Package 'NestLink'

computes the correlation of predicted and measured retention time

Description

this helper function computes a linear model between redicted and measured retention time of the as imput set given identified peptides.

TODO(cp): consider moving this method to the protViz package.

Usage

.ssrc.mascot(x, scores = c(10, 20, 40, 50), ...)

Arguments

x	as,data.frame.mascot generated data.frame object.
scores	default is c(10, 20, 40, 50).
...	passed to the plot function.

Value

a plot and summary

Author(s)

Christian Panse <[email protected]>, 2017,2019

Examples

library(ExperimentHub)
eh <- ExperimentHub(); 
load(query(eh, c("NestLink", "F255744.RData"))[[1]])
.ssrc.mascot(F255744, scores = 15)

---

Compose a peptide with a defined AA sequence frequency

Description

composes, out of an as input given amino acid distribution, a randomly sampled amino acid sequence. compose_GPGx8cTerm, compose_GSx7cTerm, and compose_GPx10R belong to three groups composing different flycode (peptide) construction. The construction is given in the function name. For example, GPGx8cTerm, composes a flycode having as prefix GPG followed by eight (x8) amino acids followed by a cTerm sequence. The different construction will have different detectability properties as mass range and hydrophobicity values.

Usage

compose_GPGx8cTerm(pool = c(rep("A", 12), rep("S", 0), rep("T", 12),
  rep("N", 12), rep("Q", 12), rep("D", 8), rep("E", 0), rep("V", 12),
  rep("L", 0), rep("F", 0), rep("Y", 8), rep("W", 0), rep("G", 12),
  rep("P", 12)), cTerm = c("VFR", "VSR", "VFGIR", "VSGER"))

Arguments

pool	AA distributen.
cTerm	c-Terms

Value

a AA sequence

Author(s)

Christian Panse <[email protected]> 2015

Examples

set.seed(1)
compose_GPGx8cTerm()
(FlyCodes <- replicate(10, compose_GPGx8cTerm()))
plot(parentIonMass(FlyCodes) ~ssrc(FlyCodes))

---

Compose a peptide with a defined AA sequence

Description

composes, out of an as input given amino acid distribution, a randomly sampled amino acid sequence. compose_GPGx8cTerm, compose_GSx7cTerm, and compose_GPx10R belong to three groups composing different flycode (peptide) construction. The construction is given in the function name. For example, GPGx8cTerm, composes a flycode having as prefix GPG followed by eight (x8) amino acids followed by a cTerm sequence. The different construction will have different detectability properties as mass range and hydrophobicity values.

Usage

compose_GPx10R(aa_pool1, aa_pool2)

Arguments

aa_pool1	AA distributen.
aa_pool2	AA distributen.

Value

a AA sequence

Author(s)

Christian Panse <[email protected]> 2015

Examples

set.seed(1)
aa_pool_1_2_9_10 <- c(rep('A', 8), rep('S', 7), rep('T', 7), rep('N', 6),
rep('Q', 6), rep('D', 8), rep('E', 8), rep('V', 9), rep('L', 6), rep('F', 5),
rep('Y', 9), rep('W', 6), rep('G', 15), rep('P', 0))

aa_pool_3_8 <- c(rep('A', 5), rep('S', 4), rep('T', 5), rep('N', 2),
rep('Q', 2), rep('D', 8), rep('E', 8), rep('V', 7), rep('L', 5), rep('F', 4),
rep('Y', 6), rep('W', 4), rep('G', 12), rep('P', 28))

compose_GPx10R(aa_pool_1_2_9_10, aa_pool_3_8)
(FlyCodes <- replicate(10, compose_GPx10R(aa_pool_1_2_9_10, aa_pool_3_8)))
plot(parentIonMass(FlyCodes) ~ssrc(FlyCodes))

---

Compose a FlyCode GSx7cTerm Amino Acid Sequence

Description

composes, out of an as input given amino acid distribution, a randomly sampled amino acid sequence. compose_GPGx8cTerm, compose_GSx7cTerm, and compose_GPx10R belong to three groups composing different flycode (peptide) construction. The construction is given in the function name. For example, GPGx8cTerm, composes a flycode having as prefix GPG followed by eight (x8) amino acids followed by a cTerm sequence. The different construction will have different detectability properties as mass range and hydrophobicity values.

Usage

compose_GSx7cTerm(pool = c(rep("A", 18), rep("S", 6), rep("T", 12),
  rep("N", 1), rep("Q", 1), rep("D", 11), rep("E", 11), rep("V", 12),
  rep("L", 2), rep("F", 1), rep("Y", 4), rep("W", 1), rep("G", 8), rep("P",
  12)), cTerm = c("WR", "WLTVR", "WQEGGR", "WQSR", "WLR"))

Arguments

pool	a vector of amino acids.
cTerm	a vector of a sequence suffix.

