R Notebook

transitions <- read.csv("R:/Dhaenens_Covid/FilesVonMaarten/transitions.csv")

Analyze precursors

The following precursors are targeted:

table(transitions$Precursor.Mz)

 373.695221  378.181878  416.232167   416.25036  421.217342  421.235535  443.706317 
          2           2           2           3           2           3           2 
 449.190009  459.251185  471.784567  472.769251  477.268259  479.249979  513.260631 
          2           3           2           3           2           3           3 
 562.301671   563.78563  564.785827  571.236235  571.263392  572.263589  612.816595 
          2           2           2           3           2           2           2 
 613.326796  619.297323  619.309006  648.988927  655.854863  657.962242  662.335591 
          2           2           2           2           3           2           3 
 664.021233  671.332823  687.388139  696.029109  842.948869  852.420702  872.413282 
          2           2           2           2           1           1           4 
 882.383632 1013.021708 1023.490576 
          4           2           2 

transitions %>% 
  select(Peptide.Modified.Sequence, Precursor.Mz, Precursor.Charge) %>% 
  unique() %>% 
  arrange(Precursor.Mz)

Results
LNQLESK and STELLIR are quite close to each other. SSYVGDEASSK and AYNVTQAFGR are quite close to each other. The heavy precursors of GWIFGTTLDSK and SFIEDLLFNK are quite close to each other. Ion match tolerance is set to 0.5 m/z. Method match tolerance is set to 0.055 m/z.

Analyze transitions

Results
Some of the transitions are also close to each other,