Contents

1 Load requiered R packages

library(NestLink)

2 Display ESP

ESP_Prediction was generated using an application https://genepattern.broadinstitute.org (???).

library(ggplot2)
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ESP <- rbind(getFC(), getNB())
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p <- ggplot(ESP, aes(x = ESP_Prediction, fill = cond)) +
  geom_histogram(bins = 50, alpha = 0.4, position="identity") +
  labs(x = "detectability in LC-MS (ESP prediction)") +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
      panel.background = element_blank(), axis.line = element_line(colour = "black"))
print(p)

ESP <- rbind(getFC(), NB.unambiguous(getNB()))
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p <- ggplot(ESP, aes(x = ESP_Prediction, fill = cond)) +
  geom_histogram(bins = 50, alpha = 0.4, position="identity") +
  labs(x = "detectability in LC-MS (ESP prediction)") +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"))
print(p)

ESP <- rbind(getFC(), NB.unique(getNB()))
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p <- ggplot(ESP, aes(x = ESP_Prediction, fill = cond)) +
  geom_histogram(bins = 50, alpha = 0.4, position="identity") +
  labs(x = "detectability in LC-MS (ESP prediction)") +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"))
print(p)

ESP <- rbind(getNB(), NB.unambiguous(getNB()), NB.unique(getNB()))
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p <- ggplot(ESP, aes(x = ESP_Prediction, fill = cond)) +
  geom_histogram(bins = 50, alpha = 0.4, position="identity") +
  labs(x = "detectability in LC-MS (ESP prediction)") +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"))
print(p)

table(ESP$cond)
## 
##             NB NB.unambiguous      NB.unique 
##          27681           5832           7705

3 Charge State Frequency

3.1 Load workunit 160118

# library(ExperimentHub)                               
# eh <- ExperimentHub();                                
# load(query(eh, c("NestLink", "WU160118.RData"))[[1]])

load(getExperimentHubFilename("WU160118.RData"))
WU <- WU160118

filtering

PATTERN <- "^GS[ASTNQDEFVLYWGP]{7}(WR|WLTVR|WQEGGR|WLR|WQSR)$"

idx <- grepl(PATTERN, WU$pep_seq)
WU <- WU[idx & WU$pep_score > 25,]

determine charge state frequency through counting

WU$chargeInt <- as.integer(substr(WU$query_charge, 0, 1))
table(WU$chargeInt)
## 
##     2     3     4 
## 33392   157     1

in percent

round(100 * (table(WU$chargeInt) / nrow(WU)), 1)
## 
##    2    3    4 
## 99.5  0.5  0.0

as histograms

library(scales)
ggplot(WU, aes(x = moverz * chargeInt, fill = (query_charge),
               colour = (query_charge))) +
    facet_wrap(~ datfilename, ncol = 2) +
    geom_histogram(binwidth= 10, alpha=.3, position="identity") +
    labs(title = "Precursor mass to charge frequency plot",
      subtitle = "Plotting frequency of precursor masses for each charge state",
      x = "Precursor mass [neutral mass]", 
      y = "Frequency [counts]",
      fill = "Charge State",
      colour = "Charge State") +
    scale_x_continuous(breaks = pretty_breaks(8)) +
    theme_light()

We confirmed this prediction by experimental data and found that 99.9 percent of flycode precursor ions correspond to doubly charge species (data not shown). The omission of positively charged residues is also critical in order to render trypsin a site-specific protease.

3.2 FlyCode Mass Distribution

WU$suffix <- substr(WU$pep_seq, 10, 100)

ggplot(WU, aes(x = moverz * chargeInt, fill = suffix, colour = suffix)) +
    geom_histogram(binwidth= 10, alpha=.2, position="identity") +
    #facet_wrap(~ substr(pep_seq, 10, 100)) +
   theme_light()

ggplot(WU, aes(x = moverz * chargeInt, fill = suffix)) +
    geom_histogram(binwidth= 10, alpha=.9, position="identity") +
    facet_wrap(~ substr(pep_seq, 10, 100)) +
   theme_light()

3.3 Mascot Ion Score Distribution

ggplot(WU, aes(x = pep_score, fill = query_charge, colour = query_charge)) +
    geom_histogram(binwidth= 2, alpha=.5, position="identity") +
    facet_wrap(~ substr(pep_seq, 10, 100)) +
   theme_light()