Value

a amino acid sequence, e.g., GSAPTTVFGWLTVR.

Author(s)

Christian Panse <[email protected]> 2015

Examples

sample.size <- 100
 #
 ## Compose a GSXXXXXXX(WR|WLTVR|WQGGER|WQSR|WLR) peptide
 set.seed(2)
 FC.GSx7cTerm <- replicate(sample.size, compose_GSx7cTerm())
 ## Some Sanity Checks
 table(FC.GSx7cTerm)
 stopifnot(length(FC.GSx7cTerm) == 100)
 FC.PATTERN <- "^GS[ASTNQDEFVLYWGP]{7}(WR|WLTVR|WQEGGR|WLR|WQSR)$"
 stopifnot(
   length(FC.GSx7cTerm[grepl(FC.PATTERN, FC.GSx7cTerm)])
     == sample.size)

---

F255744 Mascot Search results

Description

F255744 Mascot Search results

Author(s)

Pascal Egloff [email protected]

See Also

F255744

Examples

library(ExperimentHub)
eh <- ExperimentHub(); load(query(eh, c("NestLink", "F255744.RData"))[[1]])
class(F255744)
hist(F255744$RTINSECONDS)
hist(F255744$RTINSECONDS[F255744$pep_score > 20])

---

getExperimentHubFilename

Description

getExperimentHubFilename

Usage

getExperimentHubFilename(filename)

Arguments

filename

of the aws s3 blob.

Value

the file name of the local ExperimentHub.

Examples

fl <- system.file("extdata", "metadata.csv", package="NestLink")   
metadata <- read.csv(fl, stringsAsFactors=FALSE)
     metadata$Title     
     
lapply(metadata$RDataPath, getExperimentHubFilename)

---

Read FlyCodes (FCs)

Description

A wrapper function for reading the flycodes using ExperimentHub. The files are used for demonstrating the detectability of the AA sequences. The wrapper functions are extended by columns ssrc prediction and the parentIonMass. The column ESP_Prediction was generated by using the service from https://genepattern.broadinstitute.org.

Usage

getFC(pattern = "^GS[ASTNQDEFVLYWGP]{7}(WR|WLTVR|WQEGGR|WLR|WQSR)$",
  filename = NULL)

Arguments

pattern	a regular expression FlyCode pattern
filename	a two column tab separated file containing a peptide sequence and an ESP value. default is NULL which reads the data provided by the package through ExperimentHub.

Value

a data.frame object of Flycodes

Author(s)

Christian Panse <[email protected]> 2015, 2018

Source

• https://fgcz-gstore.uzh.ch/projects/p1644/analysis_20170609_o3040/p1644o3482-4_S4.extendedFrags_uniqNB2FC.txt

• https://fgcz-gstore.uzh.ch/projects/p1644/analysis_20170609_o3040/p1644o3482-5_S5.extendedFrags_uniqNB2FC.txt

Examples

FC <- getFC()
dim(FC)

---

Read NanoBodies (NBs)

Description

A wrapper function for reading the flycodes using ExperimentHub. The files are used for demonstrating the detectability of the AA sequences. The wrapper functions are extended by columns ssrc prediction and the parentIonMass. The column ESP_Prediction was generated by using the service from https://genepattern.broadinstitute.org.

Usage

getNB(filename = NULL)

Arguments

filename

a two column tab separated file containing a peptide sequence and an ESP value. default is NULL which reads the data provided by the package through ExperimentHub.

Value

a data.frame object of NBs

Author(s)

Christian Panse <[email protected]> 2015, 2018, 2019

Source

• https://fgcz-gstore.uzh.ch/projects/p1644/analysis_20170609_o3040/p1644o3482-4_S4.extendedFrags_uniqNB2FC.txt

• https://fgcz-gstore.uzh.ch/projects/p1644/analysis_20170609_o3040/p1644o3482-5_S5.extendedFrags_uniqNB2FC.txt

Examples

NB <- getNB()
dim(NB)

---

Write FASTA

Description

Write FASTA

Usage

nanobodyFlycodeLinking.as.fasta(x, file = NULL, ...)

Arguments

x	a nanobodyFlycodeLinking S3 object computed by runNGSAnalysis.
file	a filename
...	just passed

Value

sprintf stream

Author(s)

Lennart Opitz, Christian Panse 2018

Examples

library(ExperimentHub)
eh <- ExperimentHub()
f <- query(eh, c("NestLink", "nanobodyFlycodeLinkage.RData"))[[1]]
load(f)
summary(nanobodyFlycodeLinkage.sample)
nanobodyFlycodeLinking.as.fasta(nanobodyFlycodeLinkage.sample)

---

Object Summaries of S3 class nanobodyFlycodeLinking

Description

Object Summaries of S3 class nanobodyFlycodeLinking

Usage

nanobodyFlycodeLinking.summary(object)

Arguments

object

a nanobodyFlycodeLinking class computed by runNGSAnalysis.

