of scores (from 3rd column of prefFragPat) for both ends. Note : proteins must be witten as single lettre code.

Usage

scorePrefFrag(
  pepTab,
  useCol = c("seq", "precAA", "tailAA"),
  prefFragPat = NULL,
  silent = FALSE,
  debug = FALSE,
  callFrom = NULL
)

Arguments

Value

This function returns a matrix with fragment sequence, mass, start- and end-position, heading and tailing AA (or NA if terminal fragment)

See Also

makeFragments

Examples

pepT <- cbind(precAA=c("A","D","D","A","D"),seq=c("AKA","PKA","AKA","PKD","PKD"),
  tailAA=c("A","A","D","P","P"))
scorePrefFrag(pepT)

Scoring Of Identifications (For Multi-Protein Queries)

Description

Make scoring for multiple protein queries: individual components :sameSite,contiguous,prefFragSite,logPeakHeight + combined (sum of scales 0->1)

Usage

scoreProteinFragments(
  resTab,
  fragmInp = NULL,
  j = 2,
  useCol = c("orig", "precAA", "tailAA", "beg", "end", "ppmToPred", "obsInd", "predInd"),
  prefFragPat = NULL,
  contigTermFragWe = 0.5,
  returnCombined = TRUE,
  figDraw = TRUE,
  silent = FALSE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

Value

This function returns a list with matrix $scaled (combined and indiv rescaled scores) and $raw (matching lines of 'resTab')

See Also

scoreFragments, identifyPepFragments

Examples

tab2 <- matrix(c("20","2","13","11","3","10","4", "PT","PE","EP","DE","PEP","IDE","PEPT", 
  rep(c("PEPTIDE","protP"),each=7), c("inter","Nter","Cter")[c(1,2,1,3,2,3,2)], 
  c(3,1,2,6,1,5,1, 4,2,3,7,3,7,4), "E",NA,"P","I",NA,"T",NA, "I","P","T",NA,"T",NA,"I", 
  c(1,6,6,20,7,19,8), c(-0.094312,-0.14707,-0.14707,0.08641,0.0084762,-0.10965,0.057087), 
  rep(2,7)), nrow=7, dimnames=list(NULL, c("predInd","seq","orig","origNa","ty","beg","end",
  "precAA","tailAA","obsInd","ppmToPred","mass")))
tab2 <- cbind(tab2, seqNa=paste0(tab2[,"origNa"],".",tab2[,"beg"],"-",tab2[,"end"]),Abundance=1)
rownames(tab2) <- paste0(tab2[,"origNa"],".", tab2[,"beg"],"", tab2[,"end"])
obsMassX <- cbind(a=c(199.1077,296.1605,397.2082,510.2922,625.3192),
  b=c(227.1026,324.1554,425.2031,538.2871,653.3141),
  x=c(729.2937,600.2511,503.1984,402.1507,289.0666),
  y=c(703.3145,574.2719,477.2191,376.1714,263.0874))

(outF <- scoreFragments(tab2, fragmInp=cbind(as.numeric(obsMassX), Abundance=1)))
(out <- scoreProteinFragments(tab2, fragmInp=cbind(as.numeric(obsMassX), Abundance=1)))