Value

a data.frame object

Examples

library(ExperimentHub)
eh <- ExperimentHub()
f <- query(eh, c("NestLink", "nanobodyFlycodeLinkage.RData"))[[1]]
load(f)
summary(nanobodyFlycodeLinkage.sample)

---

Determine unambiguous NBs

Description

Determine unambiguous NBs

Usage

NB.unambiguous(x = getNB())

Arguments

x	a data.frame containing a column peptide

Value

a data.frame a data.frame of unambiguously assignable peptides (those, which occur only on one nanobody)

Examples

NB <- getNB()
dim(NB.unambiguous(NB))

---

make NB table unique

Description

make NB table unique

Usage

NB.unique(x = getNB())

Arguments

x	a data.frame

Value

a data.frame

Examples

NB <- getNB()
dim(NB.unique(NB))

---

PGexport results

Description

PGexport results

Author(s)

Pascal Egloff [email protected]

Source

https://fgcz-bfabric.uzh.ch

• Workunit : 158716 - QEXACTIVEHF_1 20170919_16_62465_nl5idx1-3_6titratecoli.raw 20170919_05_62465_nl5idx1-3_6titratecoli.raw

• Workunit : 158717 - QEXACTIVEHF_1 20170919_14_62466_nl5idx1-3_7titratesmeg.raw 20170919_09_62466_nl5idx1-3_7titratesmeg.raw

Examples

# filename <- system.file(
#  "extdata/PGexport2_normalizedAgainstSBstandards_Peptides.csv",
#  package = "NestLink")
library(ExperimentHub)
eh <- ExperimentHub()
filename <- query(eh,
  c("NestLink", "PGexport2_normalizedAgainstSBstandards_Peptides.csv"))[[1]]
P <- read.csv(filename, header = TRUE, sep=';')
P <- P[P$Modifications == '', ]
P <- P[,c('Accession', 'Sequence',
"X20170919_05_62465_nl5idx1.3_6titratecoli",
"X20170919_16_62465_nl5idx1.3_6titratecoli",
"X20170919_09_62466_nl5idx1.3_7titratesmeg",
"X20170919_14_62466_nl5idx1.3_7titratesmeg")]
names(P)<-c('Accession','Sequence','coli1', 'coli2', 'smeg1', 'smeg2')
P<- P[grep("^P[0-9][A-Z][0-9]", P$Accession), ]

P$FCset_ng <- NA
P$FCset_ng[P$Accession %in% c('P1A4', 'P1B4', 'P1C4',
  'P1D4', 'P1E4', 'P1F4')] <- 92
P$FCset_ng[P$Accession %in% c('P1A5', 'P1B5', 'P1C5',
  'P1D5', 'P1G4', 'P1H4')] <- 295
P$FCset_ng[P$Accession %in% c('P1A6', 'P1B6', 'P1E5',
  'P1F5', 'P1G5', 'P1H5')] <- 943
P$FCset_ng[P$Accession %in% c('P1C6', 'P1D6', 'P1E6',
  'P1F6', 'P1G6', 'P1H6')] <- 3017

 P$coli1 <- (log(P$coli1,2) - mean(log(P$coli1,2))) / sd(log(P$coli1,2))
 P$coli2 <- (log(P$coli2,2) - mean(log(P$coli2,2))) / sd(log(P$coli2,2))
 P$smeg1 <- (log(P$smeg1,2) - mean(log(P$smeg1,2))) / sd(log(P$smeg1,2))
 P$smeg2 <- (log(P$smeg2,2) - mean(log(P$smeg2,2))) / sd(log(P$smeg2,2))

  O <- P
  b <- boxplot(df<-cbind(P$coli1 - P$coli2, P$coli1 - P$smeg1,
  P$coli1 - P$smeg2,P$coli2 - P$smeg1, P$coli2 - P$smeg2,
  P$smeg1 - P$smeg2),
   ylab='normalized log2ratios', ylim = c(-1,1), axes=FALSE,
    main=paste("ConcGr = all"))
axis(1, 1:6, c('coli[12]', 'coli1-smeg1', 'coli1-smeg2', 'coli2-smeg1',
'coli2- smeg2','smeg[12]'))
abline(h=0, col='red')
box()
axis(2)
axis(3, 1:6, b$n)
outliers.idx <- sapply(1:length(b$group), function(i){
  q <- df[, b$group[i]] == b$out[i];
  text(b$group[i], b$out[i], P[q, 2], pos=4, cex=0.4);
   text(b$group[i], b$out[i], P[q, 1], pos=2, cex=0.4);
   which(q)}
   )

---

plot a LC-MS map of a given set of amino acid sequences

Description

plot a LC-MS map of a given set of amino acid sequences

Usage

plot_in_silico_LCMS_map(peptides, ...)

Arguments

peptides	a vector of pepitdes.
...	pass through the plot method.

Details

TODO(cp): consider using hexbin using ggplot2 ggplot facet_wrap aes geom_point

Value

gplots::hist2d a gplot 2d histogram

Author(s)

Christian Panse

Examples

set.seed(1)
par(mfrow=c(2,1));
FlyCodes <- replicate(10000, compose_GPGx8cTerm())
rv <- plot_in_silico_LCMS_map(FlyCodes)

---

NGS linkage workflow

Description

performs the NGS filtering workflow to get high quality FlyCode and Nanobody sequences linkage.

Usage

runNGSAnalysis(file, param)

Arguments

file	sequence file path
param	list of input parameters, explained in details paragraph below.

Details

The elements of the parameter list object is described as follows:

• NB_Linker1 nucleotide sequence of the linker left to the nanobody.

• NB_Linker2 nucleotide sequence of the linker right to the nanobody.

• ProteaseSite nucleotide sequence left to the flycode.

• FC_Linker nucleotide sequence right to the flycode.

• knownNB known nanobody sequences in the experiment.

• nReads number of Reads from the start of fastq file to process.

• minRelBestHitFreq minimal fraction of the dominant nanobody for a specific flycode.

• minConsensusScore minimal fraction per sequence position in nanabody consensus sequence calculation.

• maxMismatch number of accepted mismatches for all pattern search steps.

• minNanobodyLength minimal nanobody length in [nt].

• minFlycodeLength minimal flycode length in [nt].

• FCminFreq minimal number of subreads for a specific flycode to keep it in the analysis.

missing elements are replace by the example provided values.

Value

uniqNB2FC dataframe

Author(s)

Lennart Opitz <[email protected]>, 2019

Examples

library(ExperimentHub)
eh <- ExperimentHub()
expFile <- query(eh, c("NestLink", "NL42_100K.fastq.gz"))[[1]]
knownNB_File <- query(eh, c("NestLink", "knownNB.txt"))[[1]]    
knownNB_data <- read.table(knownNB_File, sep='\t', header = TRUE,
    row.names = 1, stringsAsFactors = FALSE)
knownNB <- Biostrings::translate(DNAStringSet(knownNB_data$Sequence))
names(knownNB) <- rownames(knownNB_data)
knownNB <- sapply(knownNB, toString)
param <- list()
param[['NB_Linker1']] <- "GGCCggcggGGCC"
param[['NB_Linker2']] <- "GCAGGAGGA"
param[['ProteaseSite']] <- "TTAGTCCCAAGA"
param[['FC_Linker']] <- "GGCCaaggaggcCGG"
param[['knownNB']] <- knownNB
param[['nReads']] <- 10000
param[['minRelBestHitFreq']] <- 0.8
param[['minConsensusScore']] <- 0.9
param[['maxMismatch']] <- 1
param[['minNanobodyLength']] <- 348
param[['minFlycodeLength']] <- 33
param[['FCminFreq']] <- 1
runNGSAnalysis(file = expFile[1], param)

---

Filter input sequences for two patterns

Description

Filter input sequences for two patterns

Usage

twoPatternReadFilter(reads, leftPattern, rightPattern, maxMismatch,
  prevPatternPos = NULL)

Arguments

reads	input sequences
leftPattern	left pattern motive.
rightPattern	right pattern motive.
maxMismatch	maximal number of miss matches.
prevPatternPos	prev pattern posiotion; default is set to NULL.

Value

list object

Examples

reads <- DNAStringSet(c('ACTGGGTTT','ACCCTGGGTTT'))
leftPattern <- 'CT'
rightPattern <- 'TTT'
maxMismatch <- 0
twoPatternReadFilter(reads, leftPattern, rightPattern, maxMismatch)

---

WU160118 Mascot Search results

Description

WU160118 Mascot Search results

Author(s)

Christian Panse

References

https://fgcz-bfabric.uzh.ch/bfabric/userlab/show-workunit.html?id=160118

See Also

please read the vignette summaryFASTA.Rmd.

Examples

library(ExperimentHub)
eh <- ExperimentHub(); 
load(query(eh, c("NestLink", "WU160118.RData"))[[1]])
class(WU160118)
PATTERN <- "^GS[ASTNQDEFVLYWGP]{7}(WR|WLTVR|WQEGGR|WLR|WQSR)$"
idx <- grepl(PATTERN, WU160118$pep_seq)
WU <- WU160118[idx & WU160118$pep_score > 25,]

library(lattice)
histogram(~RTINSECONDS| datfilename, data = WU, type='count')

---